{"count":60,"items":[{"abstract":"Business processes in customer support often remain undocumented, with critical decisions such as ticket escalation relying on tacit knowledge rather than explicit process models. Automating such process decisions is challenging because the underlying data is unstructured email text, not structured event logs assumed by existing process mining and predictive monitoring approaches. We present a novel pipeline artifact that addresses this gap: a semi-automated four-stage pipeline that extracts structured features from customer support emails using large language models (LLMs) together with lightweight domain-expert validation, and employs explainable machine learning to predict the optimal timing of ticket creation, a key decision point in the support process. Our pipeline generates an initial feature schema from sample threads, consolidates semantically similar features through embedding-based clustering under domain-expert review, extracts cumulative feature values at each email point, and constructs a structured dataset ready for machine learning. We introduce a SHAP-based active feature learning strategy that identifies which missing features, when acquired, would most improve prediction confidence, enabling principled decisions about when to request additional information versus making immediate predictions. We evaluate our approach in two stages: first, on a reproducible synthetic dataset where comparison against six baselines demonstrates that LLM-extracted features provide complementary semantic information, with combined features achieving the best overall performance (ROC-AUC 0.90) while end-to-end LLM classification performs worst (ROC-AUC 0.46), validating our design of using LLMs as feature extractors rather than decision makers; second, on 1,500 email instances from 800 real-world email threads from Nosco, a commercial CRM platform, where we demonstrate effective feature extraction, predictive performance, and active feature learning for practical deployment.","agent_summary":"","authors":["Furkan Kocak","Gyunam Park"],"bibtex":"@inproceedings{10.1007/978-3-032-37777-7_27,\n  author    = {Kocak, Furkan and Gyunam Park},\n  editor    = {Maggi, Fabrizio Maria and Leopold, Henrik and Santoro, Flavia and Gal, Avigdor},\n  title     = {Toward Automated Ticket Escalation in Customer Support Processes: From Emails to Explainable Predictions},\n  booktitle = {Business Process Management},\n  year      = {2027},\n  publisher = {Springer Nature Switzerland},\n  address   = {Cham},\n  pages     = {444--463},\n  abstract  = {Business processes in customer support often remain undocumented, with critical decisions such as ticket escalation relying on tacit knowledge rather than explicit process models. Automating such process decisions is challenging because the underlying data is unstructured email text, not structured event logs assumed by existing process mining and predictive monitoring approaches. We present a novel pipeline artifact that addresses this gap: a semi-automated four-stage pipeline that extracts structured features from customer support emails using large language models (LLMs) together with lightweight domain-expert validation, and employs explainable machine learning to predict the optimal timing of ticket creation, a key decision point in the support process. Our pipeline generates an initial feature schema from sample threads, consolidates semantically similar features through embedding-based clustering under domain-expert review, extracts cumulative feature values at each email point, and constructs a structured dataset ready for machine learning. We introduce a SHAP-based active feature learning strategy that identifies which missing features, when acquired, would most improve prediction confidence, enabling principled decisions about when to request additional information versus making immediate predictions. We evaluate our approach in two stages: first, on a reproducible synthetic dataset where comparison against six baselines demonstrates that LLM-extracted features provide complementary semantic information, with combined features achieving the best overall performance (ROC-AUC 0.90) while end-to-end LLM classification performs worst (ROC-AUC 0.46), validating our design of using LLMs as feature extractors rather than decision makers; second, on 1,500 email instances from 800 real-world email threads from Nosco, a commercial CRM platform, where we demonstrate effective feature extraction, predictive performance, and active feature learning for practical deployment.},\n  isbn      = {978-3-032-37777-7}\n}","code":"","contributions":["A semi-automated four-stage pipeline (feature identification, semantic clustering and consolidation, feature extraction, dataset construction) that converts email threads into cumulative per-email feature vectors using large language models (LLMs).","An embedding-based consolidation step (mxbai-embed-large-v1 embeddings, HDBSCAN density-based clustering) in which domain experts pick representative features, merge correlated ones and bound categorical value sets.","An explainable gradient-boosted classifier with TreeSHAP (SHapley Additive exPlanations) attributions that predicts ticket-creation timing at each email point, including attributions for missing features.","A SHAP-based active feature learning algorithm that withholds a prediction when missing features carry high impact or make the prediction unstable, and names the feature to request.","An evaluation on a public synthetic dataset of 350 threads and on 1,500 email instances from 800 threads of the Nosco customer relationship management platform, with code released on Zenodo."],"date":"2026-09-27","doi":"10.1007/978-3-032-37777-7_27","featured":false,"id":"2026-BPM-TicketEscalation","markdown_url":"https://www.gyunam.com/publication/2026-bpm-ticketescalation/index.md","number":"","pages":"444–463","pdf":"https://www.gyunam.com/publication/2026-bpm-ticketescalation/paper.pdf","projects":[],"publisher":"Springer","research_lines":["prescriptive-process-analytics"],"series":"","summary":"","tags":[],"title":"Toward Automated Ticket Escalation in Customer Support Processes: From Emails to Explainable Predictions","type":"paper-conference","url":"https://www.gyunam.com/publication/2026-bpm-ticketescalation/","venue":"Business Process Management","venue_short":"","volume":"","year":2026},{"abstract":"Given a running process case, prescriptive process analytics (PrPA) recommends the next activity that best improves a desired outcome, such as reducing cycle time. Recommendations must also be operationally feasible: an activity that improves the predicted outcome but violates process constraints cannot be enacted. The core challenge is therefore not compliance alone, which inference-time filtering can enforce, but achieving high outcome quality within the compliant space. Existing approaches fall short: post-hoc filtering never learns which compliant activities are outcome-improving, while logic-as-loss penalties balance unstable competing gradients from data fit and compliance. Both also treat all constraints equally, so combining mandatory hard constraints with relaxable soft constraints can leave many cases with no feasible recommendation. We propose NeSyPA, a neurosymbolic PrPA method with two contributions: Hierarchical Constraint Relaxation, which drops soft constraints only when the full constraint set is unsatisfiable, and Constraint-Compiled Outcome Distillation, which compiles per-prefix feasible sets into feasibility-masked, outcome-weighted soft targets that teach the neural prescriptor to rank feasible activities by outcome merit. On four real-world event logs, NeSyPA achieves the highest Effective Compliance Rate on all test sets, with gains of +6.7% to +9.9%, driven by higher outcome quality within the compliant space.","agent_summary":"","authors":["Gyunam Park"],"bibtex":"@inproceedings{10.1007/978-3-032-37777-7_30,\n  author    = {Gyunam Park},\n  editor    = {Maggi, Fabrizio Maria and Leopold, Henrik and Santoro, Flavia and Gal, Avigdor},\n  title     = {Neuro-Symbolic Prescriptive Process Analytics: Learning Compliant and Effective Process Recommendations Through Constraint Distillation},\n  booktitle = {Business Process Management},\n  year      = {2027},\n  publisher = {Springer Nature Switzerland},\n  address   = {Cham},\n  pages     = {503--520},\n  abstract  = {Given a running process case, prescriptive process analytics (PrPA) recommends the next activity that best improves a desired outcome, such as reducing cycle time. Recommendations must also be operationally feasible: an activity that improves the predicted outcome but violates process constraints cannot be enacted. The core challenge is therefore not compliance alone, which inference-time filtering can enforce, but achieving high outcome quality within the compliant space. Existing approaches fall short: post-hoc filtering never learns which compliant activities are outcome-improving, while logic-as-loss penalties balance unstable competing gradients from data fit and compliance. Both also treat all constraints equally, so combining mandatory hard constraints with relaxable soft constraints can leave many cases with no feasible recommendation. We propose NeSyPA, a neurosymbolic PrPA method with two contributions: Hierarchical Constraint Relaxation, which drops soft constraints only when the full constraint set is unsatisfiable, and Constraint-Compiled Outcome Distillation, which compiles per-prefix feasible sets into feasibility-masked, outcome-weighted soft targets that teach the neural prescriptor to rank feasible activities by outcome merit. On four real-world event logs, NeSyPA achieves the highest Effective Compliance Rate on all test sets, with gains of +6.7{\\%} to +9.9{\\%}, driven by higher outcome quality within the compliant space.},\n  isbn      = {978-3-032-37777-7}\n}","code":"","contributions":["NeSyPA, a neurosymbolic prescriptive process analytics method combining a Transformer prescriptor with Declare constraints compiled into deterministic finite automata.","Hierarchical Constraint Relaxation (HCR), which partitions the knowledge base into hard and soft constraints and drops the soft tier only when the full set admits no activity.","Constraint-Compiled Outcome Distillation (CCOD), which turns per-prefix feasible sets into feasibility-masked, outcome-weighted soft targets and fine-tunes the activity head in one pass.","Effective Compliance Rate (ECR), a metric counting recommendations that are both outcome-improving and constraint-compliant, alongside outcome-improvement and constraint-satisfaction rates.","An evaluation protocol with four baselines (unconstrained, post-hoc filtering, reinforcement learning, logic-as-loss) on four real-life test sets, with code and data on GitHub (gyunamister/nesy_prpa)."],"date":"2026-09-27","doi":"10.1007/978-3-032-37777-7_30","featured":false,"id":"2026-BPM-NeSyPrescriptive","markdown_url":"https://www.gyunam.com/publication/2026-bpm-nesyprescriptive/index.md","number":"","pages":"503–520","pdf":"https://www.gyunam.com/publication/2026-bpm-nesyprescriptive/paper.pdf","projects":[],"publisher":"Springer","research_lines":["neuro-symbolic-process-learning","prescriptive-process-analytics"],"series":"","summary":"","tags":[],"title":"Neuro-Symbolic Prescriptive Process Analytics: Learning Compliant and Effective Process Recommendations Through Constraint Distillation","type":"paper-conference","url":"https://www.gyunam.com/publication/2026-bpm-nesyprescriptive/","venue":"Business Process Management","venue_short":"","volume":"","year":2026},{"abstract":"Process analytics has become predominantly data-driven: deep sequence models such as LSTMs and Transformers now dominate predictive and prescriptive process monitoring and deliver strong average-case accuracy. These models are, however, opaque, require large amounts of data, and do not incorporate the process knowledge that organizations already hold in the form of regulations, declarative constraints, and reference process models. The symbolic side of Business Process Management (BPM) carries exactly this knowledge but is brittle on noisy, real-world event data. Neuro-symbolic AI (NeSy) aims to bridge these two traditions, and a growing body of BPM work has begun to use it. This tutorial gives the BPM community an accessible overview of the relationship between BPM and NeSy. It introduces Kautz's architectural taxonomy of NeSy and the Learning from Logical Constraints paradigm together with its main implementation, Logic Tensor Networks. It then traces how NeSy attaches to concrete BPM analytics tasks through a sequence of case studies spanning compliance-aware prediction, robust rule injection, healthcare outcome prediction, and prescriptive analytics. The tutorial highlights the failure modes that practitioners must avoid when injecting domain knowledge into deep process models, examines how to evaluate whether injected knowledge actually helps, and closes with open problems and a practical decision aid for when NeSy is worth adopting.","agent_summary":"","authors":["Gyunam Park","Fabrizio De Santis"],"bibtex":"@inproceedings{10.1007/978-3-032-37777-7_4,\n  author    = {Gyunam Park and De Santis, Fabrizio},\n  editor    = {Maggi, Fabrizio Maria and Leopold, Henrik and Santoro, Flavia and Gal, Avigdor},\n  title     = {Neuro-symbolic AI for BPM},\n  booktitle = {Business Process Management},\n  year      = {2027},\n  publisher = {Springer Nature Switzerland},\n  address   = {Cham},\n  pages     = {31--36},\n  abstract  = {Process analytics has become predominantly data-driven: deep sequence models such as LSTMs and Transformers now dominate predictive and prescriptive process monitoring and deliver strong average-case accuracy. These models are, however, opaque, require large amounts of data, and do not incorporate the process knowledge that organizations already hold in the form of regulations, declarative constraints, and reference process models. The symbolic side of Business Process Management (BPM) carries exactly this knowledge but is brittle on noisy, real-world event data. Neuro-symbolic AI (NeSy) aims to bridge these two traditions, and a growing body of BPM work has begun to use it. This tutorial gives the BPM community an accessible overview of the relationship between BPM and NeSy. It introduces Kautz's architectural taxonomy of NeSy and the Learning from Logical Constraints paradigm together with its main implementation, Logic Tensor Networks. It then traces how NeSy attaches to concrete BPM analytics tasks through a sequence of case studies spanning compliance-aware prediction, robust rule injection, healthcare outcome prediction, and prescriptive analytics. The tutorial highlights the failure modes that practitioners must avoid when injecting domain knowledge into deep process models, examines how to evaluate whether injected knowledge actually helps, and closes with open problems and a practical decision aid for when NeSy is worth adopting.},\n  isbn      = {978-3-032-37777-7}\n}","code":"","contributions":["A tutorial that organizes neuro-symbolic work in business process management (BPM) using Kautz's six-type architectural taxonomy and the Learning from Logical Constraints paradigm.","An introduction to Logic Tensor Networks and their Real Logic formalism sufficient to read and write first-order axioms that compile into training signals for deep models.","A sequence of case studies spanning compliance-aware prediction, two-stage training with rule pruning, intensive-care mortality prediction, prescriptive analytics (NeSyPA) and neuro-symbolic anomaly detection.","An analysis of failure modes and open problems: vacuous satisfaction, the soft-compliance gap, sequential constraints under fuzzy logic, and the conditions under which knowledge utility is observable.","A five-question decision aid (data scarcity, rule availability, regulatory exposure, tolerable opacity, deployment horizon) for judging when neuro-symbolic methods pay off."],"date":"2026-09-27","doi":"10.1007/978-3-032-37777-7_4","featured":false,"id":"2026-BPM-NeSyBPM","markdown_url":"https://www.gyunam.com/publication/2026-bpm-nesybpm/index.md","number":"","pages":"31–36","pdf":"https://www.gyunam.com/publication/2026-bpm-nesybpm/paper.pdf","projects":[],"publisher":"Springer","research_lines":["neuro-symbolic-process-learning"],"series":"","summary":"","tags":[],"title":"Neuro-symbolic AI for BPM","type":"paper-conference","url":"https://www.gyunam.com/publication/2026-bpm-nesybpm/","venue":"Business Process Management","venue_short":"","volume":"","year":2026},{"abstract":"Prescriptive Process Analytics is a core field in business process management that provides automated activity recommendations to improve Key Performance Indicators in business processes. Because such recommendations can directly affect individuals involved in the process, ensuring fairness is essential. Recent work has begun to address fairness in process monitoring by enforcing group-level parity across demographic groups at the predictive stage. However, these approaches leave the prescriptive phase entirely unexamined and rely solely on group fairness notions, failing to account for individual-level counterfactual fairness (i.e., the requirement that a recommendation should not change when sensitive attributes are altered). To address this gap, we propose to enforce counterfactual fairness directly in prescriptive process monitoring. We develop a novel neuro-symbolic framework based on Logic Tensor Networks that encodes counterfactual fairness as a logical axiom over predictions, seamlessly integrated into the learning process. We evaluate our approach on real-world event logs and find that it enforces counterfactual fairness at the prediction level and improves recommendation-level counterfactual consistency under the considered policy, while maintaining competitive predictive and prescriptive performance, establishing the first fairness-aware framework for Prescriptive Process Analytics.","agent_summary":"","authors":["Fabrizio De Santis","Alessandro Padella","Gyunam Park","Francesco Zanichelli","Massimiliano de Leoni"],"bibtex":"@inproceedings{10.1007/978-3-032-37777-7_23,\n  author    = {De Santis, Fabrizio and Padella, Alessandro and Gyunam Park and Zanichelli, Francesco and de Leoni, Massimiliano},\n  editor    = {Maggi, Fabrizio Maria and Leopold, Henrik and Santoro, Flavia and Gal, Avigdor},\n  title     = {Fairness in Prescriptive Process Analytics: Integrating Fairness Constraints into the Learning Phase},\n  booktitle = {Business Process Management},\n  year      = {2027},\n  publisher = {Springer Nature Switzerland},\n  address   = {Cham},\n  pages     = {369--387},\n  abstract  = {Prescriptive Process Analytics is a core field in business process management that provides automated activity recommendations to improve Key Performance Indicators in business processes. Because such recommendations can directly affect individuals involved in the process, ensuring fairness is essential. Recent work has begun to address fairness in process monitoring by enforcing group-level parity across demographic groups at the predictive stage. However, these approaches leave the prescriptive phase entirely unexamined and rely solely on group fairness notions, failing to account for individual-level counterfactual fairness (i.e., the requirement that a recommendation should not change when sensitive attributes are altered). To address this gap, we propose to enforce counterfactual fairness directly in prescriptive process monitoring. We develop a novel neuro-symbolic framework based on Logic Tensor Networks that encodes counterfactual fairness as a logical axiom over predictions, seamlessly integrated into the learning process. We evaluate our approach on real-world event logs and find that it enforces counterfactual fairness at the prediction level and improves recommendation-level counterfactual consistency under the considered policy, while maintaining competitive predictive and prescriptive performance, establishing the first fairness-aware framework for Prescriptive Process Analytics.},\n  isbn      = {978-3-032-37777-7}\n}","code":"","contributions":["A counterfactual event-log generation procedure that uses Fast Causal Inference (FCI) to discover causal dependencies among case-level attributes and fits structural equations to intervene on a sensitive attribute consistently.","A neuro-symbolic prescriptive framework (Fair-LTN) built on Logic Tensor Networks that jointly optimizes a regression axiom for key performance indicator (KPI) accuracy and a counterfactual fairness axiom.","Two evaluation metrics for fairness in process analytics: Counterfactual Mean Squared Error (CF-MSE) at prediction level and Counterfactual Recommendation Score (CF-RECC) at recommendation level.","A simulation-based evaluation protocol using the `ProSiT` simulator on original and counterfactual test logs, with code released on GitHub (`FabrizioDeSantis/Fair-PrescriptiveProcessMonitoring`).","An empirical study on BPI Challenge 2012, three Hospital Billing scenarios, and BPI Challenge 2013 incidents with gender, age, and organization country as sensitive attributes."],"date":"2026-09-27","doi":"10.1007/978-3-032-37777-7_23","featured":false,"id":"2026-BPM-Fairness","markdown_url":"https://www.gyunam.com/publication/2026-bpm-fairness/index.md","number":"","pages":"369–387","pdf":"https://www.gyunam.com/publication/2026-bpm-fairness/paper.pdf","projects":[],"publisher":"Springer","research_lines":["prescriptive-process-analytics"],"series":"","summary":"","tags":[],"title":"Fairness in Prescriptive Process Analytics: Integrating Fairness Constraints into the Learning Phase","type":"paper-conference","url":"https://www.gyunam.com/publication/2026-bpm-fairness/","venue":"Business Process Management","venue_short":"","volume":"","year":2026},{"abstract":"Digital transformation often entails small-scale updates to the business functions exposed by enterprise systems, which support the execution of organizational business processes. These changes may increase operational friction in process execution, decreasing the process performance, increasing the complexity of maintenance activities, and making system documentation and training obsolete. Detecting and understanding their impact on process execution is therefore crucial to maintain smooth operations. Previous research contributions concerning the tracking and impact analysis of information systems updates are limited in scope, functionality, and tool support. In this paper, we propose a data-driven approach that uses the recently developed Digital Twins of Organizations (DTOs) to assess the impact of (process-aware) information systems updates. We model the updates using the configuration of DTOs and the newly introduced concept of actions in DTOs. The impact of the application of actions in a given configuration is quantitatively assessed by different types of impacts: structural, operational, and performance-related. We show the feasibility of the proposed approach by discussing a software prototype implementing it, a case study involving artificially-generated data from a standard ERP procure-to-pay process, and a case study using real-world data from a loan application process.","agent_summary":"","authors":["Gyunam Park","Marco Comuzzi","Wil M. P. van der Aalst"],"bibtex":"@article{PARK2027102794,\n  title     = {Analyzing structural, operational, and performance impact of process-aware information system updates},\n  journal   = {Information Systems},\n  volume    = {143},\n  pages     = {102794},\n  year      = {2027},\n  issn      = {0306-4379},\n  doi       = {https://doi.org/10.1016/j.is.2026.102794},\n  url       = {https://www.sciencedirect.com/science/article/pii/S0306437926001080},\n  author    = {Gyunam Park and Marco Comuzzi and Wil M.P. {van der Aalst}},\n  abstract  = {Digital transformation often entails small-scale updates to the business functions exposed by enterprise systems, which support the execution of organizational business processes. These changes may increase operational friction in process execution, decreasing the process performance, increasing the complexity of maintenance activities, and making system documentation and training obsolete. Detecting and understanding their impact on process execution is therefore crucial to maintain smooth operations. Previous research contributions concerning the tracking and impact analysis of information systems updates are limited in scope, functionality, and tool support. In this paper, we propose a data-driven approach that uses the recently developed Digital Twins of Organizations (DTOs) to assess the impact of (process-aware) information systems updates. We model the updates using the configuration of DTOs and the newly introduced concept of actions in DTOs. The impact of the application of actions in a given configuration is quantitatively assessed by different types of impacts: structural, operational, and performance-related. We show the feasibility of the proposed approach by discussing a software prototype implementing it, a case study involving artificially-generated data from a standard ERP procure-to-pay process, and a case study using real-world data from a loan application process.}\n}","code":"","contributions":["A meta-model of process-aware information systems that relates business processes, functions, objects, constraint and operation rules, updates, and three impact types (structural, operational, performance).","Formalizes the Digital Twin Interface Model (DT-IM): an object-centric Petri net with linear rule expressions, configurations, operational states, interface bindings, and actions that model system updates as configuration changes.","A four-step impact analysis method: change detection, prior and posterior structural impact analysis, replay-based prior and posterior operational impact analysis, and function-wise and object-wise performance impact analysis.","A web-based prototype, `ProAct: Impact Analysis`, with open source code and a user manual, implementing the four analyses with interactive visualizations.","Two scenario-based case studies: a simulated procure-to-pay system mirroring SAP ERP rules, and a real-life loan application process (BPI Challenge 2017)."],"date":"2026-08-20","doi":"10.1016/j.is.2026.102794","featured":false,"id":"2026-IS-ProcessAwareImpact","markdown_url":"https://www.gyunam.com/publication/2026-is-processawareimpact/index.md","number":"","pages":"102794","pdf":"https://www.gyunam.com/publication/2026-is-processawareimpact/paper.pdf","projects":[],"publisher":"Elsevier","research_lines":["prescriptive-process-analytics"],"series":"","summary":"","tags":[],"title":"Analyzing structural, operational, and performance impact of process-aware information system updates","type":"article-journal","url":"https://www.gyunam.com/publication/2026-is-processawareimpact/","venue":"Information Systems","venue_short":"","volume":"143","year":2026},{"abstract":"Process discovery enables organizations to analyze and improve their operations by automatically deriving process models from event logs. While the Inductive Mining framework is widely adopted for ensuring model soundness, its strict block-structured nature often fails to capture the true complexity of real-world control flows. Recent advancements, such as the Partially Ordered Workflow Language (POWL), have relaxed these constraints for concurrency, yet a significant gap remains in effectively modeling complex decision logic and unstructured loops. We bridge this gap by introducing POWL 2.0 , an extended modeling language that integrates choice graphs to represent non-block-structured decisions and cyclic flows within a hierarchical framework. In this paper, we present a robust inductive discovery algorithm that leverages POWL 2.0, and we explore several mining strategies for choice graphs. These strategies range in strictness to resolve the ambiguity between genuine loops and hidden concurrency. Our experimental evaluation demonstrates that the proposed approach captures complex behaviors without compromising the scalability and quality guarantees of the Inductive Mining framework.","agent_summary":"","authors":["Humam Kourani","Gyunam Park","Wil M. P. van der Aalst"],"bibtex":"@article{DBLP:journals/procsci/KouraniPA26,\n  author    = {Humam Kourani and Gyunam Park and Wil M. P. van der Aalst},\n  title     = {A discovery technique for expressive yet sound process models},\n  journal   = {Process Sci.},\n  volume    = {3},\n  number    = {1},\n  pages     = {14},\n  year      = {2026},\n  url       = {https://doi.org/10.1007/s44311-026-00046-8},\n  doi       = {10.1007/S44311-026-00046-8}\n}","code":"","contributions":[],"date":"2026-06-10","doi":"10.1007/s44311-026-00046-8","featured":false,"id":"2026-ProcSci-SoundDiscovery","markdown_url":"https://www.gyunam.com/publication/2026-procsci-sounddiscovery/index.md","number":"1","pages":"14","pdf":"","projects":[],"publisher":"Springer","research_lines":["process-discovery-and-diagnostics"],"series":"","summary":"","tags":[],"title":"A discovery technique for expressive yet sound process models","type":"article-journal","url":"https://www.gyunam.com/publication/2026-procsci-sounddiscovery/","venue":"Process Science","venue_short":"","volume":"3","year":2026},{"abstract":"Process anomaly detection is an important application of process mining for identifying deviations from the normal behavior of a process. Neural network-based methods have recently been applied to this task, learning directly from event logs without requiring a predefined process model. However, since anomaly detection is a purely statistical task, these models fail to incorporate human domain knowledge. As a result, rare but conformant traces are often misclassified as anomalies due to their low frequency, which limits the effectiveness of the detection process. Recent developments in the field of neuro-symbolic AI have introduced Logic Tensor Networks (LTN) as a means to integrate symbolic knowledge into neural networks using real-valued logic. In this work, we propose a neuro-symbolic approach that integrates domain knowledge into neural anomaly detection using LTN and Declare constraints. Using autoencoder models as a foundation, we encode Declare constraints as soft logical guiderails within the learning process to distinguish between anomalous and rare but conformant behavior. Evaluations on synthetic and real-world datasets demonstrate that our approach improves F1 scores even when as few as 10 conformant traces exist, and that the choice of Declare constraint and by extension human domain knowledge significantly influences performance gains.","agent_summary":"","authors":["Devashish Gaikwad","Wil M. P. van der Aalst","Gyunam Park"],"bibtex":"@inproceedings{DBLP:conf/caise/GaikwadAP26,\n  author    = {Devashish Gaikwad and Wil M. P. van der Aalst and Gyunam Park},\n  editor    = {Lidia Fuentes and Pierluigi Plebani and Carlo Combi and Hajo A. Reijers},\n  title     = {Neuro-Symbolic Process Anomaly Detection},\n  booktitle = {Advanced Information Systems Engineering - 38th International Conference, CAiSE 2026, Verona, Italy, June 8-12, 2026, Proceedings, Part {II}},\n  series    = {Lecture Notes in Computer Science},\n  volume    = {16559},\n  pages     = {240--255},\n  publisher = {Springer},\n  year      = {2026},\n  url       = {https://doi.org/10.1007/978-3-032-28117-3\\_14},\n  doi       = {10.1007/978-3-032-28117-3\\_14}\n}","code":"","contributions":["A neuro-symbolic anomaly detection approach that fine-tunes a pretrained denoising autoencoder with Logic Tensor Networks (LTN), a framework that grounds first-order logic in real-valued fuzzy logic.","A knowledge base construction step that mines Declare constraints, keeps low-support high-confidence constraints, selects them with domain knowledge and translates them into first-order logic over positional activity predicates.","A training objective that combines reconstruction loss with the satisfiability of the constraint on constraint-satisfying traces and its violation on violating traces, reusing established threshold heuristics for detection.","An evaluation protocol on eight synthetic logs and BPIC12, BPIC13 and BPIC17 with 30 percent injected anomalies, varying the number of rare conformant traces from 10 to 300, plus an ablation over seven Declare templates.","An open-source implementation, `LTNcoder`, on GitHub."],"date":"2026-06-09","doi":"10.1007/978-3-032-28117-3_14","featured":false,"id":"2026-CAiSE-NeSyAnomaly","markdown_url":"https://www.gyunam.com/publication/2026-caise-nesyanomaly/index.md","number":"","pages":"240–255","pdf":"https://www.gyunam.com/publication/2026-caise-nesyanomaly/paper.pdf","projects":[],"publisher":"Springer","research_lines":["neuro-symbolic-process-learning"],"series":"LNCS","summary":"","tags":[],"title":"Neuro-Symbolic Process Anomaly Detection","type":"paper-conference","url":"https://www.gyunam.com/publication/2026-caise-nesyanomaly/","venue":"Advanced Information Systems Engineering - 38th International Conference, CAiSE 2026, Verona, Italy, June 8-12, 2026, Proceedings, Part II","venue_short":"","volume":"16559","year":2026},{"abstract":"Existing approaches for predictive process monitoring are sub-symbolic, meaning that they learn correlations between descriptive features and a target feature fully based on data, e.g., predicting the surgical needs of a patient based on historical events and biometrics. However, such approaches fail to incorporate domain-specific process constraints (knowledge), e.g., surgery can only be planned if the patient was released more than a week ago, limiting the adherence to compliance and providing less accurate predictions. In this paper, we present a neuro-symbolic approach for predictive process monitoring, leveraging Logic Tensor Networks (LTNs) to inject process knowledge into predictive models. The proposed approach follows a structured pipeline consisting of four key stages: 1) feature extraction; 2) rule extraction; 3) knowledge base creation; and 4) knowledge injection. Our evaluation shows that, in addition to learning the process constraints, the neuro-symbolic model also achieves better performance, demonstrating higher compliance and improved accuracy compared to baseline approaches across all compliance-aware experiments.","agent_summary":"","authors":["Fabrizio De Santis","Gyunam Park","Wil M. P. van der Aalst","Francesco Zanichelli"],"bibtex":"@inproceedings{10.1007/978-3-032-28117-3_13,\n  author    = {De Santis, Fabrizio and Gyunam Park and van der Aalst, Wil M. P. and Zanichelli, Francesco},\n  editor    = {Fuentes, Lidia and Plebani, Pierluigi and Combi, Carlo and Reijers, Hajo},\n  title     = {Compliance-Aware Predictive Process Monitoring: A Neuro-Symbolic Approach},\n  booktitle = {Advanced Information Systems Engineering},\n  year      = {2026},\n  publisher = {Springer Nature Switzerland},\n  address   = {Cham},\n  pages     = {223--239},\n  abstract  = {Existing approaches for predictive process monitoring are sub-symbolic, meaning that they learn correlations between descriptive features and a target feature fully based on data, e.g., predicting the surgical needs of a patient based on historical events and biometrics. However, such approaches fail to incorporate domain-specific process constraints (knowledge), e.g., surgery can only be planned if the patient was released more than a week ago, limiting the adherence to compliance and providing less accurate predictions. In this paper, we present a neuro-symbolic approach for predictive process monitoring, leveraging Logic Tensor Networks (LTNs) to inject process knowledge into predictive models. The proposed approach follows a structured pipeline consisting of four key stages: 1) feature extraction; 2) rule extraction; 3) knowledge base creation; and 4) knowledge injection. Our evaluation shows that, in addition to learning the process constraints, the neuro-symbolic model also achieves better performance, demonstrating higher compliance and improved accuracy compared to baseline approaches across all compliance-aware experiments.},\n  isbn      = {978-3-032-28117-3}\n}","code":"","contributions":["A four-stage neuro-symbolic pipeline for predictive process monitoring: feature extraction, rule extraction, knowledge base creation, and knowledge injection through Logic Tensor Networks (LTNs).","A feature vocabulary of control-flow, temporal and payload features aligned with Declare constraints, service-level agreements and payload rules, so that every rule type maps onto features.","A translation of control-flow rules in Linear Temporal Logic, temporal rules and payload rules into first-order logic, with a categorization into class-dependent (outcome- or non-outcome-oriented) and class-independent knowledge.","Three injection mechanisms relative to the classification predicate: feature expansion at the input, output refinement on predicate outputs, and parallel constraints as auxiliary objectives.","A compliance-aware evaluation protocol on four public logs with eight architectural variants and a constraint-satisfaction metric; code released on GitHub (NeSyPPM-Compliance)."],"date":"2026-06-09","doi":"10.1007/978-3-032-28117-3_13","featured":false,"id":"2026-CAiSE-CompliancePPM","markdown_url":"https://www.gyunam.com/publication/2026-caise-complianceppm/index.md","number":"","pages":"223–239","pdf":"https://www.gyunam.com/publication/2026-caise-complianceppm/paper.pdf","projects":[],"publisher":"Springer","research_lines":["neuro-symbolic-process-learning"],"series":"","summary":"","tags":[],"title":"Compliance-Aware Predictive Process Monitoring: A Neuro-Symbolic Approach","type":"paper-conference","url":"https://www.gyunam.com/publication/2026-caise-complianceppm/","venue":"Advanced Information Systems Engineering","venue_short":"","volume":"","year":2026},{"abstract":"Decomposition is widely used in process mining to enhance scalability, facilitate focused analysis, and mitigate the cognitive complexity of large-scale models. While both process discovery and conformance checking benefit from decomposition, existing approaches—especially those based on single-entry single-exit (SESE) fragments—impose structural constraints that limit diagnostic value. This paper proposes a complementary decomposition technique that partitions a WF-net into isolated subnets, i.e., internally coherent fragments with clear interfaces. The decomposition is hierarchical, independent of SESE assumptions, and compatible with valid system net decompositions, enabling divide-and-conquer conformance checking. Experiments show that our approach improves alignment runtime and yields fitness approximations that are closer to the true values than existing decomposition methods.","agent_summary":"","authors":["Tsung-Hao Huang","Lukas M. Jansen","Marco Pegoraro","Gyunam Park","Wil M. P. van der Aalst"],"bibtex":"@inproceedings{10.1007/978-3-032-28110-4_17,\n  author    = {Huang, Tsung-Hao and Jansen, Lukas M. and Pegoraro, Marco and Gyunam Park and van der Aalst, Wil M. P.},\n  editor    = {Fuentes, Lidia and Plebani, Pierluigi and Combi, Carlo and Reijers, Hajo},\n  title     = {Flexible and Hierarchical Decomposition of Workflow Nets for Process Analysis},\n  booktitle = {Advanced Information Systems Engineering},\n  year      = {2026},\n  publisher = {Springer Nature Switzerland},\n  address   = {Cham},\n  pages     = {305--321},\n  abstract  = {Decomposition is widely used in process mining to enhance scalability, facilitate focused analysis, and mitigate the cognitive complexity of large-scale models. While both process discovery and conformance checking benefit from decomposition, existing approaches---especially those based on single-entry single-exit (SESE) fragments---impose structural constraints that limit diagnostic value. This paper proposes a complementary decomposition technique that partitions a WF-net into isolated subnets, i.e., internally coherent fragments with clear interfaces. The decomposition is hierarchical, independent of SESE assumptions, and compatible with valid system net decompositions, enabling divide-and-conquer conformance checking. Experiments show that our approach improves alignment runtime and yields fitness approximations that are closer to the true values than existing decomposition methods.},\n  isbn      = {978-3-032-28110-4}\n}","code":"","contributions":["Formalizes isolated subnets, transition-bordered fragments of a workflow net whose start transitions share one input place set and end transitions share one output place set, and a decomposition into disjoint isolated subnets covering all transitions.","An integer linear programming (ILP) formulation, solved iteratively with `PySCIPOpt`, that extracts the maximal non-trivial isolated subnet around a seed transition and yields a hierarchical, recursively refinable decomposition.","Proves that sub-workflow nets derived from isolated subnets inherit soundness and free-choiceness from the original net.","A construction (extended and induced valid decomposition) that adds place-induced subnets and merges fragments sharing silent transitions so the result is a valid decomposition for divide-and-conquer conformance checking.","An implementation and experimental artifacts published in a public repository, evaluated against general decomposition, single-entry single-exit (SESE) decomposition, and non-decomposed alignments on synthetic models, PLG-generated models, and BPI Challenge 2015 logs."],"date":"2026-06-08","doi":"10.1007/978-3-032-28110-4_17","featured":false,"id":"2026-CAiSE-Decomposition","markdown_url":"https://www.gyunam.com/publication/2026-caise-decomposition/index.md","number":"","pages":"305–321","pdf":"https://www.gyunam.com/publication/2026-caise-decomposition/paper.pdf","projects":[],"publisher":"Springer","research_lines":["process-discovery-and-diagnostics"],"series":"","summary":"","tags":[],"title":"Flexible and Hierarchical Decomposition of Workflow Nets for Process Analysis","type":"paper-conference","url":"https://www.gyunam.com/publication/2026-caise-decomposition/","venue":"Advanced Information Systems Engineering","venue_short":"","volume":"","year":2026},{"abstract":"Complex processes can be difficult to understand from event data. Traditional process models effectively visualize control flows, but lack temporal context for performance analysis. To address this, the Performance Spectrum was introduced to map the dynamics of all steps and cases over time. However, its manual interpretation is challenging due to the overlapping patterns and noise. In this tool demonstration paper, we present PerSpect, an interactive, web-based user interface that significantly enhances the original Performance Spectrum approach. To facilitate interactive analysis, the tool automates the targeted detection of established operational behaviors (e.g., batching and FIFO), alongside advanced filtering capabilities and a side-by-side comparison function for evaluating the same segment across different process variants.","agent_summary":"","authors":["Moritz Schlegelmilch","Piotr Zaniewski","Volodymyr Diemieniev","Jasper Saathoff","Tsung-Hao Huang","Gyunam Park"],"bibtex":"@inproceedings{10.1007/978-3-032-27997-2_18,\n  author    = {Moritz Schlegelmilch and Piotr Zaniewski and Volodymyr Diemieniev and Jasper Saathoff and Tsung{-}Hao Huang and Gyunam Park},\n  editor    = {Francesca Zerbato and Jelena Zdravkovic and Luise Pufahl and Geert Poels and Marite Kirikova},\n  title     = {PerSpect: An Interactive Tool for Performance Pattern Mining},\n  booktitle = {Intelligent Information Systems - CAiSE 2026 Forum and Doctoral Consortium, Verona, Italy, June 8-12, 2026, Proceedings},\n  series    = {Lecture Notes in Business Information Processing},\n  volume    = {587},\n  pages     = {158--166},\n  publisher = {Springer},\n  year      = {2026},\n  url       = {https://doi.org/10.1007/978-3-032-27997-2\\_18},\n  doi       = {10.1007/978-3-032-27997-2\\_18}\n}","code":"","contributions":["`PerSpect`, an open-source web-based tool (GitHub) that reimplements the Performance Spectrum as the foundation of a pattern-oriented analysis workflow.","Automated batch detection in a spectrum segment via DBSCAN clustering on start, end, or both timestamps, with user-set epsilon and minimum-sample parameters and a FIFO versus non-FIFO distinction.","Global and segment-specific filters (variant, date range, duration quartile, interactive case selection) whose effects propagate across all displayed spectra, with tracked filter history.","Side-by-side comparison of named spectrum configurations, process and batch statistics, frequency diagrams, and export of the filtered cases as an XES event log.","A client-server architecture with a FastAPI backend, SQLite metadata store, in-memory LRU caching of logs, and a Vue single-page frontend."],"date":"2026-06-07","doi":"10.1007/978-3-032-27997-2_18","featured":false,"id":"2026-CAiSE-Forum-PerSpect","markdown_url":"https://www.gyunam.com/publication/2026-caise-forum-perspect/index.md","number":"","pages":"158–166","pdf":"https://www.gyunam.com/publication/2026-caise-forum-perspect/paper.pdf","projects":[],"publisher":"Springer","research_lines":["process-discovery-and-diagnostics"],"series":"LNBIP","summary":"","tags":[],"title":"PerSpect: An Interactive Tool for Performance Pattern Mining","type":"paper-conference","url":"https://www.gyunam.com/publication/2026-caise-forum-perspect/","venue":"Intelligent Information Systems - CAiSE 2026 Forum and Doctoral Consortium, Verona, Italy, June 8-12, 2026, Proceedings","venue_short":"","volume":"587","year":2026},{"abstract":"Predictive modeling on sequential event data is critical for fraud detection and healthcare monitoring. Existing data-driven approaches learn correlations from historical data but fail to incorporate domain-specific sequential constraints and logical rules governing event relationships, limiting accuracy and regulatory compliance. For example, healthcare procedures must follow specific sequences, and financial transactions must adhere to compliance rules. We present a neuro-symbolic approach integrating domain knowledge as differentiable logical constraints using Logic Networks (LTNs). We formalize control-flow, temporal, and payload knowledge using Linear Temporal Logic and first-order logic. Our key contribution is a two-stage optimization strategy addressing LTNs’ tendency to satisfy logical formulas at the expense of predictive accuracy. The approach uses weighted axiom loss during pretraining to prioritize data learning, followed by rule pruning that retains only consistent, contributive axioms based on satisfaction dynamics. Evaluation on four real-world event logs shows that domain knowledge injection significantly improves predictive performance, with the two-stage optimization proving essential knowledge (without it, knowledge can severely degrade performance). The approach excels particularly in compliance-constrained scenarios with limited compliant training examples, achieving superior performance compared to purely data-driven baselines while ensuring adherence to domain constraints.","agent_summary":"","authors":["Fabrizio De Santis","Gyunam Park","Francesco Zanichelli"],"bibtex":"@inproceedings{DBLP:conf/pakdd/SantisPZ26,\n  author    = {Fabrizio De Santis and Gyunam Park and Francesco Zanichelli},\n  editor    = {Raymond Chi{-}Wing Wong and Hanghang Tong and Hua Lu and James T. Kwok and Flora D. Salim and Yuanfeng Song and Man Lung Yiu},\n  title     = {Neuro-Symbolic Learning for Predictive Process Monitoring via Two-Stage Logic Tensor Networks with Rule Pruning},\n  booktitle = {Advances in Knowledge Discovery and Data Mining - 30th Pacific-Asia Conference on Knowledge Discovery and Data Mining, {PAKDD} 2026, Hong Kong, China, June 9-12, 2026, Proceedings, Part {II}},\n  series    = {Lecture Notes in Computer Science},\n  volume    = {16598},\n  pages     = {104--118},\n  publisher = {Springer},\n  year      = {2026},\n  url       = {https://doi.org/10.1007/978-981-92-1462-4\\_9},\n  doi       = {10.1007/978-981-92-1462-4\\_9}\n}","code":"","contributions":["A scheme for extracting and categorizing process knowledge into control-flow, temporal and payload features and rules, formalized in Linear Temporal Logic (LTL) and first-order logic using Declare mining, SLA compliance analysis and statistical correlations.","A neuro-symbolic outcome classifier for predictive process monitoring built on Logic Tensor Networks (LTN) with LSTM or Transformer backbones, separating data axioms from knowledge axioms.","A two-stage optimization: weighted axiom loss (0.8 data, 0.2 knowledge) during pretraining, then rule pruning with an analytic gating score from mean and variance of rule satisfaction, followed by fine-tuning on the pruned knowledge base.","A compliance-aware evaluation protocol that enriches test sets with rule-compliant traces, used alongside a standard temporal split.","An open-source implementation on GitHub (FabrizioDeSantis/NeSy-PPM)."],"date":"2026-06-07","doi":"10.1007/978-981-92-1462-4_9","featured":false,"id":"2026-PAKDD-TwoStage","markdown_url":"https://www.gyunam.com/publication/2026-pakdd-twostage/index.md","number":"","pages":"104–118","pdf":"https://www.gyunam.com/publication/2026-pakdd-twostage/paper.pdf","projects":[],"publisher":"Springer","research_lines":["neuro-symbolic-process-learning"],"series":"LNCS","summary":"","tags":[],"title":"Neuro-Symbolic Learning for Predictive Process Monitoring via Two-Stage Logic Tensor Networks with Rule Pruning","type":"paper-conference","url":"https://www.gyunam.com/publication/2026-pakdd-twostage/","venue":"Advances in Knowledge Discovery and Data Mining - 30th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2026, Hong Kong, China, June 9-12, 2026, Proceedings, Part II","venue_short":"","volume":"16598","year":2026},{"abstract":"Sepsis mortality prediction in intensive care units represents a critical engineering challenge in healthcare systems, where timely intervention through intelligent decision support systems can significantly reduce mortality rates. Current clinical decision support systems rely on black-box machine learning models that lack the interpretability required for safe deployment in life-critical engineering applications. We develop a neuro-symbolic artificial intelligence framework that integrates Logic Tensor Networks with automated clinical knowledge extraction for interpretable mortality prediction. Our novel contributions include: (1) an automated pipeline for transforming clinical guidelines into first-order logic constraints using medical knowledge graphs and rule mining; (2) a dual axiom architecture with weak anchoring that enables context-adaptive learning while maintaining semantic grounding; and (3) a three-tier explainability framework providing concept-level, rule-level, and patient-level interpretability. Using 19,328 sepsis patients from a publicly available real-world clinical dataset, our system achieves 86.45% accuracy at 6-h prediction windows, outperforming traditional machine learning baselines while providing full transparency in decision-making. External validation on 6735 patients demonstrates preserved ranking ability despite domain shift challenges.","agent_summary":"","authors":["Fabrizio De Santis","Gyunam Park","Francesco Zanichelli"],"bibtex":"@article{DESANTIS2026114920,\n  title     = {Neuro-symbolic artificial intelligence for real-time sepsis mortality prediction},\n  journal   = {Engineering Applications of Artificial Intelligence},\n  volume    = {177},\n  pages     = {114920},\n  year      = {2026},\n  issn      = {0952-1976},\n  doi       = {https://doi.org/10.1016/j.engappai.2026.114920},\n  url       = {https://www.sciencedirect.com/science/article/pii/S0952197626012029},\n  author    = {Fabrizio {De Santis} and Gyunam Park and Francesco Zanichelli},\n  abstract  = {Sepsis mortality prediction in intensive care units represents a critical engineering challenge in healthcare systems, where timely intervention through intelligent decision support systems can significantly reduce mortality rates. Current clinical decision support systems rely on black-box machine learning models that lack the interpretability required for safe deployment in life-critical engineering applications. We develop a neuro-symbolic artificial intelligence framework that integrates Logic Tensor Networks with automated clinical knowledge extraction for interpretable mortality prediction. Our novel contributions include: (1) an automated pipeline for transforming clinical guidelines into first-order logic constraints using medical knowledge graphs and rule mining; (2) a dual axiom architecture with weak anchoring that enables context-adaptive learning while maintaining semantic grounding; and (3) a three-tier explainability framework providing concept-level, rule-level, and patient-level interpretability. Using 19,328 sepsis patients from a publicly available real-world clinical dataset, our system achieves 86.45% accuracy at 6-h prediction windows, outperforming traditional machine learning baselines while providing full transparency in decision-making. External validation on 6735 patients demonstrates preserved ranking ability despite domain shift challenges.}\n}","code":"","contributions":["NeSy-SMP, a neuro-symbolic framework for sepsis mortality prediction that grounds Logic Tensor Networks (LTN) predicates in a bidirectional LSTM over temporal sequences of clinical events plus comorbidity features.","An automated knowledge pipeline that builds a SNOMED CT knowledge graph from the Surviving Sepsis Campaign guidelines, mines Horn rules with `AnyBURL` (confidence above 0.8) and translates them into first-order logic axioms.","A dual axiom architecture that separates data axioms from knowledge axioms with a weighted satisfiability loss (0.8/0.2) and weakly anchors each clinical predicate to an established threshold.","A three-tier explainability framework covering clinical concept learning verification, medical rule consistency validation and an individual patient reasoning dashboard.","An evaluation protocol over 6, 12, 24 and 48-hour lead times on MIMIC-IV with clinical operating-point analysis and external validation on eICU, with code released for reproduction."],"date":"2026-04-24","doi":"10.1016/j.engappai.2026.114920","featured":false,"id":"2026-EAAI-Sepsis","markdown_url":"https://www.gyunam.com/publication/2026-eaai-sepsis/index.md","number":"","pages":"114920","pdf":"https://www.gyunam.com/publication/2026-eaai-sepsis/paper.pdf","projects":[],"publisher":"Elsevier","research_lines":["neuro-symbolic-process-learning"],"series":"","summary":"","tags":[],"title":"Neuro-symbolic artificial intelligence for real-time sepsis mortality prediction","type":"article-journal","url":"https://www.gyunam.com/publication/2026-eaai-sepsis/","venue":"Engineering Applications of Artificial Intelligence","venue_short":"","volume":"177","year":2026},{"abstract":"Large Reasoning Models (LRMs), a subset of Large Language Models (LLMs) trained to articulate their chain-of-thought, have shown promise in tackling complex scientific tasks. However, evaluating and configuring their reasoning processes remains underexplored. This paper leverages a process mining-specific LLM evaluation framework to propose a methodology for analyzing and configuring LRMs. We introduce an approach to extract and classify reasoning steps by type (e.g., Deductive Reasoning, or Hypothesis Generation) and effect (Positive, Indifferent, Negative) on the overall reasoning, enabling a detailed assessment of reasoning quality. From this, we derive a new benchmark, PMLRM-Bench, which evaluates not only the correctness of outputs but also the robustness of the reasoning process. A case study on the QwQ-32B LLM demonstrates how targeted adjustments to reasoning type frequencies can boost task-specific performance. Our results reveal distinct reasoning patterns across models and provide actionable insights for LRM configuration. This work bridges process mining and LLM evaluation, offering a scalable framework for reasoning analysis.","agent_summary":"","authors":["Alessandro Berti","Humam Kourani","Gyunam Park","Wil M. P. van der Aalst"],"bibtex":"@inproceedings{10.1007/978-3-032-13426-4_30,\n  author    = {Berti, Alessandro and Kourani, Humam and Gyunam Park and van der Aalst, Wil M. P.},\n  editor    = {van de Weerd, Inge and Estrada Torres, Bedilia and van der Aa, Han},\n  title     = {Configuring Large Reasoning Models Using Process Mining: A Benchmark and a Case Study},\n  booktitle = {Business Process Management Workshops},\n  year      = {2026},\n  publisher = {Springer Nature Switzerland},\n  address   = {Cham},\n  pages     = {412--424},\n  abstract  = {Large Reasoning Models (LRMs), a subset of Large Language Models (LLMs) trained to articulate their chain-of-thought, have shown promise in tackling complex scientific tasks. However, evaluating and configuring their reasoning processes remains underexplored. This paper leverages a process mining-specific LLM evaluation framework to propose a methodology for analyzing and configuring LRMs. We introduce an approach to extract and classify reasoning steps by type (e.g., Deductive Reasoning, or Hypothesis Generation) and effect (Positive, Indifferent, Negative) on the overall reasoning, enabling a detailed assessment of reasoning quality. From this, we derive a new benchmark, PMLRM-Bench, which evaluates not only the correctness of outputs but also the robustness of the reasoning process. A case study on the QwQ-32B LLM demonstrates how targeted adjustments to reasoning type frequencies can boost task-specific performance. Our results reveal distinct reasoning patterns across models and provide actionable insights for LRM configuration. This work bridges process mining and LLM evaluation, offering a scalable framework for reasoning analysis.},\n  isbn      = {978-3-032-13426-4}\n}","code":"","contributions":["A reasoning analysis pipeline that parses chain-of-thought traces into steps and classifies each by one of ten reasoning types and by positive, indifferent or negative effect, using a judge LLM.","A composite score S that rewards correct conclusions and positive-effect steps while penalizing partially correct and wrong conclusions and indifferent or negative steps.","PMLRM-Bench, an extension of PM-LLM-Benchmark v2.0 covering 17 large reasoning models, released on GitHub (fit-alessandro-berti/pmllmbench-lrms-reasoning-analysis).","A case study protocol that varies the frequency of hypothesis generation, ethical reasoning and validation/backtracking in QwQ-32B via system prompts and re-evaluates the variants.","A validation of the judge's classifications with a second judge LLM."],"date":"2026-04-01","doi":"10.1007/978-3-032-13426-4_30","featured":false,"id":"2025-BPM-Workshop-LRM","markdown_url":"https://www.gyunam.com/publication/2025-bpm-workshop-lrm/index.md","number":"","pages":"412–424","pdf":"https://www.gyunam.com/publication/2025-bpm-workshop-lrm/paper.pdf","projects":[],"publisher":"Springer","research_lines":["process-discovery-and-diagnostics"],"series":"","summary":"","tags":[],"title":"Configuring Large Reasoning Models Using Process Mining: A Benchmark and a Case Study","type":"paper-conference","url":"https://www.gyunam.com/publication/2025-bpm-workshop-lrm/","venue":"Business Process Management Workshops","venue_short":"","volume":"","year":2026},{"abstract":"With the growing availability of unstructured text data in organizations, automating the extraction of process models from natural language descriptions has become increasingly crucial. Traditional rule-based techniques face challenges such as limited generalization and constraints to narrow sets of control-flow constructs. We present a systematic empirical study of Large Language Models (LLMs) for translating natural language sentences into declarative process constraints using the Declare modeling language. Through comprehensive evaluation across seven LLM architectures, we demonstrate that fine-tuned models achieve 97.8% template accuracy compared to 53.8% for existing rule-based approaches. Our fine-tuning approach substantially outperforms prompting techniques (99.4% vs. 56.5% template accuracy), establishing clear guidance for practical deployment. We contribute a benchmark dataset of 969 sentence-constraint pairs across 11 Declare templates, direction-sensitive evaluation metrics, and complete reproducibility materials. The extracted constraints enable practical decision support applications including compliance monitoring, conformance checking, and automated governance dashboards.","agent_summary":"","authors":["Gyunam Park","Julian Kofferath","Minsu Cho"],"bibtex":"@article{PARK2026114627,\n  title     = {Extracting declarative constraints for process modeling from natural language descriptions with large language models},\n  journal   = {Decision Support Systems},\n  volume    = {203},\n  pages     = {114627},\n  year      = {2026},\n  issn      = {0167-9236},\n  doi       = {https://doi.org/10.1016/j.dss.2026.114627},\n  url       = {https://www.sciencedirect.com/science/article/pii/S0167923626000163},\n  author    = {Gyunam Park and Julian Kofferath and Minsu Cho},\n  abstract  = {With the growing availability of unstructured text data in organizations, automating the extraction of process models from natural language descriptions has become increasingly crucial. Traditional rule-based techniques face challenges such as limited generalization and constraints to narrow sets of control-flow constructs. We present a systematic empirical study of Large Language Models (LLMs) for translating natural language sentences into declarative process constraints using the Declare modeling language. Through comprehensive evaluation across seven LLM architectures, we demonstrate that fine-tuned models achieve 97.8% template accuracy compared to 53.8% for existing rule-based approaches. Our fine-tuning approach substantially outperforms prompting techniques (99.4% vs. 56.5% template accuracy), establishing clear guidance for practical deployment. We contribute a benchmark dataset of 969 sentence-constraint pairs across 11 Declare templates, direction-sensitive evaluation metrics, and complete reproducibility materials. The extracted constraints enable practical decision support applications including compliance monitoring, conformance checking, and automated governance dashboards.}\n}","code":"","contributions":["A pipeline that translates each sentence of a process description into a Declare constraint with a fine-tuned decoder-only large language model (LLM), matches activity labels across constraints and merges them into a Declare process model.","Parameter-efficient fine-tuning with Low-Rank Adaptation (LoRA) of five open seven-to-eight-billion-parameter models (Gemma 7B, Mistral 7B, Falcon 7B, Llama-2 7B, Llama-3 8B) on a question-and-answer prompt format.","A publicly released benchmark dataset of 969 sentences paired with expert-annotated Declare constraints covering 11 templates across healthcare, compliance, education and finance.","An evaluation metric that weights template errors above activity errors and penalizes direction errors, which the slot-filling precision and recall used previously cannot detect.","Zero-shot and few-shot prompting strategies for matching semantically equivalent activity labels (verb-, noun- and gerund-based) across extracted constraints."],"date":"2026-01-30","doi":"10.1016/j.dss.2026.114627","featured":false,"id":"2026-DSS-DeclarativeNL","markdown_url":"https://www.gyunam.com/publication/2026-dss-declarativenl/index.md","number":"","pages":"114627","pdf":"https://www.gyunam.com/publication/2026-dss-declarativenl/paper.pdf","projects":[],"publisher":"Elsevier","research_lines":["neuro-symbolic-process-learning"],"series":"","summary":"","tags":[],"title":"Extracting declarative constraints for process modeling from natural language descriptions with large language models","type":"article-journal","url":"https://www.gyunam.com/publication/2026-dss-declarativenl/","venue":"Decision Support Systems","venue_short":"","volume":"203","year":2026},{"abstract":"Business process simulation (BPS) has emerged as a crucial tool that offers a risk-free virtual environment to analyze, test, and optimize complex service compositions and orchestrations in cloud and edge computing environments. BPS enables the evaluation of alternative scenarios (a.k.a. “what-if” scenarios) by capturing the dynamic behavior of service interactions, including control-flow, task durations, and resource utilization. Several techniques to discover BPS models employ black-box predictors to characterize the run-time simulation aspects. The downside of these approaches is that the rules guiding the predictors cannot be provided in an intelligible form. This means that service architects and process analysts cannot modify or explore the decision-making logic that drives service orchestration and composition in these simulations. Moreover, these models are often deterministic and thus unable to capture uncertainty, which is essential to realistically simulate dynamic environments. This paper presents white-box predictors based on probabilistic decision trees, which are intelligible and easy to configure. The experiments show that white-box predictors can improve the simulation accuracy and variability, while being naturally intelligible.","agent_summary":"","authors":["Francesco Vinci","Gyunam Park","Wil M. P. van der Aalst","Massimiliano de Leoni"],"bibtex":"@inproceedings{10.1007/978-981-95-5015-9_24,\n  author    = {Vinci, Francesco and Gyunam Park and van der Aalst, Wil M. P. and de Leoni, Massimiliano},\n  editor    = {Aiello, Marco and Deng, Shuiguang and Murillo, Juan-Manuel and Georgievski, Ilche and Benatallah, Boualem and Wang, Zhongjie},\n  title     = {Reliable and Configurable Process Simulations via Probabilistic White-Box Models},\n  booktitle = {Service-Oriented Computing},\n  year      = {2026},\n  publisher = {Springer Nature Singapore},\n  address   = {Singapore},\n  pages     = {337--352},\n  abstract  = {Business process simulation (BPS) has emerged as a crucial tool that offers a risk-free virtual environment to analyze, test, and optimize complex service compositions and orchestrations in cloud and edge computing environments. BPS enables the evaluation of alternative scenarios (a.k.a. ``what-if'' scenarios) by capturing the dynamic behavior of service interactions, including control-flow, task durations, and resource utilization. Several techniques to discover BPS models employ black-box predictors to characterize the run-time simulation aspects. The downside of these approaches is that the rules guiding the predictors cannot be provided in an intelligible form. This means that service architects and process analysts cannot modify or explore the decision-making logic that drives service orchestration and composition in these simulations. Moreover, these models are often deterministic and thus unable to capture uncertainty, which is essential to realistically simulate dynamic environments. This paper presents white-box predictors based on probabilistic decision trees, which are intelligible and easy to configure. The experiments show that white-box predictors can improve the simulation accuracy and variability, while being naturally intelligible.},\n  isbn      = {978-981-95-5015-9}\n}","code":"","contributions":["Decision-Aware Business Process Simulation (DBPS) models: a Petri net combined with probabilistic decision trees that set transition weights, activity execution times, resource-specific waiting times and inter-arrival times from trace history, attributes, resource, workload, weekday and hour.","A discovery technique that aligns the log to the control-flow model, trains CART decision trees per transition, activity and resource, and fits a per-leaf distribution (normal, exponential, log-normal, uniform or constant) by minimizing Wasserstein distance.","Three what-if interaction operations on the trees: changing leaf distributions, replacing a subtree by a leaf, and replacing a leaf by a subtree.","An open-source Python package (`prosit` on GitHub) that discovers a model from an event log and Petri net or lets users define each element manually.","An evaluation protocol on five logs (Purchase-to-Pay, a pharmacy process, academic credential recognition, and the BPI Challenge 2012 and 2017 workflow logs) with control-flow, cycle-time, arrival, rule-level and entropy measures."],"date":"2026-01-02","doi":"10.1007/978-981-95-5015-9_24","featured":false,"id":"2025-ICSOC-WhiteBoxSim","markdown_url":"https://www.gyunam.com/publication/2025-icsoc-whiteboxsim/index.md","number":"","pages":"337–352","pdf":"https://www.gyunam.com/publication/2025-icsoc-whiteboxsim/paper.pdf","projects":[],"publisher":"Springer","research_lines":["prescriptive-process-analytics"],"series":"","summary":"","tags":[],"title":"Reliable and Configurable Process Simulations via Probabilistic White-Box Models","type":"paper-conference","url":"https://www.gyunam.com/publication/2025-icsoc-whiteboxsim/","venue":"Service-Oriented Computing","venue_short":"","volume":"","year":2026},{"abstract":"Service-oriented architectures facilitate distributed business processes where multiple services collaborate to achieve business goals. However, traditional process mining approaches rely on single-case event logs that fail to capture the complex interactions between services and their associated data objects. Object-Centric Event Logs (OCELs) address this challenge by modeling service processes as graphs of interconnected events and objects, providing a foundation for comprehensive service analysis. While existing querying approaches offer capabilities for traditional event data, they lack the expressiveness needed for analyzing object-centric service interactions. This paper introduces GOProQ, a graphical query language specifically designed for querying object-centric service processes. We identify key requirements for service-oriented process querying, define a formal language that addresses these requirements, and implement an interactive tool for service analysts. Our evaluation across multiple real-world service datasets demonstrates GOProQ's expressiveness and performance for service monitoring in complex service-oriented environments.","agent_summary":"","authors":["Gyunam Park","Jan Niklas Adams","Daniel Schuster"],"bibtex":"@inproceedings{DBLP:conf/icsoc/ParkAS25,\n  author    = {Gyunam Park and Jan Niklas Adams and Daniel Schuster},\n  editor    = {Marco Aiello and Shuiguang Deng and Juan Manuel Murillo and Ilche Georgievski and Boualem Benatallah and Zhongjie Wang},\n  title     = {GOProQ: {A} Graphical Query Language for Object-Centric Process Analysis},\n  booktitle = {Service-Oriented Computing - 23rd International Conference, {ICSOC} 2025, Shenzhen, China, December 1-4, 2025, Proceedings, Part {II}},\n  series    = {Lecture Notes in Computer Science},\n  volume    = {16321},\n  pages     = {287--305},\n  publisher = {Springer},\n  year      = {2025},\n  url       = {https://doi.org/10.1007/978-981-95-5015-9\\_21},\n  doi       = {10.1007/978-981-95-5015-9\\_21}\n}","code":"","contributions":["A two-dimensional requirements framework for querying service process executions: structural elements (event nodes, precedence edges) crossed with label types (activities, object types), plus four expressiveness and three performance requirements.","GOProQ, a graphical query language with formal syntax and semantics over service process executions derived from OCELs, covering object-type queries, activity presence/start/end queries, directly-follows and eventually-follows queries with cardinality constraints, and AND/OR/NOT composition.","A query-reordering evaluation strategy that runs cheap object-type and activity queries before control-flow queries and NOT before AND before OR, to prune non-matching executions early.","An interactive tool to import OCELs, compose queries graphically, execute them and visualize matching executions, released with a user manual on GitHub (gyunamister/GOProQ).","An evaluation on six public OCELs (two loan-application logs, recruiting, travel reimbursement, procure-to-payment, order-to-cash; up to 392,018 events and 31,305 executions)."],"date":"2026-01-02","doi":"10.1007/978-981-95-5015-9_21","featured":false,"id":"2025-ICSOC-GOProQ","markdown_url":"https://www.gyunam.com/publication/2025-icsoc-goproq/index.md","number":"","pages":"287–305","pdf":"https://www.gyunam.com/publication/2025-icsoc-goproq/paper.pdf","projects":[],"publisher":"Springer","research_lines":["object-centric-process-mining"],"series":"LNCS","summary":"","tags":[],"title":"GOProQ: A Graphical Query Language for Object-Centric Process Analysis","type":"paper-conference","url":"https://www.gyunam.com/publication/2025-icsoc-goproq/","venue":"Service-Oriented Computing - 23rd International Conference, ICSOC 2025, Shenzhen, China, December 1-4, 2025, Proceedings, Part II","venue_short":"","volume":"16321","year":2026},{"abstract":"Business Process Simulation (BPS) provides a controlled environment to evaluate alternative process designs and perform “what-if” analyses. While several approaches exist for deriving simulation models directly from event logs, existing solutions are often limited in accessibility, configurability, and interpretability. We present ProSiT: Process Simulation Tool, a user-friendly environment for data–driven simulation. ProSiT supports (i) the ingestion of event logs and process models, (ii) automated discovery of control-flow, timing, resource, and trace attribute parameters, (iii) interactive configuration of alternative service scenarios, (iv) visualization of accuracy metrics, and (v) generation and statistical analysis of simulated event logs. ProSiT integrates state-of-the-art machine learning methods for parameter discovery in an explainable whitebox form, contrasting with existing deep learning–based approaches that act as black boxes. By combining simulation accuracy with transparency, ProSiT empowers service architects and process analysts to experiment with alternative designs, evaluate service-level impacts, and explore optimization opportunities through an intuitive graphical interface.","agent_summary":"","authors":["Francesco Vinci","Gyunam Park","Wil M. P. van der Aalst","Massimiliano de Leoni"],"bibtex":"@inproceedings{vinci2025prosit,\n  title     = {Prosit: A tool for interactive and transparent process simulations},\n  author    = {Vinci, Francesco and Gyunam Park and van der Aalst, Wil M. P. and de Leoni, Massimiliano},\n  booktitle = {Proceedings of the 23rd International Conference on Service-Oriented Computing (ICSOC 2025), Demo Track. Lecture Notes in Computer Science, Springer, Cham},\n  year      = {2025}\n}","code":"","contributions":["`ProSiT` (Process Simulation Tool), a web-based client-server application with a Flask backend, HTML/CSS/JavaScript frontend, SQLite persistence, and Docker distribution, with source code on GitHub.","A five-phase workflow: ingestion of XES logs and optional PNML models, parameter discovery, interactive scenario configuration, accuracy assessment, and simulation producing a downloadable synthetic log.","White-box parameter discovery based on probabilistic decision trees of controllable depth, covering transition weights, resource assignment, multitasking and calendars, arrival, execution and waiting time distributions, and trace attributes, with optional incremental discovery.","An accuracy assessment module comparing simulated and real logs by n-gram control-flow distance, temporal distribution metrics, resource handover realism, and entropy-based generalization.","A RESTful API (upload, discover, get and update parameters, simulate) enabling integration with external systems alongside the graphical interface."],"date":"2025-12-01","doi":"","featured":false,"id":"2025-ICSOC-Prosit","markdown_url":"https://www.gyunam.com/publication/2025-icsoc-prosit/index.md","number":"","pages":"","pdf":"https://www.gyunam.com/publication/2025-icsoc-prosit/paper.pdf","projects":[],"publisher":"","research_lines":["prescriptive-process-analytics"],"series":"","summary":"","tags":[],"title":"Prosit: A tool for interactive and transparent process simulations","type":"paper-conference","url":"https://www.gyunam.com/publication/2025-icsoc-prosit/","venue":"Proceedings of the 23rd International Conference on Service-Oriented Computing (ICSOC 2025), Demo Track. Lecture Notes in Computer Science, Springer, Cham","venue_short":"","volume":"","year":2025},{"abstract":"Process discovery aims to automatically derive process models from event logs, enabling organizations to analyze and improve their operational processes. Inductive mining algorithms, while prioritizing soundness and efficiency through hierarchical modeling languages, often impose a strict block-structured representation. This limits their ability to accurately capture the complexities of real-world processes. While recent advancements like the Partially Ordered Workflow Language (POWL) have addressed the block-structure limitation for concurrency, a significant gap remains in effectively modeling non-block-structured decision points. In this paper, we bridge this gap by proposing an extension of POWL to handle non-block-structured decisions through the introduction of choice graphs. Choice graphs offer a structured yet flexible approach to model complex decision logic within the hierarchical framework of POWL. We present an inductive mining discovery algorithm that uses our extension and preserves the quality guarantees of the inductive mining framework. Our experimental evaluation demonstrates that the discovered models, enriched with choice graphs, more precisely represent the complex decision-making behavior found in real-world processes, without compromising the high scalability inherent in inductive mining techniques.","agent_summary":"","authors":["Humam Kourani","Gyunam Park","Wil M. P. van der Aalst"],"bibtex":"@inproceedings{10.1007/978-3-032-02867-9_10,\n abstract = {Process discovery aims to automatically derive process models from event logs, enabling organizations to analyze and improve their operational processes. Inductive mining algorithms, while prioritizing soundness and efficiency through hierarchical modeling languages, often impose a strict block-structured representation. This limits their ability to accurately capture the complexities of real-world processes. While recent advancements like the Partially Ordered Workflow Language (POWL) have addressed the block-structure limitation for concurrency, a significant gap remains in effectively modeling non-block-structured decision points. In this paper, we bridge this gap by proposing an extension of POWL to handle non-block-structured decisions through the introduction of choice graphs. Choice graphs offer a structured yet flexible approach to model complex decision logic within the hierarchical framework of POWL. We present an inductive mining discovery algorithm that uses our extension and preserves the quality guarantees of the inductive mining framework. Our experimental evaluation demonstrates that the discovered models, enriched with choice graphs, more precisely represent the complex decision-making behavior found in real-world processes, without compromising the high scalability inherent in inductive mining techniques.},\n address = {Cham},\n author = {Kourani, Humam\nand Park, Gyunam\nand van der Aalst, Wil M. P.},\n booktitle = {Business Process Management},\n editor = {Senderovich, Arik\nand Cabanillas, Cristina\nand Vanderfeesten, Irene\nand A. Reijers, Hajo},\n isbn = {978-3-032-02867-9},\n pages = {144--161},\n publisher = {Springer Nature Switzerland},\n title = {Unlocking Non-Block-Structured Decisions: Inductive Mining with Choice Graphs},\n year = {2026}\n}","code":"","contributions":["Formalizes choice graphs, a directed start-to-end graph over sub-models that generalizes the exclusive-choice operator, and defines POWL 2.0 together with its language semantics.","A discussion of conversion of POWL 2.0 to workflow nets and of its expressiveness, covering sound state-machine and marked-graph workflow nets within the free-choice class.","A discovery algorithm that replaces the exclusive-choice cut of the POWL Inductive Miner with a valid choice graph cut computed from the directly-follows graph, restricted to acyclic graphs.","Proves, by induction over the recursion, that the extended miner preserves the Inductive Miner's fitness guarantee for every trace in the log.","An implementation in the Python library `powl` (pip-installable, Zenodo archived), a Streamlit web demo, and a published collection of all discovered models."],"date":"2025-08-31","doi":"10.1007/978-3-032-02867-9_10","featured":false,"id":"2025-BPM-POWL","markdown_url":"https://www.gyunam.com/publication/2025-bpm-powl/index.md","number":"","pages":"144–161","pdf":"https://www.gyunam.com/publication/2025-bpm-powl/paper.pdf","projects":[],"publisher":"Springer","research_lines":["process-discovery-and-diagnostics"],"series":"","summary":"","tags":[],"title":"Unlocking Non-Block-Structured Decisions: Inductive Mining with Choice Graphs","type":"paper-conference","url":"https://www.gyunam.com/publication/2025-bpm-powl/","venue":"Business Process Management","venue_short":"","volume":"","year":2025},{"abstract":"Business Process Simulation (BPS) refers to techniques designed to replicate the dynamic behavior of a business process. Many approaches have been proposed to automatically discover simulation models from historical event logs, reducing the cost and time to manually design them. However, in dynamic business environments, organizations continuously refine their processes to enhance efficiency, reduce costs, and improve customer satisfaction. Existing techniques to process simulation discovery lack adaptability to real-time operational changes. In this paper, we propose a streaming process simulation discovery technique that integrates Incremental Process Discovery with Online Machine Learning methods. This technique prioritizes recent data while preserving historical information, ensuring adaptation to evolving process dynamics. Experiments conducted on four different event logs demonstrate the importance in simulation of giving more weight to recent data while retaining historical knowledge. Our technique not only produces more stable simulations but also exhibits robustness in handling concept drift, as highlighted in one of the use cases.","agent_summary":"","authors":["Francesco Vinci","Gyunam Park","Wil M. P. van der Aalst","Massimiliano de Leoni"],"bibtex":"@inproceedings{10.1007/978-3-032-02867-9_27,\n abstract = {Business Process Simulation (BPS) refers to techniques designed to replicate the dynamic behavior of a business process. Many approaches have been proposed to automatically discover simulation models from historical event logs, reducing the cost and time to manually design them. However, in dynamic business environments, organizations continuously refine their processes to enhance efficiency, reduce costs, and improve customer satisfaction. Existing techniques to process simulation discovery lack adaptability to real-time operational changes. In this paper, we propose a streaming process simulation discovery technique that integrates Incremental Process Discovery with Online Machine Learning methods. This technique prioritizes recent data while preserving historical information, ensuring adaptation to evolving process dynamics. Experiments conducted on four different event logs demonstrate the importance in simulation of giving more weight to recent data while retaining historical knowledge. Our technique not only produces more stable simulations but also exhibits robustness in handling concept drift, as highlighted in one of the use cases.},\n address = {Cham},\n author = {Vinci, Francesco\nand Park, Gyunam\nand van der Aalst, Wil M. P.\nand de Leoni, Massimiliano},\n booktitle = {Business Process Management},\n editor = {Senderovich, Arik\nand Cabanillas, Cristina\nand Vanderfeesten, Irene\nand A. Reijers, Hajo},\n isbn = {978-3-032-02867-9},\n pages = {451--468},\n publisher = {Springer Nature Switzerland},\n title = {Online Discovery of Simulation Models for Evolving Business Processes},\n year = {2026}\n}","code":"","contributions":["Formalizes online simulation-model discovery as an update function over a sliding window of an event stream, producing a sequence of models each extending the previous one.","A four-step update technique: trace-fragment extraction, incremental control-flow discovery (Schuster et al.), descriptive-parameter refresh, and online updating of predictive parameters.","Uses Hoeffding Adaptive Trees with adaptive windowing (ADWIN) drift detection as explainable, incrementally trained models for durations, waiting times, arrivals, and branching probabilities.","An implementation in Python on top of the `Cortado` library and the `River` online-learning library, released on GitHub (`franvinci/ProcessSimulationTool`).","An evaluation protocol with ten temporal windows and eight simulation-quality distances (Chapela-Campa et al.) comparing single-batch, last-batch, and online discovery on four event logs."],"date":"2025-08-31","doi":"10.1007/978-3-032-02867-9_27","featured":false,"id":"2025-BPM-Sim","markdown_url":"https://www.gyunam.com/publication/2025-bpm-sim/index.md","number":"","pages":"451–468","pdf":"https://www.gyunam.com/publication/2025-bpm-sim/paper.pdf","projects":[],"publisher":"Springer","research_lines":["prescriptive-process-analytics"],"series":"","summary":"","tags":[],"title":"Online Discovery of Simulation Models for Evolving Business Processes","type":"paper-conference","url":"https://www.gyunam.com/publication/2025-bpm-sim/","venue":"Business Process Management","venue_short":"","volume":"","year":2025},{"abstract":"Process discovery aims to automatically derive process models from event logs, enabling organizations to analyze and improve their operational processes. Inductive mining algorithms, while prioritizing soundness and efficiency through hierarchical modeling languages, often impose a strict block-structured representation. This limits their ability to accurately capture the complexities of real-world processes. While recent advancements like the Partially Ordered Workflow Language (POWL) have addressed the block-structure limitation for concurrency, a significant gap remains in effectively modeling non-block-structured decision points. In this paper, we bridge this gap by proposing an extension of POWL to handle non-block-structured decisions through the introduction of choice graphs. Choice graphs offer a structured yet flexible approach to model complex decision logic within the hierarchical framework of POWL. We present an inductive mining discovery algorithm that uses our extension and preserves the quality guarantees of the inductive mining framework. Our experimental evaluation demonstrates that the discovered models, enriched with choice graphs, more precisely represent the complex decision-making behavior found in real-world processes, without compromising the high scalability inherent in inductive mining techniques.","agent_summary":"","authors":["Humam Kourani","Gyunam Park","Wil M. P. van der Aalst"],"bibtex":"@inproceedings{DBLP:conf/apn/KouraniPA25,\n author = {Humam Kourani and\nGyunam Park and\nWil M. P. van der Aalst},\n bibsource = {dblp computer science bibliography, https://dblp.org},\n biburl = {https://dblp.org/rec/conf/apn/KouraniPA25.bib},\n booktitle = {Application and Theory of Petri Nets and Concurrency - 46th International\nConference, PETRI NETS 2025, Paris, France, June 22-27, 2025,\nProceedings},\n doi = {10.1007/978-3-031-94634-9_12},\n editor = {Elvio Gilberto Amparore and\nLukasz Mikulski},\n pages = {242--264},\n publisher = {Springer},\n series = {Lecture Notes in Computer Science},\n timestamp = {Sun, 06 Jul 2025 13:23:39 +0200},\n title = {Translating Workflow Nets into the Partially Ordered Workflow Language},\n url = {https://doi.org/10.1007/978-3-031-94634-9_12},\n volume = {15714},\n year = {2025}\n}","code":"","contributions":["A recursive top-down algorithm that identifies exclusive-choice, loop and partial-order patterns in a safe and sound workflow net and projects the net onto sub-nets for further decomposition.","Formal definitions of the three patterns and their projections, plus reduction rules (explicit decision-point places, self-loop handling) that widen the set of nets the algorithm accepts.","Proofs that each projection preserves safeness and soundness and that each pattern preserves language, yielding a correctness theorem for every produced POWL model.","Defines the class of semi-block-structured workflow nets, which contains equivalents of all POWL models, and proves the algorithm is complete on that class.","An open-source implementation (GitHub humam-kourani/WF-net-to-POWL), also integrated into the `ProMoAI` tool, evaluated against the `PM4Py`-based workflow-net-to-process-tree converter."],"date":"2025-06-01","doi":"10.1007/978-3-031-94634-9_12","featured":false,"id":"2025-PETRI","markdown_url":"https://www.gyunam.com/publication/2025-petri/index.md","number":"","pages":"242–264","pdf":"https://www.gyunam.com/publication/2025-petri/paper.pdf","projects":[],"publisher":"Springer","research_lines":["process-discovery-and-diagnostics"],"series":"LNCS","summary":"","tags":[],"title":"Translating Workflow Nets into the Partially Ordered Workflow Language","type":"paper-conference","url":"https://www.gyunam.com/publication/2025-petri/","venue":"Application and Theory of Petri Nets and Concurrency - 46th International Conference, PETRI NETS 2025, Paris, France, June 22-27, 2025, Proceedings","venue_short":"","volume":"15714","year":2025},{"abstract":"In business processes, an operational problem refers to a deviation and an inefficiency that prohibits an organization from reaching its goals, e.g., a delay in approving a purchase order in a Procure-To-Pay (P2P) process. Operational process monitoring aims to assess the occurrence of such operational problems by analyzing event data that record the execution of business processes. Once the problems are detected, organizations can act upon the corresponding problems with viable actions, e.g., adding more resources, bypassing problematic activities, etc. A plethora of approaches have been proposed to implement operational process monitoring. The lion’s share of existing approaches assumes that a single case notion (e.g., a purchase order in a P2P process) exists in a business process and analyzes operational problems defined over the single case notion. However, most real-life business processes manifest the interplay of multiple interrelated objects. For instance, an execution of an omnipresent P2P process involves multiple objects of different types, e.g., purchase orders, goods receipts, invoices, etc. Applying the existing approaches in these object-centric business processes results in inaccurate or misleading results. In this study, we propose a novel approach to assessing operational problems within object-centric business processes. Our approach not only ensures an accurate assessment of existing problems but also facilitates the analysis of object-centric problems that consider the interaction among different objects. We evaluate this approach by applying it to both simulated business processes and real-life business processes.","agent_summary":"","authors":["Gyunam Park","Wil M. P. van der Aalst"],"bibtex":"@article{PARK2025104170,\n  title     = {Operational process monitoring: An object-centric approach},\n  journal   = {Computers in Industry},\n  volume    = {164},\n  pages     = {104170},\n  year      = {2025},\n  issn      = {0166-3615},\n  doi       = {https://doi.org/10.1016/j.compind.2024.104170},\n  url       = {https://www.sciencedirect.com/science/article/pii/S0166361524000988},\n  author    = {Gyunam Park and Wil M.P. {van der Aalst}},\n  abstract  = {In business processes, an operational problem refers to a deviation and an inefficiency that prohibits an organization from reaching its goals, e.g., a delay in approving a purchase order in a Procure-To-Pay (P2P) process. Operational process monitoring aims to assess the occurrence of such operational problems by analyzing event data that record the execution of business processes. Once the problems are detected, organizations can act upon the corresponding problems with viable actions, e.g., adding more resources, bypassing problematic activities, etc. A plethora of approaches have been proposed to implement operational process monitoring. The lion’s share of existing approaches assumes that a single case notion (e.g., a purchase order in a P2P process) exists in a business process and analyzes operational problems defined over the single case notion. However, most real-life business processes manifest the interplay of multiple interrelated objects. For instance, an execution of an omnipresent P2P process involves multiple objects of different types, e.g., purchase orders, goods receipts, invoices, etc. Applying the existing approaches in these object-centric business processes results in inaccurate or misleading results. In this study, we propose a novel approach to assessing operational problems within object-centric business processes. Our approach not only ensures an accurate assessment of existing problems but also facilitates the analysis of object-centric problems that consider the interaction among different objects. We evaluate this approach by applying it to both simulated business processes and real-life business processes.}\n}","code":"","contributions":["A taxonomy of object-centric operational problems covering occurrence, ordering relation, object involvement (absence, presence, cardinality) and time- and frequency-related performance problems.","A set of object-centric behavioral metrics over Object-Centric Event Logs (OCELs), including flow, sojourn, synchronization, pooling, lagging and readiness time derived from enabling events.","Operational Problem Graphs (OPGs), a hypergraph notation with activity and object type nodes and three edge types, with formal occurrence semantics and a monitoring function over OCELs.","A web application (`ProAct` monitoring engine) with an OPG editor, repository and assessment and visualization module, released with documentation, source code and the simulated and real-life OCELs.","Use cases on a simulated manufacturing process and a simulated purchase-to-pay process on SAP ERP (enterprise resource planning) plus a real-life loan application process."],"date":"2025-01-01","doi":"10.1016/j.compind.2024.104170","featured":false,"id":"2024-COMIND","markdown_url":"https://www.gyunam.com/publication/2024-comind/index.md","number":"","pages":"104170","pdf":"https://www.gyunam.com/publication/2024-comind/paper.pdf","projects":[],"publisher":"Elsevier","research_lines":["object-centric-process-mining","prescriptive-process-analytics"],"series":"","summary":"","tags":["Operational process monitoring","Object-centric process mining","Decision support systems","Operational support"],"title":"Operational process monitoring: An object-centric approach","type":"article-journal","url":"https://www.gyunam.com/publication/2024-comind/","venue":"Computers in Industry","venue_short":"","volume":"164","year":2025},{"abstract":"This paper presents a novel approach for generating actionable recommendations from educational event data collected by Campus Management Systems (CMS) to enhance study planning in higher education. The approach unfolds in three phases: feature identification tailored to the educational context, predictive modeling employing the RuleFit algorithm, and extracting actionable recommendations. We utilize diverse features, encompassing academic histories and course sequences, to capture the multi-dimensional nature of student academic behaviors. The effectiveness of our approach is empirically validated using data from the computer science bachelor's program at RWTH Aachen University, with the goal of predicting overall GPA and formulating recommendations to enhance academic performance. Our contributions lie in the novel adaptation of behavioral features for the educational domain and the strategic use of the RuleFit algorithm for both predictive modeling and the generation of practical recommendations, offering a data-driven foundation for informed study planning and academic decision-making.","agent_summary":"","authors":["Gyunam Park","Lukas Liss","Wil M. P. van der Aalst"],"bibtex":"@article{jiis_learning,\n abstract = {This paper presents a novel approach for generating actionable recommendations from educational event data collected by Campus Management Systems (CMS) to enhance study planning in higher education. The approach unfolds in three phases: feature identification tailored to the educational context, predictive modeling employing the RuleFit algorithm, and extracting actionable recommendations. We utilize diverse features, encompassing academic histories and course sequences, to capture the multi-dimensional nature of student academic behaviors. The effectiveness of our approach is empirically validated using data from the computer science bachelor's program at RWTH Aachen University, with the goal of predicting overall GPA and formulating recommendations to enhance academic performance. Our contributions lie in the novel adaptation of behavioral features for the educational domain and the strategic use of the RuleFit algorithm for both predictive modeling and the generation of practical recommendations, offering a data-driven foundation for informed study planning and academic decision-making.},\n author = {Gyunam Park\nand Liss, Lukas\nand van der Aalst, Wil M. P.},\n day = {13},\n doi = {10.1007/s10844-024-00873-w},\n issn = {1573-7675},\n journal = {Journal of Intelligent Information Systems},\n month = {Aug},\n title = {Learning recommendations from educational event data in higher education},\n url = {https://doi.org/10.1007/s10844-024-00873-w},\n year = {2024}\n}","code":"","contributions":["A three-phase approach that defines objectives, engineers behavioral features from educational event logs, and distills recommendations from an interpretable predictive model.","A catalogue of educational behavioral features adapted from a general process mining framework, spanning control-flow (occurrences, first and last occurrence, directly-follows, eventually-follows), data (credits, grades), and time (study and course duration) perspectives.","Uses the RuleFit algorithm (gradient-boosted rules plus a Lasso-regularized sparse linear model) for predicting grade point average and ranking rules by importance and coefficient.","A recommendation generation procedure that interprets high-importance rules by coefficient sign and magnitude into guidance on course load and course sequencing.","An empirical study on campus management data of 3,190 computer science bachelor students at RWTH Aachen University, with per-semester datasets and five-fold cross-validation."],"date":"2024-08-01","doi":"10.1007/s10844-024-00873-w","featured":false,"id":"2024-JIIS-LearningRecommendation","markdown_url":"https://www.gyunam.com/publication/2024-jiis-learningrecommendation/index.md","number":"","pages":"","pdf":"https://www.gyunam.com/publication/2024-jiis-learningrecommendation/paper.pdf","projects":[],"publisher":"Springer","research_lines":["prescriptive-process-analytics"],"series":"","summary":"","tags":[],"title":"Learning recommendations from educational event data in higher education","type":"article-journal","url":"https://www.gyunam.com/publication/2024-jiis-learningrecommendation/","venue":"Journal of Intelligent Information Systems","venue_short":"","volume":"","year":2024},{"abstract":"Process mining offers methods to analyze the actual controlflow behavior of a process. The two main available methods are process discovery and variant analysis. While process discovery aggregates all variants into one model, the models often suffer from high complexity and lack detailed granularity. Conversely, variants are simple and offer fine granularity but their sheer number makes them difficult to comprehend. To bridge this gap, we introduce the concept of super variants. These represent a middle ground between the complexity of discovered process models and the simplicity of variants, offering an aggregation of closely related variants. We propose a super-variant mining framework based on object-centric variants, evaluate its scalability, and demonstrate its utility through a practical use case. This new approach promises to enhance control-flow analysis by striking a new balance between complexity and aggregation.","agent_summary":"","authors":["Jan Niklas Adams","Emilie Hastrup-Kiil","Gyunam Park","Wil M. P. van der Aalst"],"bibtex":"@inproceedings{bpm2024,\n _editor = {Javier Troya and\nBrahim Medjahed and\nMario Piattini and\nLina Yao and\nPablo Fernández and\nAntonio Ruiz-Cortés},\n _pages = {36--53},\n _volume = {13740},\n author = {Jan Niklas Adams and\nEmilie Hastrup-Kiil and\nGyunam Park and\nWil M. P. van der Aalst},\n booktitle = {22nd Business Process Management Conference 2024, BPM\n2024, Krakow, Poland, 1-6 September, 2024, Proceedings},\n note = {to appear},\n publisher = {Springer},\n series = {Lecture Notes in Computer Science},\n title = {Super Variants},\n year = {2024}\n}","code":"","contributions":["The concept of super variants: object-centric variants extended with choice (including optionality) and object multiplicity through super objects, with an equivalence notion reducible to labeled graph isomorphism.","A super variant mining framework with intra-variant summarization (generating potential super variants and a hitting-set selection) and inter-variant summarization (a Levenshtein-based distance, a matching problem with a non-assignment penalty, recursive merging into a hierarchy).","A visualization that extends the object-centric variant lane notation with star-marked super objects, vertically split chevrons for choices and dotted borders for optional activities.","An open-source implementation on top of the `ocpa` library, released on GitHub (EmilieHK/Object-Centric-Super-Variants).","A proof of concept on a public procure-to-pay OCEL 2.0 event log and a scalability evaluation that decomposes computation time by framework step."],"date":"2024-06-06","doi":"10.1007/978-3-031-05760-1_10","featured":false,"id":"2024-BPM","markdown_url":"https://www.gyunam.com/publication/2024-bpm/index.md","number":"","pages":"111–128","pdf":"https://www.gyunam.com/publication/2024-bpm/paper.pdf","projects":[],"publisher":"Springer","research_lines":["object-centric-process-mining"],"series":"LNCS","summary":"","tags":[],"title":"Super Variants","type":"paper-conference","url":"https://www.gyunam.com/publication/2024-bpm/","venue":"22nd Business Process Management Conference 2024, BPM2024, Krakow, Poland, 1-6 September, 2024, Proceedings","venue_short":"","volume":"14940","year":2024},{"abstract":"Traditional process mining often simplifies the multi-object reality of business processes, linking each event to a single object called “the case”. This oversimplification may lead to inaccurate analyses, caused by missing interactions between object types. In contrast, object-centric process mining permits events to be tied to multiple objects, capturing complex interactions and providing a more accurate representation of business processes. This paper introduces an approach for supporting object-centric process mining utilizing Object-Centric Directly-Follows Graphs (OC-DFGs). Despite their advantages, e.g., simplicity, OC-DFGs have been relatively untapped for essential process mining tasks, such as conformance checking and performance analysis. In order to address this, our research presents a comprehensive approach for OC-DFG-based conformance checking and performance analysis. We fully implement the proposed approach as a web application and demonstrate the use of OC-DFGs for these tasks within a case study of a real-life loan application process.","agent_summary":"","authors":["Gyunam Park","Jan Niklas Adams","Wil M. P. van der Aalst"],"bibtex":"@inproceedings{bpm2024-forum,\n _editor = {Javier Troya and\nBrahim Medjahed and\nMario Piattini and\nLina Yao and\nPablo Fernández and\nAntonio Ruiz-Cortés},\n _pages = {36--53},\n _volume = {13740},\n author = {Gyunam Park and\nJan Niklas Adams and\nWil M. P. van der Aalst},\n booktitle = {22nd Business Process Management Forum 2024, BPM Forum\n2024, Krakow, Poland, 1-6 September, 2024, Proceedings},\n note = {to appear},\n publisher = {Springer},\n series = {Lecture Notes in Business Information Processing},\n title = {Conformance Checking and Performance Analysis Using Object-Centric Directly-Follows Graphs},\n year = {2024}\n}","code":"","contributions":["A conformance checking approach that projects an object-centric directly-follows graph per object type, translates each projection into a workflow net, aligns the projected event log, and maps log and model moves back onto the graph.","A visualization scheme distinguishing five kinds of log moves (dotted edges, self-loops) and model moves (double-line edges) on the object-centric graph.","A performance analysis approach that projects alignments onto the model, builds per-event relationships to preceding events, and computes sojourn, synchronization, flow, lagging and pooling time.","`Explori`, an open-source web application on top of the `ocpa` Python library (GitHub gyunamister/Explori) with log management, discovery, conformance checking and performance analysis.","A case study applying the approach to the BPI Challenge 2017 loan application process with applications and offers as object types."],"date":"2024-06-06","doi":"10.1007/978-3-031-70418-5_11","featured":false,"id":"2024-BPM-Forum","markdown_url":"https://www.gyunam.com/publication/2024-bpm-forum/index.md","number":"","pages":"179–196","pdf":"https://www.gyunam.com/publication/2024-bpm-forum/paper.pdf","projects":[],"publisher":"Springer","research_lines":["object-centric-process-mining","process-discovery-and-diagnostics"],"series":"","summary":"","tags":[],"title":"Conformance Checking and Performance Analysis Using Object-Centric Directly-Follows Graphs","type":"paper-conference","url":"https://www.gyunam.com/publication/2024-bpm-forum/","venue":"22nd Business Process Management Forum 2024, BPM Forum2024, Krakow, Poland, 1-6 September, 2024, Proceedings","venue_short":"","volume":"","year":2024},{"abstract":"Learning analytics, located at the intersection of learning science, data science, and computer science, aims to leverage educational data to enhance teaching and learning. However, as educational data increases, distilling meaningful insights presents challenges, particularly concerning individual learner differences. This work introduces a comprehensive approach for designing and automatically mapping learners into meaningful cohorts based on diverse learning behaviors. It defines four critical contexts, including engagement, direction, repetitiveness, and orderliness, and generates practical learning cohorts from their combinations. The approach employs a time-series-based clustering method with K-means clustering using dynamic time warping to identify similar learning patterns. Statistical techniques like the Mann-Kendall test and Theil-Sen estimator further refine the process. A case study on data science courses validates the approach, offering novel insights into learner behavior. The contributions include a novel time series approach to characterizing learning behavior, new learning cohorts based on critical contexts, and a systematic method for automated cohort identification.","agent_summary":"","authors":["Hyomin Kim","Gyunam Park","Minsu Cho"],"bibtex":"@article{kim2024unlocking,\n author = {Kim, Hyomin and Park, Gyunam and Cho, Minsu},\n journal = {Education and Information Technologies},\n pages = {1--41},\n publisher = {Springer},\n title = {Unlocking learner engagement and performance: A multidimensional approach to mapping learners to learning cohorts},\n year = {2024}\n}","code":"","contributions":["A taxonomy of nine learning cohorts (for example Orderly Engagers, Recurrent Disorderly Observers, Contrary Learners) derived from four behavioural dimensions: engagement, direction, repetitiveness, and orderliness.","A representation of learning behaviour as a time series over logical time, mapping the sequence of lectures a learner watched against the course's prescribed order.","A method that segments learners by record length, clusters them with K-means under dynamic time warping, and computes DTW barycenters per cluster.","A rule-based mapping of barycenters to cohorts using the Mann-Kendall trend test, the Theil-Sen slope estimator, and a log-difference fluctuation measure.","A case study on three data-science courses (18,405 learners, 171,440 video-watching events) from a real online education platform, including process models discovered with Fluxicon `Disco`."],"date":"2024-06-01","doi":"10.1007/s10639-024-12782-0","featured":true,"id":"2024-kim-unlocking","markdown_url":"https://www.gyunam.com/publication/2024-kim-unlocking/index.md","number":"","pages":"1–41","pdf":"https://www.gyunam.com/publication/2024-kim-unlocking/paper.pdf","projects":[],"publisher":"Springer","research_lines":["process-discovery-and-diagnostics"],"series":"","summary":"","tags":["Education"],"title":"Unlocking learner engagement and performance - A multidimensional approach to mapping learners to learning cohorts","type":"article-journal","url":"https://www.gyunam.com/publication/2024-kim-unlocking/","venue":"Education and Information Technologies","venue_short":"","volume":"","year":2024},{"abstract":"","agent_summary":"","authors":["Jan Niklas Adams","Gyunam Park","Wil M. P. van der Aalst"],"bibtex":"@inproceedings{ecis2024,\n _editor = {Javier Troya and\nBrahim Medjahed and\nMario Piattini and\nLina Yao and\nPablo Fernández and\nAntonio Ruiz-Cortés},\n _pages = {36--53},\n _publisher = {Springer},\n _series = {Lecture Notes in Computer Science},\n _volume = {13740},\n author = {Jan Niklas Adams and\nGyunam Park and\nWil M. P. van der Aalst},\n booktitle = {The 32nd European Conference on Information Systems, ECIS\n2024, Paphos, Cyprus, 13-19 June, 2024, Proceedings},\n note = {to appear},\n title = {Improving Predictive Process Monitoring Using Object-Centric Process Mining},\n year = {2024}\n}","code":"","contributions":[],"date":"2024-06-01","doi":"","featured":false,"id":"2024-ECIS","markdown_url":"https://www.gyunam.com/publication/2024-ecis/index.md","number":"","pages":"","pdf":"","projects":[],"publisher":"","research_lines":["object-centric-process-mining","prescriptive-process-analytics"],"series":"","summary":"","tags":[],"title":"Improving Predictive Process Monitoring Using Object-Centric Process Mining","type":"paper-conference","url":"https://www.gyunam.com/publication/2024-ecis/","venue":"The 32nd European Conference on Information Systems, ECIS2024, Paphos, Cyprus, 13-19 June, 2024, Proceedings","venue_short":"","volume":"","year":2024},{"abstract":"This paper addresses overstock issues in a real-life Purchase-to-Pay process in cooperation with the industry leader in pet retail in Europe. It highlights the development of solutions for more efficient inventory management, thereby reducing overstock. Our approach involves identifying patterns leading to overstock and proposing specific improvement measures within the existing logistics systems. This includes technical modifications in the order suggestion and purchase order processes using Logomate and SAP systems. The research utilizes object-centric process mining techniques as a crucial tool to uncover these patterns, with a focus on the practical solutions derived for overstock reduction. The case study conducted with the PM2 methodology demonstrates potential benefits in optimizing inventory structure and suggests a path for future research in generalizing these findings across various sectors and automating overstock pattern detection.","agent_summary":"","authors":["Dina Kretzschmann","Gyunam Park","Alessandro Berti","Wil M. P. van der Aalst"],"bibtex":"@inproceedings{DBLP:conf/caise/KretzschmannPBA24,\n author = {Dina Kretzschmann and\nGyunam Park and\nAlessandro Berti and\nWil M. P. van der Aalst},\n bibsource = {dblp computer science bibliography, https://dblp.org},\n biburl = {https://dblp.org/rec/conf/caise/KretzschmannPBA24.bib},\n booktitle = {Advanced Information Systems Engineering Workshops - CAiSE 2024 International\nWorkshops, Limassol, Cyprus, June 3-7, 2024, Proceedings},\n doi = {10.1007/978-3-031-61003-5_29},\n editor = {João Paulo A. Almeida and\nClaudio Di Ciccio and\nChristos Kalloniatis},\n pages = {347--359},\n publisher = {Springer},\n series = {Lecture Notes in Business Information Processing},\n timestamp = {Fri, 14 Jun 2024 14:34:22 +0200},\n title = {Overstock Problems in a Purchase-to-Pay Process: An Object-Centric\nProcess Mining Case Study},\n url = {https://doi.org/10.1007/978-3-031-61003-5_29},\n volume = {521},\n year = {2024}\n}","code":"","contributions":["A real-life object-centric process mining case study at Europe's largest pet retailer, following the PM2 project methodology adapted to the retailer's setting.","A conceptual model of the purchase-to-pay process with five object types and their object-to-object and event-to-object cardinalities, covering both the order suggestion and purchase order sub-processes.","An extraction and transformation pipeline from `Celonis` (fed by Logomate and SAP) into an object-centric event log in the OCEL standard format.","Two object-centric Petri nets, discovered and analyzed with the `ocpa` Python library, giving a holistic view of ordering and a material-focused view of receipt behavior.","A catalogue of assessed behavior patterns in both sub-processes and two concrete improvement measures agreed with logistics management for the Logomate and SAP systems."],"date":"2024-01-01","doi":"10.1007/978-3-031-61003-5_29","featured":false,"id":"2024-CAiSE-W","markdown_url":"https://www.gyunam.com/publication/2024-caise-w/index.md","number":"","pages":"347–359","pdf":"https://www.gyunam.com/publication/2024-caise-w/paper.pdf","projects":[],"publisher":"Springer","research_lines":["object-centric-process-mining"],"series":"LNBIP","summary":"","tags":[],"title":"Overstock Problems in a Purchase-to-Pay Process: An Object-CentricProcess Mining Case Study","type":"paper-conference","url":"https://www.gyunam.com/publication/2024-caise-w/","venue":"Advanced Information Systems Engineering Workshops - CAiSE 2024 InternationalWorkshops, Limassol, Cyprus, June 3-7, 2024, Proceedings","venue_short":"","volume":"521","year":2024},{"abstract":"Process mining is a relatively new discipline that focuses on gaining process-centric knowledge from event logs collected by enterprise systems. From an academic standpoint, there has been a constant effort to develop various techniques to automatically discover process models, analyze the compliance of real-life processes to the process models, predict operational frictions, and recommend possible actions to mitigate emerging risks. As far as applications are concerned, process mining techniques have been adopted in various industries, such as healthcare, manufacturing, logistics, and finance. In this work, we analyze the process mining literature in-depth using text mining and machine learning techniques. More in detail, we (1) analyze the main research fields in process mining and their trends, (2) investigate the relationship between the fields, and (3) predict the expansion of the fields in the near future. To that end, we analyze 2,677 process mining articles from 2003 to 2022 using a range of techniques such as topic modeling with the pre-trained language model, exploratory bibliometric analysis, network and community detection, and future prediction.","agent_summary":"","authors":["Gyunam Park","Minsu Cho","Jiyoon Lee"],"bibtex":"@article{PARK2024122435,\n abstract = {Process mining is a relatively new discipline that focuses on gaining process-centric knowledge from event logs collected by enterprise systems. From an academic standpoint, there has been a constant effort to develop various techniques to automatically discover process models, analyze the compliance of real-life processes to the process models, predict operational frictions, and recommend possible actions to mitigate emerging risks. As far as applications are concerned, process mining techniques have been adopted in various industries, such as healthcare, manufacturing, logistics, and finance. In this work, we analyze the process mining literature in-depth using text mining and machine learning techniques. More in detail, we (1) analyze the main research fields in process mining and their trends, (2) investigate the relationship between the fields, and (3) predict the expansion of the fields in the near future. To that end, we analyze 2,677 process mining articles from 2003 to 2022 using a range of techniques such as topic modeling with the pre-trained language model, exploratory bibliometric analysis, network and community detection, and future prediction.},\n author = {Gyunam Park and Minsu Cho and Jiyoon Lee},\n doi = {https://doi.org/10.1016/j.eswa.2023.122435},\n issn = {0957-4174},\n journal = {Expert Systems with Applications},\n keywords = {Process mining, Text mining, Network analysis, Literature reviews, Emerging trends, Predictions},\n pages = {122435},\n title = {Leveraging machine learning for automatic topic discovery and forecasting of process mining research: A literature review},\n url = {https://www.sciencedirect.com/science/article/pii/S0957417423029378},\n volume = {239},\n year = {2024}\n}","code":"","contributions":["A five-step quantitative literature-review method (data preparation, research-field identification, field investigation, relation identification, future prediction) built on text mining and machine learning.","A corpus of 2,207 process mining articles and proceedings (2002 to 2021) from Web of Science, topic-modeled with BERTopic into 32 research fields grouped into technique, application and support categories.","A keyword-similarity network of research fields (YAKE! keyword extraction, Jaccard similarity) with Louvain community detection to expose relationships between topics.","A Long Short-Term Memory (LSTM) forecasting model fed with a publication representation matrix that combines historical counts and inter-field similarities to predict publications per field.","A comparison of the identified fields with workshop topics at the leading process mining conferences and with the functionalities of tools such as Celonis, UiPath, `ProM` and Disco."],"date":"2024-01-01","doi":"10.1016/j.eswa.2023.122435","featured":false,"id":"2024-ESWA","markdown_url":"https://www.gyunam.com/publication/2024-eswa/index.md","number":"","pages":"122435","pdf":"https://www.gyunam.com/publication/2024-eswa/paper.pdf","projects":[],"publisher":"Elsevier","research_lines":[],"series":"","summary":"","tags":["Process mining","Text mining","Network analysis","Literature reviews","Emerging trends","Predictions"],"title":"Leveraging machine learning for automatic topic discovery and forecasting of process mining research: A literature review","type":"article-journal","url":"https://www.gyunam.com/publication/2024-eswa/","venue":"Expert Systems with Applications","venue_short":"","volume":"239","year":2024},{"abstract":"AI planning plays a crucial role in the design and optimization of business processes, providing optimal plans, i.e., sequence of activities, based on manually crafted or formally documented rules. When these plans are executed in business processes, the supporting information systems record a wealth of event data. Analyzing such event data facilitates understanding implicit patterns and recommendations that have the potential to refine planning strategies significantly. In this paper, we introduce a systematic approach to mining these recommendations from event data and integrating them into AI planning, thus creating plans that are informed by both the regulatory hard rules and the flexibility of soft recommendations.","agent_summary":"","authors":["Gyunam Park","Majid Rafiei","Hayyan Helal","Gerhard Lakemeyer","Wil M. P. van der Aalst"],"bibtex":"@inproceedings{caise2024,\n _editor = {Javier Troya and\nBrahim Medjahed and\nMario Piattini and\nLina Yao and\nPablo Fernández and\nAntonio Ruiz-Cortés},\n _volume = {13740},\n author = {Gyunam Park and\nMajid Rafiei and Hayyan Helal and Gerhard Lakemeyer and\nWil M. P. van der Aalst},\n booktitle = {36th International Conference on Advanced Information Systems Engineering Forum, CAiSE Forum\n2024, Limassol, Cyprus, 03-07 June, 2024, Proceedings},\n pages = {20--28},\n publisher = {Springer},\n series = {Lecture Notes in Business Information Processing},\n title = {Incorporating Behavioral Recommendations Mined from Event Logs into AI Planning},\n year = {2024}\n}","code":"","contributions":["A two-phase approach that mines behavioral recommendations from event data and feeds them into AI planning as soft constraints alongside hard regulatory rules.","A mining procedure that instantiates Declare temporal pattern templates (precedence, response, exclusive choice), checks each case with Linear Temporal Logic (LTL), and keeps only patterns whose satisfied and violated cases differ significantly on an outcome score.","Formalizes recommendation-based planning problems with a weight function that sums the weights of recommended temporal patterns holding in a plan, defining optimal plans as valid plans of maximal weight.","Shows that recommendation-based planning with each action occurring at most once can be solved as an integer linear programming optimization over plan membership and ordering relations.","A running example from curriculum design in which course prerequisites act as hard rules and mined course-ordering patterns act as soft rules."],"date":"2024-01-01","doi":"10.1007/978-3-031-61000-4_3","featured":false,"id":"2024-CAiSE-F","markdown_url":"https://www.gyunam.com/publication/2024-caise-f/index.md","number":"","pages":"20–28","pdf":"https://www.gyunam.com/publication/2024-caise-f/paper.pdf","projects":[],"publisher":"Springer","research_lines":["neuro-symbolic-process-learning","prescriptive-process-analytics"],"series":"LNBIP","summary":"","tags":[],"title":"Incorporating Behavioral Recommendations Mined from Event Logs into AI Planning","type":"paper-conference","url":"https://www.gyunam.com/publication/2024-caise-f/","venue":"36th International Conference on Advanced Information Systems Engineering Forum, CAiSE Forum2024, Limassol, Cyprus, 03-07 June, 2024, Proceedings","venue_short":"","volume":"520","year":2024},{"abstract":"Objectives. This study aims to enhance the analysis of healthcare processes by introducing Object-Centric Process Mining (OCPM). By offering a holistic perspective that accounts for the interactions among various objects, OCPM transcends the constraints of conventional patient-centric process mining approaches, ensuring a more detailed and inclusive understanding of healthcare dynamics. Methods. We develop a novel method to transform the Observational Medical Outcomes Partnership Common Data Models (OMOP CDM) into Object-Centric Event Logs (OCELs). First, an OMOP CDM4PM is created from the standard OMOP CDM, focusing on data relevant to generating OCEL and addressing healthcare data’s heterogeneity and standardization challenges. Second, this subset is transformed into OCEL based on specified healthcare criteria, including identifying various object types, clinical activities, and their relationships. The methodology is tested on the MIMIC-IV database to evaluate its effectiveness and utility. Results. The application of our proposed method to the MIMIC-IV dataset effectively produces OCELs, allowing for the implementation of OCPM in the healthcare industry. We rigorously evaluate the comprehensiveness and level of abstraction to validate our approach’s effectiveness. Additionally, we create diverse object-centric process models intricately designed to navigate the complexities inherent in healthcare processes. Conclusion. Our approach introduces a novel perspective by integrating multiple viewpoints simultaneously. To the best of our knowledge, this is the inaugural application of OCPM within the healthcare sector, marking a significant advancement in the field.","agent_summary":"","authors":["Gyunam Park","Yaejin Lee","Minsu Cho"],"bibtex":"@article{park2024enhancing,\n  title     = {Enhancing healthcare process analysis through object-centric process mining: Transforming OMOP common data models into object-centric event logs},\n  author    = {Gyunam Park and Lee, Yaejin and Cho, Minsu},\n  journal   = {Journal of Biomedical Informatics},\n  pages     = {104682},\n  year      = {2024},\n  publisher = {Elsevier}\n}","code":"","contributions":["OMOP CDM4PM, a formally defined subset of the OMOP CDM that organizes event tables, object tables, event-object relationships and concept relationships for process mining.","A formal transformation from OMOP CDM4PM to an object-centric event log (OCEL) built from identifier, timestamp, event type and object type assignment functions.","A three-level event type assignment (domain, concept class, concept name) and a two-level object type assignment derived from the OMOP concept table and the SNOMED CT hierarchy.","An evaluation protocol that applies the Kahn et al. data quality framework (completeness, conformance) together with abstraction and utility analyses on three MIMIC-IV sub-datasets.","An open-source implementation and manual, `omop2ocel`, released on GitHub (gyunamister/omop2ocel)."],"date":"2024-01-01","doi":"","featured":false,"id":"2024-JBI-OMOP","markdown_url":"https://www.gyunam.com/publication/2024-jbi-omop/index.md","number":"","pages":"104682","pdf":"https://www.gyunam.com/publication/2024-jbi-omop/paper.pdf","projects":[],"publisher":"Elsevier","research_lines":["object-centric-process-mining"],"series":"","summary":"","tags":[],"title":"Enhancing healthcare process analysis through object-centric process mining: Transforming OMOP common data models into object-centric event logs","type":"article-journal","url":"https://www.gyunam.com/publication/2024-jbi-omop/","venue":"Journal of Biomedical Informatics","venue_short":"","volume":"","year":2024},{"abstract":"The purchase-to-pay (P2P) process is one of the core business processes in any organization. It ensures the correct and efficient provisioning of materials and services. An efficient P2P process reduces operational costs by ensuring discounts, avoiding late payments, and choosing the optimal supplier for the goods. Process mining techniques help practitioners optimize the execution of P2P processes by analyzing the execution data and providing useful insights. However, existing techniques may result in misleading insights due to many-to-many relationships between business objects, e.g., between orders and invoices in the P2P process. Recently, object-centric process mining techniques have been proposed to avoid the limitations of traditional process mining techniques. In this paper, we present a case study on a real-life P2P process using object-centric process mining techniques. To that end, we adopt the well-known PM $$^{2}$$ 2 process mining project methodology to the object-centric setting and analyze the performance and compliance.","agent_summary":"","authors":["Alessandro Berti","Urszula Jessen","Gyunam Park","Majid Rafiei","Wil M. P. van der Aalst"],"bibtex":"@article{p2pcasestudy,\n  author    = {Alessandro Berti and Urszula Jessen and Gyunam Park and Majid Rafiei and Wil M. P. van der Aalst},\n  title     = {Analyzing interconnected processes: using object-centric process mining to analyze procurement processes},\n  journal   = {Int. J. Data Sci. Anal.},\n  volume    = {20},\n  number    = {2},\n  pages     = {475--497},\n  year      = {2025},\n  url       = {https://doi.org/10.1007/s41060-023-00427-3},\n  doi       = {10.1007/S41060-023-00427-3}\n}","code":"","contributions":[],"date":"2023-07-16","doi":"10.1007/s41060-023-00427-3","featured":false,"id":"2025-IJDSA-P2P","markdown_url":"https://www.gyunam.com/publication/2025-ijdsa-p2p/index.md","number":"2","pages":"475–497","pdf":"","projects":[],"publisher":"Springer","research_lines":["object-centric-process-mining"],"series":"","summary":"","tags":[],"title":"Analyzing interconnected processes: using object-centric process mining to analyze procurement processes","type":"article-journal","url":"https://www.gyunam.com/publication/2025-ijdsa-p2p/","venue":"International Journal of Data Science and Analytics","venue_short":"","volume":"20","year":2023},{"abstract":"Interactions of multiple processes and different objects can be captured using object-centric event data. Object-centric event data represent process executions as event graphs of interacting objects. When applying machine learning techniques to object-centric event data, the event log has to be flattened into sequential process executions used as input for traditional process mining approaches. However, sequentializing the events by flattening removes the graph structure of object-centric event data and, therefore, constitutes an information loss. In this paper, we present a general approach to preserve the graph structures of object-centric event data across machine learning tasks in process mining. We provide two different techniques to preserve these structures depending on the required input format of machine learning techniques: as direct graph encodings or as graph embeddings. We evaluate our contributions by applying three different predictive process monitoring tasks to direct graph encodings, graph embeddings, and flattened event logs. Based on the relative performances, we assess the information contained in the graph structure of object-centric event logs that is, currently, lost in machine learning approaches for traditional process mining. Furthermore, we compare different graph embedding techniques for object-centric event logs to derive recommendations for future research and deployment. To conclude, we assess the improvement that graph embeddings constitute over state-of-the-art approaches that capture object information through specific features. Our contributions improve predictive process monitoring on object-centric event data and quantify the potential performance increases of predictive models. These contributions are especially relevant when looking at real-life applications of predictive process monitoring which are often applied to information systems with relational databases that contain multiple objects and, currently, still enforce flattening.","agent_summary":"","authors":["Jan Niklas Adams","Gyunam Park","Wil M. P. van der Aalst"],"bibtex":"@article{ADAMS2023106764,\n abstract = {Interactions of multiple processes and different objects can be captured using object-centric event data. Object-centric event data represent process executions as event graphs of interacting objects. When applying machine learning techniques to object-centric event data, the event log has to be flattened into sequential process executions used as input for traditional process mining approaches. However, sequentializing the events by flattening removes the graph structure of object-centric event data and, therefore, constitutes an information loss. In this paper, we present a general approach to preserve the graph structures of object-centric event data across machine learning tasks in process mining. We provide two different techniques to preserve these structures depending on the required input format of machine learning techniques: as direct graph encodings or as graph embeddings. We evaluate our contributions by applying three different predictive process monitoring tasks to direct graph encodings, graph embeddings, and flattened event logs. Based on the relative performances, we assess the information contained in the graph structure of object-centric event logs that is, currently, lost in machine learning approaches for traditional process mining. Furthermore, we compare different graph embedding techniques for object-centric event logs to derive recommendations for future research and deployment. To conclude, we assess the improvement that graph embeddings constitute over state-of-the-art approaches that capture object information through specific features. Our contributions improve predictive process monitoring on object-centric event data and quantify the potential performance increases of predictive models. These contributions are especially relevant when looking at real-life applications of predictive process monitoring which are often applied to information systems with relational databases that contain multiple objects and, currently, still enforce flattening.},\n author = {Jan Niklas Adams and Gyunam Park and Wil M.P. van der Aalst},\n doi = {https://doi.org/10.1016/j.engappai.2023.106764},\n issn = {0952-1976},\n journal = {Engineering Applications of Artificial Intelligence},\n keywords = {Object-centric process mining, Machine learning, Predictive process monitoring},\n pages = {106764},\n title = {Preserving complex object-centric graph structures to improve machine learning tasks in process mining},\n url = {https://www.sciencedirect.com/science/article/pii/S095219762300948X},\n volume = {125},\n year = {2023}\n}","code":"","contributions":["A general approach that keeps object-centric process executions as event graphs throughout the machine-learning pipeline, either as direct graph input or as graph embeddings for vector-based models.","Formalizes process executions as event graphs over connected components of the object graph, and flattening as a baseline transformation, with worked examples of three kinds of structural information loss.","An end-to-end predictive pipeline (subgraph extraction, leakage-free train/test split, graph neural network or embedding plus regression/MLP) covering next activity, next timestamp and remaining time.","An evaluation protocol that isolates the value of structure alone (no event features) and a benchmark that measures the added value of embeddings over state-of-the-art object-centric features.","An implementation on top of the `ocpa` Python library with experiments released on GitHub (niklasadams/PreservingOCStructures)."],"date":"2023-07-15","doi":"10.1016/j.engappai.2023.106764","featured":true,"id":"2023-EAAI","markdown_url":"https://www.gyunam.com/publication/2023-eaai/index.md","number":"","pages":"106764","pdf":"https://www.gyunam.com/publication/2023-eaai/paper.pdf","projects":[],"publisher":"Elsevier","research_lines":["object-centric-process-mining","prescriptive-process-analytics"],"series":"","summary":"","tags":["Object-centric process mining","Machine learning","Predictive process monitoring"],"title":"Preserving Complex Object-Centric Graph Structures to Improve Machine Learning Tasks in Process Mining","type":"article-journal","url":"https://www.gyunam.com/publication/2023-eaai/","venue":"Engineering Applications of Artificial Intelligence","venue_short":"","volume":"125","year":2023},{"abstract":"Process mining provides a collection of techniques to gain insights into business processes by analyzing event logs. Organizations can gain various insights into their business processes by using process mining techniques. Such techniques use event logs extracted from relational databases supporting the business process as input. However, extracting event logs is challenging due to the size of the data, and it remains ad-hoc. Existing commercial tools partly support the extraction of event logs, but they are proprietary and focus on the mainstream processes such as Purchase-To-Pay (P2P) and Order-To-Cash (O2C). Moreover, the extracted event logs suffer from well-known deficiency, convergence, and divergence issues. For example, due to convergence events are unintentionally duplicated causing unreliable or confusing performance diagnostics. In this paper, we propose an approach to extract event logs while avoiding the aforementioned issues. More in detail, we extract object-centric event logs by using an abstraction layer of the database, called Graph of Relationships (GoRs), designing blueprints with domain knowledge, and converting the database and blueprint into object-centric event logs.We fully implemented the proposed approach, which can extract object-centric event logs from SAP ERP systems, and evaluate the utility and scalability of the proposed approach.","agent_summary":"","authors":["Alessandro Berti","Gyunam Park","Majid Rafiei","Wil MP van der Aalst"],"bibtex":"@article{berti2023generic,\n author = {Berti, Alessandro and Park, Gyunam and Rafiei, Majid and van der Aalst, Wil MP},\n journal = {Journal of Intelligent Information Systems},\n pages = {1--23},\n publisher = {Springer},\n title = {A generic approach to extract object-centric event data from databases supporting SAP ERP},\n year = {2023}\n}","code":"","contributions":["A technology-independent database abstraction (tables, entries, object types, transactions, keys, object relationships) and a derived Graph of Relationships (GoR) that supports process identification and table selection.","The concept of blueprints that map table entries to primitive events, with a basic blueprint needing no domain knowledge and a domain-knowledge blueprint grouping related entries into one event.","A formal conversion from database plus blueprint to an object-centric event log in the OCEL standard, including a projection to a chosen subset of tables.","A concrete realization for SAP ERP: the SQL queries that build the GoR from SAP dictionary tables and a classification of SAP tables into change, transaction and object tables.","The open-source Interactive SAP Explorer tool (Neo4J graph database, Angular front end, Python extractor on GitHub) plus two extracted O2C and P2P logs published in JSON-OCEL."],"date":"2023-07-13","doi":"10.1007/s10844-023-00799-9","featured":false,"id":"2023-JIIS-Generic-SAP-Extraction","markdown_url":"https://www.gyunam.com/publication/2023-jiis-generic-sap-extraction/index.md","number":"3","pages":"835–857","pdf":"https://www.gyunam.com/publication/2023-jiis-generic-sap-extraction/paper.pdf","projects":[],"publisher":"Springer","research_lines":["object-centric-process-mining"],"series":"","summary":"","tags":[],"title":"A Generic Approach to Extract Object-Centric Event Data from Databases Supporting SAP ERP","type":"article-journal","url":"https://www.gyunam.com/publication/2023-jiis-generic-sap-extraction/","venue":"Journal of Intelligent Information Systems","venue_short":"","volume":"61","year":2023},{"abstract":"Recent breakthroughs in predictive business process monitoring equip process analysts with predictions on running process instances, supporting the elicitation of proactive measures to mitigate risks that can be caused by the process instance. However, contrary to active research on providing various predictions and improving the accuracy of prediction models, the practical application of such predictions has been left to the subjective judgment of domain experts. In this work, we explore the exploitation of the insights from predictive information for the actual process improvement in practice. Concretely, we focus on improving resource allocation in business processes where the goal is to allocate appropriate resources to tasks at the proper time. Based on design science methodology, we develop a two-phase method to improve resource allocation by leveraging predictions. Based on the method, we instantiate an algorithm to optimize total-weighted completion time and evaluate its effectiveness and efficiency. From an academic standpoint, our work demonstrates the combination of predictions using machine learning and optimizations based on scheduling. From a practical standpoint, our work provides a general approach to optimize resource allocations for different objectives using predictions.","agent_summary":"","authors":["Gyunam Park","Minseok Song"],"bibtex":"@article{DBLP:journals/access/ParkS23,\n author = {Gyunam Park and\nMinseok Song},\n bibsource = {dblp computer science bibliography, https://dblp.org},\n biburl = {https://dblp.org/rec/journals/access/ParkS23.bib},\n doi = {10.1109/ACCESS.2023.3267538},\n journal = {IEEE Access},\n pages = {38309--38323},\n timestamp = {Wed, 17 May 2023 21:55:55 +0200},\n title = {Optimizing Resource Allocation Based on Predictive Process Monitoring},\n url = {https://doi.org/10.1109/ACCESS.2023.3267538},\n volume = {11},\n year = {2023}\n}","code":"","contributions":["A two-phase method, formalized through scheduling instances, work assignments, schedules and objective functions, that first trains prediction models offline and then predicts missing parameters online before optimizing the allocation.","An uncertainty-aware prediction step based on Bayesian neural networks with Monte Carlo dropout and LSTM layers, which discards predictions whose uncertainty exceeds a threshold.","An instantiation for minimizing total weighted completion time of weight-aware non-clairvoyant instances that casts allocation as a minimum cost maximum flow problem solved by the network simplex method.","An evaluation protocol comparing against a maximum-weight baseline on a simulated emergency department and a real tertiary-hospital emergency log, including a controlled study of prediction accuracy.","A public Python implementation on GitHub (`prediction_based_resource_allocation`)."],"date":"2023-06-01","doi":"10.1109/ACCESS.2023.3267538","featured":true,"id":"2023-ACCESS","markdown_url":"https://www.gyunam.com/publication/2023-access/index.md","number":"","pages":"38309–38323","pdf":"https://www.gyunam.com/publication/2023-access/paper.pdf","projects":[],"publisher":"IEEE","research_lines":["prescriptive-process-analytics"],"series":"","summary":"","tags":[],"title":"Optimizing Resource Allocation Based on Predictive Process Monitoring","type":"article-journal","url":"https://www.gyunam.com/publication/2023-access/","venue":"IEEE Access","venue_short":"","volume":"11","year":2023},{"abstract":"Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, most of the state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. Moreover, most of these methods require a hyper-parameter optimization that requires several repetitions of the training process which is not feasible in many real-life applications. In this paper, we propose an instance selection procedure that allows sampling training process instances for prediction models. We show that our instance selection procedure allows for a significant increase of training speed for next activity and remaining time prediction methods while maintaining reliable levels of prediction accuracy.","agent_summary":"","authors":["Mohammadreza Fani Sani","Mozhgan Vazifehdoostirani","Gyunam Park","Marco Pegoraro","Sebastiaan J van Zelst","Wil MP van der Aalst"],"bibtex":"@article{fani2023performance,\n author = {Fani Sani, Mohammadreza and Vazifehdoostirani, Mozhgan and Park, Gyunam and Pegoraro, Marco and van Zelst, Sebastiaan J and van der Aalst, Wil MP},\n journal = {Journal of Intelligent Information Systems},\n pages = {1--30},\n publisher = {Springer},\n title = {Performance-preserving event log sampling for predictive monitoring},\n year = {2023}\n}","code":"","contributions":["A four-step instance selection procedure for event logs: traverse variants, compute attribute distributions per variant, sort the traces of each variant, and return a sample of the training log only.","Three selection strategies, unique, logarithmic (log base k) and division (ceiling of frequency over k), with a formal notion of variant-preserving sampling.","Relative evaluation metrics for size reduction, accuracy, F1-score, MAE (mean absolute error), RMSE (root mean squared error), feature extraction time and training time against the full-log baseline.","An implementation on both Java and Python platforms, with code and feature generation published at `github.com/gyunamister/pm-prediction`.","An evaluation protocol on three real-life logs (BPIC2012-W, Road Traffic Fine Management, Sepsis) with LSTM and XGBoost, 5-fold cross-validation repeated 5 times on one CPU thread."],"date":"2023-03-06","doi":"10.1007/s10844-022-00775-9","featured":false,"id":"2023-sampling-JIIS","markdown_url":"https://www.gyunam.com/publication/2023-sampling-jiis/index.md","number":"1","pages":"53–82","pdf":"https://www.gyunam.com/publication/2023-sampling-jiis/paper.pdf","projects":[],"publisher":"Springer","research_lines":["prescriptive-process-analytics"],"series":"","summary":"","tags":[],"title":"Performance-preserving event log sampling for predictive monitoring","type":"article-journal","url":"https://www.gyunam.com/publication/2023-sampling-jiis/","venue":"Journal of Intelligent Information Systems","venue_short":"","volume":"61","year":2023},{"abstract":"Constraint monitoring aims to monitor the violation of constraints in business processes, e.g., an invoice should be cleared within 48 h after the corresponding goods receipt, by analyzing event data. Existing techniques for constraint monitoring assume that a single case notion exists in a business process, e.g., a patient in a healthcare process, and each event is associated with the case notion. However, in reality, business processes are object-centric , i.e., multiple case notions (objects) exist, and an event may be associated with multiple objects. For instance, an Order-To-Cash (O2C) process involves order , item , delivery , etc., and they interact when executing an event, e.g., packing multiple items together for a delivery. The existing techniques produce misleading insights when applied to such object-centric business processes. In this work, we propose an approach to monitoring constraints in object-centric business processes. To this end, we introduce Object-Centric Constraint Graphs (OCCGs) to represent constraints that consider the interaction of objects. Next, we evaluate the constraints represented by OCCGs by analyzing Object-Centric Event Logs (OCELs) that store the interaction of different objects in events. We have implemented a web application to support the proposed approach and conducted two case studies using a real-life SAP ERP system.","agent_summary":"","authors":["Gyunam Park","Wil M. P. van der Aalst"],"bibtex":"@inproceedings{DBLP:conf/icpm/ParkA22,\n author = {Gyunam Park and\nWil M. P. van der Aalst},\n bibsource = {dblp computer science bibliography, https://dblp.org},\n biburl = {https://dblp.org/rec/conf/icpm/ParkA22.bib},\n booktitle = {Process Mining Workshops - ICPM 2022 International Workshops, Bozen-Bolzano,\nItaly, October 23-28, 2022, Revised Selected Papers},\n doi = {10.1007/978-3-031-27815-0_35},\n editor = {Marco Montali and\nArik Senderovich and\nMatthias Weidlich},\n pages = {479--492},\n publisher = {Springer},\n series = {Lecture Notes in Business Information Processing},\n timestamp = {Fri, 26 May 2023 07:40:33 +0200},\n title = {Monitoring Constraints in Business Processes Using Object-Centric\nConstraint Graphs},\n url = {https://doi.org/10.1007/978-3-031-27815-0_35},\n volume = {468},\n year = {2022}\n}","code":"","contributions":["Object-centric behavioral metrics over Object-Centric Event Logs (OCELs): causal, concurrent and choice ordering relations per object type, object involvement metrics (absent, singular, multiple) and underspecified performance metrics.","Object-Centric Constraint Graphs (OCCGs), a directed graph notation with activity, object type and formula nodes and control-flow, object involvement and performance edges with thresholds, plus formal violation semantics.","A monitoring engine that computes the metrics from an OCEL, reusing the OPerA object-centric performance analysis for sojourn, waiting, synchronization, pooling and lagging times, and evaluates OCCG violations.","A web application (`ProPPa`) for importing OCELs in JSON, XML and CSV, designing OCCGs graphically and visualizing monitoring results, with demo video, manual and event logs on GitHub.","Two case studies on a production process and a purchase-to-pay process of an SAP ERP (enterprise resource planning) system."],"date":"2023-03-01","doi":"10.1007/978-3-031-27815-0_35","featured":false,"id":"2022-PQMI","markdown_url":"https://www.gyunam.com/publication/2022-pqmi/index.md","number":"","pages":"479–492","pdf":"https://www.gyunam.com/publication/2022-pqmi/paper.pdf","projects":[],"publisher":"Springer","research_lines":["object-centric-process-mining"],"series":"LNBIP","summary":"","tags":[],"title":"Monitoring Constraints in Business Processes Using Object-Centric Constraint Graphs","type":"paper-conference","url":"https://www.gyunam.com/publication/2022-pqmi/","venue":"Process Mining Workshops - ICPM 2022 International Workshops, Bozen-Bolzano, Italy, October 23-28, 2022, Revised Selected Papers","venue_short":"","volume":"468","year":2023},{"abstract":"SAP ERP is one of the most popular information systems supporting various organizational processes, e.g., O2C and P2P. However, the amount of processes and data contained in SAP ERP is enormous. Thus, the identification of the processes that are contained in a specific SAP instance, and the creation of a list of related tables is a significant challenge. Eventually, one needs to extract an event log for process mining purposes from SAP ERP. This demo paper shows the tool Interactive SAP Explorer that tackles the process identification and selection problem by encoding the relational structure of SAP ERP in a labeled property graph. Our approach allows asking complex process-related queries along with advanced representations of the relational structure.","agent_summary":"","authors":["Julian Weber","Gyunam Park","Majid Rafiei","Wil M. P. van der Aalst"],"bibtex":"@inproceedings{DBLP:conf/icpm/WeberPRA22,\n author = {Julian Weber and\nGyunam Park and\nMajid Rafiei and\nWil M. P. van der Aalst},\n bibsource = {dblp computer science bibliography, https://dblp.org},\n biburl = {https://dblp.org/rec/conf/icpm/WeberPRA22.bib},\n booktitle = {Proceedings of the ICPM Doctoral Consortium and Demo Track 2022\nco-located with 4th International Conference on Process Mining (ICPM\n2022), Bolzano, Italy, October, 2022},\n editor = {Marwan Hassani and\nAgnes Koschmider and\nMarco Comuzzi and\nFabrizio Maria Maggi and\nLuise Pufahl},\n pages = {61--64},\n publisher = {CEUR-WS.org},\n series = {CEUR Workshop Proceedings},\n timestamp = {Fri, 10 Mar 2023 16:22:44 +0100},\n title = {Interactive Process Identification and Selection from SAP ERP\n(Extended Abstract)},\n url = {https://ceur-ws.org/Vol-3299/Paper13.pdf},\n volume = {3299},\n year = {2022}\n}","code":"","contributions":[],"date":"2023-03-01","doi":"","featured":false,"id":"2022-ICPM-Demo","markdown_url":"https://www.gyunam.com/publication/2022-icpm-demo/index.md","number":"","pages":"61–64","pdf":"https://www.gyunam.com/publication/2022-icpm-demo/paper.pdf","projects":[],"publisher":"CEUR-WS.org","research_lines":["object-centric-process-mining"],"series":"CEUR-WS","summary":"","tags":[],"title":"Interactive Process Identification and Selection from SAP ERP","type":"paper-conference","url":"https://www.gyunam.com/publication/2022-icpm-demo/","venue":"Proceedings of the ICPM Doctoral Consortium and Demo Track 2022 co-located with 4th International Conference on Process Mining (ICPM 2022), Bolzano, Italy, October, 2022","venue_short":"","volume":"3299","year":2023},{"abstract":"As business environments become more competitive, organizations strive to improve their business processes to reduce costs and increase quality and productivity. As process improvement traditionally embraces manual creative tasks that are time-consuming and labor-intensive, the need for automating it arises. Action-Oriented Process Mining (AOPM) aims to support automated process improvement by leveraging various process mining techniques. To that end, AOPM first monitors the presence of operational constraints, i.e., operational problems, in business processes, e.g., a high waiting time for patients to register. Next, it produces interim management actions designed to address these transient problems by analyzing the monitoring results. For instance, if an excessive waiting time persists for more than a week, the system might recommend dispatching additional resources for the upcoming week. Contrary to the mature process mining support for monitoring operational constraints, the action part is typically missing in today’s process mining tools. In this work, we propose an action engine to support the automatic generation of actions. It analyzes temporal patterns of monitoring results and produces action plans that describe the execution of management actions. We have demonstrated a use case using the data of a Dutch financial institute to evaluate the feasibility of the proposed action engine and conducted experiments to evaluate its effectiveness.","agent_summary":"","authors":["Gyunam Park","Daniel Schuster","Wil M. P. van der Aalst"],"bibtex":"@article{DBLP:journals/cii/ParkSA23,\n author = {Gyunam Park and\nDaniel Schuster and\nWil M. P. van der Aalst},\n bibsource = {dblp computer science bibliography, https://dblp.org},\n biburl = {https://dblp.org/rec/journals/cii/ParkSA23.bib},\n doi = {10.1016/J.COMPIND.2023.104020},\n journal = {Comput. Ind.},\n pages = {104020},\n timestamp = {Tue, 28 Nov 2023 20:05:24 +0100},\n title = {Pattern-based action engine: Generating process management actions\nusing temporal patterns of process-centric problems},\n url = {https://doi.org/10.1016/j.compind.2023.104020},\n volume = {153},\n year = {2023}\n}","code":"","contributions":["A pattern-based action engine with three phases: domain experts design action graphs, an action generator derives action requirements from monitoring results, and an action planner schedules conflict-free action plans.","Formalizes constraint instances as interval-based data, temporal pattern trees built on Allen's 13 temporal relations, and action graphs that connect patterns to actions with required durations.","An action conflict model (a directed acyclic graph of precedence constraints) and a makespan-minimizing planner cast as parallel machine scheduling with precedence constraints and unlimited machines.","A use case on the BPI Challenge 2017 loan application process in which frequent temporal patterns are discovered from weekly monitoring results with the process mining tool `Cortado`.","An open-source implementation and experiment scripts on GitHub (aopm/pattern-based-action-engine)."],"date":"2023-01-01","doi":"10.1016/J.COMPIND.2023.104020","featured":false,"id":"2023-COMIND-ActionEngine","markdown_url":"https://www.gyunam.com/publication/2023-comind-actionengine/index.md","number":"","pages":"104020","pdf":"https://www.gyunam.com/publication/2023-comind-actionengine/paper.pdf","projects":[],"publisher":"Elsevier","research_lines":["prescriptive-process-analytics"],"series":"","summary":"","tags":[],"title":"Pattern-based action engine: Generating process management actions using temporal patterns of process-centric problems","type":"article-journal","url":"https://www.gyunam.com/publication/2023-comind-actionengine/","venue":"Comput. Ind.","venue_short":"","volume":"153","year":2023},{"abstract":"","agent_summary":"","authors":["Majid Rafiei","Duygu Bayrak","Mahsa Pourbafrani","Gyunam Park","Hayyan Helal","Gerhard Lakemeyer","Wil M. P. van der Aalst"],"bibtex":"@inproceedings{DBLP:conf/icpm/RafieiBPPHLA23,\n author = {Majid Rafiei and\nDuygu Bayrak and\nMahsa Pourbafrani and\nGyunam Park and\nHayyan Helal and\nGerhard Lakemeyer and\nWil M. P. van der Aalst},\n bibsource = {dblp computer science bibliography, https://dblp.org},\n biburl = {https://dblp.org/rec/conf/icpm/RafieiBPPHLA23.bib},\n booktitle = {Process Mining Workshops - ICPM 2023 International Workshops, Rome,\nItaly, October 23-27, 2023, Revised Selected Papers},\n doi = {10.1007/978-3-031-56107-8_28},\n editor = {Johannes De Smedt and\nPnina Soffer},\n pages = {361--374},\n publisher = {Springer},\n series = {Lecture Notes in Business Information Processing},\n timestamp = {Fri, 17 May 2024 21:42:15 +0200},\n title = {Extracting Rules from Event Data for Study Planning},\n url = {https://doi.org/10.1007/978-3-031-56107-8_28},\n volume = {503},\n year = {2023}\n}","code":"","contributions":[],"date":"2023-01-01","doi":"10.1007/978-3-031-56107-8_28","featured":false,"id":"2023-ICPM-EduPM","markdown_url":"https://www.gyunam.com/publication/2023-icpm-edupm/index.md","number":"","pages":"361–374","pdf":"","projects":[],"publisher":"Springer","research_lines":["neuro-symbolic-process-learning","prescriptive-process-analytics"],"series":"LNBIP","summary":"","tags":[],"title":"Extracting Rules from Event Data for Study Planning","type":"paper-conference","url":"https://www.gyunam.com/publication/2023-icpm-edupm/","venue":"Process Mining Workshops - ICPM 2023 International Workshops, Rome,Italy, October 23-27, 2023, Revised Selected Papers","venue_short":"","volume":"503","year":2023},{"abstract":"","agent_summary":"","authors":["Tian Li","Gyunam Park","Wil M. P. van der Aalst"],"bibtex":"@inproceedings{DBLP:conf/icpm/LiPA23,\n author = {Tian Li and\nGyunam Park and\nWil M. P. van der Aalst},\n bibsource = {dblp computer science bibliography, https://dblp.org},\n biburl = {https://dblp.org/rec/conf/icpm/LiPA23.bib},\n booktitle = {Process Mining Workshops - ICPM 2023 International Workshops, Rome,\nItaly, October 23-27, 2023, Revised Selected Papers},\n doi = {10.1007/978-3-031-56107-8_30},\n editor = {Johannes De Smedt and\nPnina Soffer},\n pages = {392--405},\n publisher = {Springer},\n series = {Lecture Notes in Business Information Processing},\n timestamp = {Mon, 15 Apr 2024 16:46:44 +0200},\n title = {Checking Constraints for Object-Centric Process Executions},\n url = {https://doi.org/10.1007/978-3-031-56107-8_30},\n volume = {503},\n year = {2023}\n}","code":"","contributions":[],"date":"2023-01-01","doi":"10.1007/978-3-031-56107-8_30","featured":false,"id":"2023-ICPM-PQMI","markdown_url":"https://www.gyunam.com/publication/2023-icpm-pqmi/index.md","number":"","pages":"392–405","pdf":"","projects":[],"publisher":"Springer","research_lines":["object-centric-process-mining"],"series":"LNBIP","summary":"","tags":[],"title":"Checking Constraints for Object-Centric Process Executions","type":"paper-conference","url":"https://www.gyunam.com/publication/2023-icpm-pqmi/","venue":"Process Mining Workshops - ICPM 2023 International Workshops, Rome,Italy, October 23-27, 2023, Revised Selected Papers","venue_short":"","volume":"503","year":2023},{"abstract":"Process mining, a technique turning event data into business process insights, has traditionally operated on the assumption that each event corresponds to a singular case or object. However, many real-world processes are intertwined with multiple objects, making them object-centric. This paper focuses on the emerging domain of object-centric process mining, highlighting its potential yet underexplored benefits in actual operational scenarios. Through an in-depth case study of Borusan Cat’s after-sales service process, this study emphasizes the capability of object-centric process mining to capture entangled business process details. Utilizing an event log of approximately 65,000 events, our analysis underscores the importance of embracing this paradigm for richer business insights and enhanced operational improvements.","agent_summary":"","authors":["Gyunam Park","Sevde Aydin","Cüneyt Ugur","Wil M. P. van der Aalst"],"bibtex":"@inproceedings{DBLP:conf/icpm/ParkAUA23,\n author = {Gyunam Park and\nSevde Aydin and\nCüneyt Ugur and\nWil M. P. van der Aalst},\n bibsource = {dblp computer science bibliography, https://dblp.org},\n biburl = {https://dblp.org/rec/conf/icpm/ParkAUA23.bib},\n booktitle = {Process Mining Workshops - ICPM 2023 International Workshops, Rome,\nItaly, October 23-27, 2023, Revised Selected Papers},\n doi = {10.1007/978-3-031-56107-8_31},\n editor = {Johannes De Smedt and\nPnina Soffer},\n pages = {406--418},\n publisher = {Springer},\n series = {Lecture Notes in Business Information Processing},\n timestamp = {Mon, 15 Apr 2024 16:46:44 +0200},\n title = {Analyzing an After-Sales Service Process Using Object-Centric Process\nMining: A Case Study},\n url = {https://doi.org/10.1007/978-3-031-56107-8_31},\n volume = {503},\n year = {2023}\n}","code":"","contributions":["An extension of the PM2 process mining project methodology into five stages for object-centric projects: planning, extraction, data preprocessing, mining and analysis, improvement.","An object-centric event log in the OCEL standard extracted from the Oracle database of Borusan Cat's WeKing scheduling application (65,774 events, 6,483 schedules, 173 technicians).","A case study combining object-centric Petri net discovery, compliance rule monitoring and object-centric performance metrics on the after-sales service process.","A technician-day case notion that makes single-viewpoint analysis of technician workload tractable.","Documented improvement actions implemented at the company and proposals for further ones."],"date":"2023-01-01","doi":"10.1007/978-3-031-56107-8_31","featured":false,"id":"2023-ICPM-PQMI-2","markdown_url":"https://www.gyunam.com/publication/2023-icpm-pqmi-2/index.md","number":"","pages":"406–418","pdf":"https://www.gyunam.com/publication/2023-icpm-pqmi-2/paper.pdf","projects":[],"publisher":"Springer","research_lines":["object-centric-process-mining"],"series":"LNBIP","summary":"","tags":[],"title":"Analyzing an After-Sales Service Process Using Object-Centric ProcessMining: A Case Study","type":"paper-conference","url":"https://www.gyunam.com/publication/2023-icpm-pqmi-2/","venue":"Process Mining Workshops - ICPM 2023 International Workshops, Rome,Italy, October 23-27, 2023, Revised Selected Papers","venue_short":"","volume":"503","year":2023},{"abstract":"ocpa is a Python library supporting object-centric process mining. Traditional process mining generate insights for one single process. However, many real-life processes are composed of multiple interacting subprocesses and events may involve multiple objects. Object-centric process mining provides techniques for analyzing multiple interacting processes by generalizing process mining techniques. ocpa contains algorithms for object-centric event log management, process discovery, conformance checking, enhancement, and predictive process monitoring. ocpa is easily integrable with existing solutions as it supports existing object-centric event log standards, provides extensive documentation, and is installable through the Python package installer.","agent_summary":"","authors":["Jan Niklas Adams","Gyunam Park","Wil M. P. van der Aalst"],"bibtex":"@article{DBLP:journals/simpa/AdamsPA22,\n author = {Jan Niklas Adams and\nGyunam Park and\nWil M. P. van der Aalst},\n bibsource = {dblp computer science bibliography, https://dblp.org},\n biburl = {https://dblp.org/rec/journals/simpa/AdamsPA22.bib},\n doi = {10.1016/j.simpa.2022.100438},\n journal = {Softw. Impacts},\n pages = {100438},\n timestamp = {Fri, 10 Feb 2023 23:34:32 +0100},\n title = {ocpa: A Python library for object-centric process analysis},\n url = {https://doi.org/10.1016/j.simpa.2022.100438},\n volume = {14},\n year = {2022}\n}","code":"","contributions":["`ocpa`, an open-source Python library (GPL-3.0, GitHub `ocpm/ocpa`, installable with pip, documented on readthedocs, with a Code Ocean capsule) for object-centric process mining.","A data management submodule that imports CSV, JSON-OCEL and XML-OCEL (Object-Centric Event Log) files into an OCEL class, derives process executions, variants and statistics, and exports JSON-OCEL.","Algorithms for discovering object-centric Petri nets and variant graphs, computing precision and fitness, and monitoring control-flow, object involvement and performance constraints.","Model-based object-centric performance analysis (flow, synchronization, pooling and lagging time plus waiting, service and sojourn time) and feature extraction with table, sequence and graph encodings for predictive monitoring.","A visualization submodule with GraphViz rendering of object-centric Petri nets and a dedicated layout algorithm for variant graphs."],"date":"2022-11-01","doi":"10.1016/j.simpa.2022.100438","featured":false,"id":"2023-SoftwareImpact","markdown_url":"https://www.gyunam.com/publication/2023-softwareimpact/index.md","number":"","pages":"100438","pdf":"https://www.gyunam.com/publication/2023-softwareimpact/paper.pdf","projects":[],"publisher":"Elsevier","research_lines":["object-centric-process-mining"],"series":"","summary":"","tags":[],"title":"ocpa: A Python library for object-centric process analysis","type":"article-journal","url":"https://www.gyunam.com/publication/2023-softwareimpact/","venue":"Softw. Impacts","venue_short":"","volume":"14","year":2022},{"abstract":"Several decision points exist in business processes (e.g., whether a purchase order needs a manager’s approval or not), and different decisions are made for different process instances based on their characteristics (e.g., a purchase order higher than e500 needs a manager approval). Decision mining in process mining aims to describe/predict the routing of a process instance at a decision point of the process. By predicting the decision, one can take proactive actions to improve the process. For instance, when a bottleneck is developing in one of the possible decisions, one can predict the decision and bypass the bottleneck. However, despite its huge potential for such operational support, existing techniques for decision mining have focused largely on describing decisions but not on predicting them, deploying decision trees to produce logical expressions to explain the decision. In this work, we aim to enhance the predictive capability of decision mining to enable proactive operational support by deploying more advanced machine learning algorithms. Our proposed approach provides explanations of the predicted decisions using SHAP values to support the elicitation of proactive actions. We have implemented a Web application to support the proposed approach and evaluated the approach using the implementation.","agent_summary":"","authors":["Gyunam Park","Aaron Küsters","Mara Tews","Cameron Pitsch","Jonathan Schneider","Wil M. P. van der Aalst"],"bibtex":"@inproceedings{DBLP:conf/icsoc/ParkKTPSA22,\n author = {Gyunam Park and\nAaron Küsters and\nMara Tews and\nCameron Pitsch and\nJonathan Schneider and\nWil M. P. van der Aalst},\n bibsource = {dblp computer science bibliography, https://dblp.org},\n biburl = {https://dblp.org/rec/conf/icsoc/ParkKTPSA22.bib},\n booktitle = {Service-Oriented Computing - ICSOC 2022 Workshops - ASOCA, AI-PA,\nFMCIoT, WESOACS 2022, Sevilla, Spain, November 29 - December 2,\n2022 Proceedings},\n doi = {10.1007/978-3-031-26507-5_6},\n editor = {Javier Troya and\nRaffaela Mirandola and\nElena Navarro and\nAndrea Delgado and\nSergio Segura and\nGuadalupe Ortiz and\nCesare Pautasso and\nChristian Zirpins and\nPablo Fernández and\nAntonio Ruiz-Cortés},\n pages = {66--79},\n publisher = {Springer},\n series = {Lecture Notes in Computer Science},\n timestamp = {Sat, 25 Mar 2023 17:25:05 +0100},\n title = {Explainable Predictive Decision Mining for Operational Support},\n url = {https://doi.org/10.1007/978-3-031-26507-5_6},\n volume = {13821},\n year = {2022}\n}","code":"","contributions":["Formalizes situation tables, decision models as mappings from feature mappings to likelihood distributions over outgoing transitions of a decision point in a labeled Petri net, and SHAP-based explanations of decisions.","A two-phase approach: an offline phase that computes situation tables from historical event logs and estimates decision models, and an online phase that predicts and explains decisions for running instances.","Uses Shapley Additive Explanations (SHAP) to deliver local explanations per running instance and global explanations by aggregating SHAP values across instances.","An open-source web application, `eXdpn` (GitHub `aarkue/eXdpn`), that discovers Petri nets with the inductive miner, builds situation tables from case, event, and performance features, trains four model families, and visualizes explanations.","An evaluation protocol combining 5-fold cross-validated F1 scores on BPI Challenge 2012 and 2019 with a controlled CPN Tools simulation of a Purchase-to-Pay process whose decision logic is known."],"date":"2022-11-01","doi":"10.1007/978-3-031-26507-5_6","featured":false,"id":"2022-ICSOC-Workshop","markdown_url":"https://www.gyunam.com/publication/2022-icsoc-workshop/index.md","number":"","pages":"66–79","pdf":"https://www.gyunam.com/publication/2022-icsoc-workshop/paper.pdf","projects":[],"publisher":"Springer","research_lines":["prescriptive-process-analytics"],"series":"LNCS","summary":"","tags":[],"title":"Explainable Predictive Decision Mining for Operational Support","type":"paper-conference","url":"https://www.gyunam.com/publication/2022-icsoc-workshop/","venue":"Service-Oriented Computing - ICSOC 2022 Workshops - ASOCA, AI-PA, FMCIoT, WESOACS 2022, Sevilla, Spain, November 29 - December 2, 2022 Proceedings","venue_short":"","volume":"13821","year":2022},{"abstract":"A deviation detection aims to detect deviating process instances, e.g., patients in the healthcare process and products in the manufacturing process. A business process of an organization is executed in various contextual situations, e.g., a COVID-19 pandemic in the case of hospitals and a lack of semiconductor chip shortage in the case of automobile companies. Thus, context-aware deviation detection is essential to provide relevant insights. However, existing work 1) does not provide a systematic way of incorporating various contexts, 2) is tailored to a specific approach without using an extensive pool of existing deviation detection techniques, and 3) does not distinguish positive and negative contexts that justify and refute deviation, respectively. In this work, we provide a framework to bridge the aforementioned gaps. We have implemented the proposed framework as a web service that can be extended to various contexts and deviation detection methods. We have evaluated the effectiveness of the proposed framework by conducting experiments using 255 different contextual scenarios.","agent_summary":"","authors":["Gyunam Park","Janik-Vasily Benzin","Wil M. P. van der Aalst"],"bibtex":"@inproceedings{DBLP:conf/bpm/ParkBA22,\n author = {Gyunam Park and\nJanik-Vasily Benzin and\nWil M. P. van der Aalst},\n bibsource = {dblp computer science bibliography, https://dblp.org},\n biburl = {https://dblp.org/rec/conf/bpm/ParkBA22.bib},\n booktitle = {Business Process Management Forum - BPM 2022 Forum, Münster,\nGermany, September 11-16, 2022, Proceedings},\n doi = {10.1007/978-3-031-16171-1_12},\n editor = {Claudio Di Ciccio and\nRemco M. Dijkman and\nAdela del-Río-Ortega and\nStefanie Rinderle-Ma},\n pages = {190--206},\n publisher = {Springer},\n series = {Lecture Notes in Business Information Processing},\n timestamp = {Thu, 29 Sep 2022 14:12:53 +0200},\n title = {Detecting Context-Aware Deviations in Process Executions},\n url = {https://doi.org/10.1007/978-3-031-16171-1_12},\n volume = {458},\n year = {2022}\n}","code":"","contributions":["Formalizes context-aware deviation detection as a four-class labeling problem combining non-context and context classifications (context-insensitive or context-sensitive, deviating or normal).","A context ontology for deviation detection that merges prior business process context work into intrinsic, relational, and external contexts, and extends it with positive and negative context semantics.","A post-processing framework with four components (deviation detection, context analysis, context linking to events and traces, score revision) that plugs in any score-based detector.","An extensible cloud-based web service, `contect`, on GitHub with a user manual, implementing workload, weekend, waiting time, and capacity utilization contexts and four detectors (Inductive Miner with alignments, Profiles, ADAR, Autoencoder).","An evaluation protocol that injects four deviation types and four contextual scenarios into CPN Tools simulations of an order management process, yielding 225 scenarios per detector."],"date":"2022-09-01","doi":"10.1007/978-3-031-16171-1_12","featured":false,"id":"2022-BPM-Forum","markdown_url":"https://www.gyunam.com/publication/2022-bpm-forum/index.md","number":"","pages":"190–206","pdf":"https://www.gyunam.com/publication/2022-bpm-forum/paper.pdf","projects":[],"publisher":"Springer","research_lines":["process-discovery-and-diagnostics"],"series":"LNBIP","summary":"","tags":[],"title":"Detecting Context-Aware Deviations in Process Executions","type":"paper-conference","url":"https://www.gyunam.com/publication/2022-bpm-forum/","venue":"Business Process Management Forum - BPM 2022 Forum, Münster, Germany, September 11-16, 2022, Proceedings","venue_short":"","volume":"458","year":2022},{"abstract":"As business environments become more dynamic and complex, it becomes indispensable for organizations to objectively analyze business processes, monitor the existing and potential operational frictions, and take proactive actions to mitigate risks and improve performances. Process mining provides techniques to extract insightful knowledge of business processes from event data collected during the execution of the processes. Besides, various approaches have been suggested to support the real-time (predictive) monitoring of the process-related problems. However, the link between the insights from the continuous monitoring and the concrete management actions for the actual process improvement is missing. Action-oriented process mining aims at connecting the knowledge extracted from event data to actions. In this work, we propose a general framework for action-oriented process mining covering the continuous monitoring of operational processes and the automated execution of management actions. Based on the framework, we suggest a cube-based action engine where actions are generated by analyzing monitoring results in a multi-dimensional way. The framework is implemented as a ProM plug-in and evaluated by conducting experiments on both artificial and real-life information systems.","agent_summary":"","authors":["Gyunam Park","Wil MP van der Aalst"],"bibtex":"@article{park2022action,\n author = {Park, Gyunam and van der Aalst, Wil MP},\n journal = {Progress in Artificial Intelligence},\n pages = {1--22},\n publisher = {Springer},\n title = {Action-oriented process mining: bridging the gap between insights and actions},\n year = {2022}\n}","code":"","contributions":["A general framework for action-oriented process mining with a constraint monitor that turns an event stream into a constraint instance stream and an action engine that turns it into an action instance stream.","Formal definitions of constraints, time moments, constraint instances, transactions, actions and action instances over object-centric event data in which multiple object types interact.","A cube-based action engine: constraint cubes with constraint, context and time dimensions, OLAP (online analytical processing) operations such as slice, dice, roll-up and drill-down, assessment functions and transaction mappings.","An open-source `ProM` plug-in, `ActionOrientedProcessMining`, that reads JSON-OCEL (Object-Centric Event Log) streams and defines constraints and actions with a Constraint Formula Language and an Action Formula Language; manual and data on GitHub.","An evaluation protocol that drives a real SAP ERP (enterprise resource planning) ECC 6.0 system with CPN Tools-simulated automation logs and executes generated actions through a dedicated gateway."],"date":"2022-07-01","doi":"10.1007/s13748-022-00281-7","featured":true,"id":"2022-PRAI","markdown_url":"https://www.gyunam.com/publication/2022-prai/index.md","number":"","pages":"","pdf":"https://www.gyunam.com/publication/2022-prai/paper.pdf","projects":[],"publisher":"Springer","research_lines":["prescriptive-process-analytics"],"series":"","summary":"","tags":[],"title":"Action-Oriented Process Mining: Bridging the Gap Between Insights and Actions","type":"article-journal","url":"https://www.gyunam.com/publication/2022-prai/","venue":"Progress in Artificial Intelligence","venue_short":"","volume":"","year":2022},{"abstract":"Digital transformation often entails small-scale changes to information systems supporting the execution of business processes. These changes may increase the operational frictions in process execution, which decreases the process performance. The contributions in the literature providing support to the tracking and impact analysis of small-scale changes are limited in scope and functionality. In this paper, we use the recently developed Digital Twins of Organizations (DTOs) to assess the impact of (process-aware) information systems updates. More in detail, we model the updates using the configuration of DTOs and quantitatively assess different types of impacts of information system updates (structural, operational, and performance-related). We implemented a prototype of the proposed approach. Moreover, we discuss a case study involving a standard ERP procure-to-pay business process.","agent_summary":"","authors":["Gyunam Park","Marco Comuzzi","Wil M. P. van der Aalst"],"bibtex":"@inproceedings{DBLP:conf/rcis/ParkCA22,\n author = {Gyunam Park and\nMarco Comuzzi and\nWil M. P. van der Aalst},\n bibsource = {dblp computer science bibliography, https://dblp.org},\n biburl = {https://dblp.org/rec/conf/rcis/ParkCA22.bib},\n booktitle = {Research Challenges in Information Science - 16th International Conference,\nRCIS 2022, Barcelona, Spain, May 17-20, 2022, Proceedings},\n doi = {10.1007/978-3-031-05760-1_10},\n editor = {Renata S. S. Guizzardi and\nJolita Ralyté and\nXavier Franch},\n pages = {159--176},\n publisher = {Springer},\n series = {Lecture Notes in Business Information Processing},\n timestamp = {Wed, 18 May 2022 10:21:56 +0200},\n title = {Analyzing Process-Aware Information System Updates Using Digital Twins\nof Organizations},\n url = {https://doi.org/10.1007/978-3-031-05760-1_10},\n volume = {446},\n year = {2022}\n}","code":"","contributions":["A meta-model of process-aware information system (PAIS) entities, updates to business functions and business objects, and three impact types: structural, operational and performance.","Formalizes PAIS updates as actions and action instances that change the configuration (valve and operation assignments) of a Digital Twin Interface Model (DT-IM) built on object-centric Petri nets, with a notion of effective change.","Formal definitions of impacted object types and transitions, of the running objects affected via markings and a transition relation function, and of performance impact as the difference in diagnostics before and after an update.","A Web-based prototype, `impacta` (GitHub gyunamister/impacta), that discovers an object-centric Petri net from OCEL data, synchronizes configuration and state via token-based replay, executes actions and reports impacts.","A case study on a simulated procure-to-pay PAIS modeled after the standard SAP ERP process."],"date":"2022-05-01","doi":"10.1007/978-3-031-05760-1_10","featured":false,"id":"2022-RCIS","markdown_url":"https://www.gyunam.com/publication/2022-rcis/index.md","number":"","pages":"159–176","pdf":"https://www.gyunam.com/publication/2022-rcis/paper.pdf","projects":[],"publisher":"Springer","research_lines":["prescriptive-process-analytics"],"series":"LNBIP","summary":"","tags":[],"title":"Analyzing Process-Aware Information System Updates Using Digital Twins of Organizations","type":"paper-conference","url":"https://www.gyunam.com/publication/2022-rcis/","venue":"Research Challenges in Information Science - 16th International Conference, RCIS 2022, Barcelona, Spain, May 17-20, 2022, Proceedings","venue_short":"","volume":"446","year":2022},{"abstract":"Performance analysis in process mining aims to provide insights on the performance of a business process by using a process model as a formal representation of the process. Existing techniques for performance analysis assume that a single case notion exists in a business process (e.g., a patient in healthcare process). However, in reality, different objects might interact (e.g., order, delivery, and invoice in an O2C process). In such a setting, traditional techniques may yield misleading or even incorrect insights on performance metrics such as waiting time. More importantly, by considering the interaction between objects, we can define object-centric performance metrics such as synchronization time, pooling time, and lagging time. In this work, we propose a novel approach to performance analysis considering multiple case notions by using object-centric Petri nets as formal representations of business processes. The proposed approach correctly computes existing performance metrics, while supporting the derivation of newly-introduced object-centric performance metrics. We have implemented the approach as a web application and conducted a case study based on a real-life loan application process.","agent_summary":"","authors":["Gyunam Park","Jan Niklas Adams","Wil M. P. van der Aalst"],"bibtex":"@inproceedings{DBLP:conf/er/ParkAA22,\n author = {Gyunam Park and\nJan Niklas Adams and\nWil M. P. van der Aalst},\n bibsource = {dblp computer science bibliography, https://dblp.org},\n biburl = {https://dblp.org/rec/conf/er/ParkAA22.bib},\n booktitle = {Conceptual Modeling - 41st International Conference, ER 2022, Hyderabad,\nIndia, October 17-20, 2022, Proceedings},\n doi = {10.1007/978-3-031-17995-2_20},\n editor = {Jolita Ralyté and\nSharma Chakravarthy and\nMukesh K. Mohania and\nManfred A. Jeusfeld and\nKamalakar Karlapalem},\n pages = {281--292},\n publisher = {Springer},\n series = {Lecture Notes in Computer Science},\n timestamp = {Wed, 22 Mar 2023 17:54:58 +0100},\n title = {OPerA: Object-Centric Performance Analysis},\n url = {https://doi.org/10.1007/978-3-031-17995-2_20},\n volume = {13607},\n year = {2022}\n}","code":"","contributions":["An approach that discovers an object-centric Petri net (OCPN) from an object-centric event log (OCEL), replays the log to obtain event occurrences and token visits, and computes performance per event occurrence.","Formalizes replay (event occurrences, token visits) and the relation between an event occurrence and the latest token visit of each object it involves.","Seven measurement functions: flow, sojourn, waiting and service time plus the object-centric synchronization, pooling and lagging time.","A web application implementation (`OPerA`, on GitHub at gyunamister/OPerA) with demo video and manuals.","A case study on the BPI Challenge 2017 loan application log with application and offer object types."],"date":"2022-01-01","doi":"10.1007/978-3-031-17995-2_20","featured":false,"id":"2022-ER","markdown_url":"https://www.gyunam.com/publication/2022-er/index.md","number":"","pages":"281–292","pdf":"https://www.gyunam.com/publication/2022-er/paper.pdf","projects":[],"publisher":"Springer","research_lines":["object-centric-process-mining"],"series":"LNCS","summary":"","tags":[],"title":"OPerA: Object-Centric Performance Analysis","type":"paper-conference","url":"https://www.gyunam.com/publication/2022-er/","venue":"Conceptual Modeling - 41st International Conference, ER 2022, Hyderabad, India, October 17-20, 2022, Proceedings","venue_short":"","volume":"13607","year":2022},{"abstract":"Traditional process mining techniques take event data as input where each event is associated with exactly one object. An object represents the instantiation of a process. Object-centric event data contain events associated with multiple objects expressing the interaction of multiple processes. As traditional process mining techniques assume events associated with exactly one object, these techniques cannot be applied to object-centric event data. To use traditional process mining techniques, object-centric event data are flattened by removing all object references but one. The flattening process is lossy, leading to inaccurate features extracted from flattened data. Furthermore, the graph-like structure of object-centric event data is lost when flattening. In this paper, we introduce a general framework for extracting and encoding features from object-centric event data. We calculate features natively on the object-centric event data, leading to accurate measures. Furthermore, we provide three encodings for these features: tabular, sequential, and graph-based. While tabular and sequential encodings have been heavily used in process mining, the graph-based encoding is a new technique preserving the structure of the object-centric event data. We provide six use cases: a visualization and a prediction use case for each of the three encodings. We use explainable AI in the prediction use cases to show the utility of both the object-centric features and the structure of the sequential and graph-based encoding for a predictive model.","agent_summary":"","authors":["Jan Niklas Adams","Gyunam Park","Sergej Levich","Daniel Schuster","Wil M. P. van der Aalst"],"bibtex":"@inproceedings{DBLP:conf/icsoc/AdamsPLSA22,\n author = {Jan Niklas Adams and\nGyunam Park and\nSergej Levich and\nDaniel Schuster and\nWil M. P. van der Aalst},\n bibsource = {dblp computer science bibliography, https://dblp.org},\n biburl = {https://dblp.org/rec/conf/icsoc/AdamsPLSA22.bib},\n booktitle = {Service-Oriented Computing - 20th International Conference, ICSOC\n2022, Seville, Spain, November 29 - December 2, 2022, Proceedings},\n doi = {10.1007/978-3-031-20984-0_3},\n editor = {Javier Troya and\nBrahim Medjahed and\nMario Piattini and\nLina Yao and\nPablo Fernández and\nAntonio Ruiz-Cortés},\n pages = {36--53},\n publisher = {Springer},\n series = {Lecture Notes in Computer Science},\n timestamp = {Wed, 28 Dec 2022 16:42:43 +0100},\n title = {A Framework for Extracting and Encoding Features from Object-Centric\nEvent Data},\n url = {https://doi.org/10.1007/978-3-031-20984-0_3},\n volume = {13740},\n year = {2022}\n}","code":"","contributions":["A general framework that translates the control-flow, data-flow, resource and performance features of de Leoni et al. to object-centric event logs and adds an object perspective and object-centric performance features.","Formal definitions of the object graph, process executions and two extraction techniques, connected components and leading object type, on Object-Centric Event Logs (OCELs).","Three formally defined feature encodings, tabular, sequential and a new graph-based encoding whose nodes are events and whose edges follow each object's lifecycle.","Six use cases on the BPI Challenge 2017 loan application log, one visualization and one remaining-time prediction per encoding, with SHAP values to explain feature and structure importance.","An open-source implementation in the `ocpa` Python library and a GitHub repository to reproduce the experiments."],"date":"2022-01-01","doi":"10.1007/978-3-031-20984-0_3","featured":false,"id":"2022-ICSOC","markdown_url":"https://www.gyunam.com/publication/2022-icsoc/index.md","number":"","pages":"36–53","pdf":"https://www.gyunam.com/publication/2022-icsoc/paper.pdf","projects":[],"publisher":"Springer","research_lines":["object-centric-process-mining"],"series":"LNCS","summary":"","tags":[],"title":"A Framework for Extracting and Encoding Features from Object-Centric Event Data","type":"paper-conference","url":"https://www.gyunam.com/publication/2022-icsoc/","venue":"Service-Oriented Computing - 20th International Conference, ICSOC 2022, Seville, Spain, November 29 - December 2, 2022, Proceedings","venue_short":"","volume":"13740","year":2022},{"abstract":"Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. This paper proposes an instance selection procedure that allows sampling training process instances for prediction models. We show that our sampling method allows for a significant increase of training speed for next activity prediction methods while maintaining reliable levels of prediction accuracy.","agent_summary":"","authors":["Mohammadreza Fani Sani","Mozhgan Vazifehdoostirani","Gyunam Park","Marco Pegoraro","Sebastiaan J. van Zelst","and Wil M.P. van der Aalst"],"bibtex":"@inproceedings{DBLP:conf/icpm/SaniVP0ZA21,\n  author    = {Mohammadreza Fani Sani and Mozhgan Vazifehdoostirani and Gyunam Park and Marco Pegoraro and Sebastiaan J. van Zelst and Wil M. P. van der Aalst},\n  editor    = {Jorge Munoz{-}Gama and Xixi Lu},\n  title     = {Event Log Sampling for Predictive Monitoring},\n  booktitle = {Process Mining Workshops - {ICPM} 2021 International Workshops, Eindhoven, The Netherlands, October 31 - November 4, 2021, Revised Selected Papers},\n  series    = {Lecture Notes in Business Information Processing},\n  volume    = {433},\n  pages     = {154--166},\n  publisher = {Springer},\n  year      = {2021},\n  url       = {https://doi.org/10.1007/978-3-030-98581-3\\_12},\n  doi       = {10.1007/978-3-030-98581-3\\_12}\n}","code":"","contributions":["An event log sampling procedure for predictive monitoring that traverses variants, computes attribute distributions per variant, sorts cases within each variant and returns a subset of cases.","Three variant-aware selection rules: unique (one case per variant), logarithmic (log base k of the variant frequency) and division (frequency divided by k), the last guaranteed to preserve all variants.","Formalizes variant-preserving sampling and the evaluation measures reduction rate, relative accuracy and training and feature-extraction speed-up.","An implementation of the sampling methods in `ProM`, with feature extraction and prediction code on GitHub.","An evaluation protocol on two real logs (Road Traffic Fine Management and BPI Challenge 2012 W) with LSTM and XGBoost next-activity predictors, 5-fold cross-validation repeated five times."],"date":"2021-11-01","doi":"10.1007/978-3-030-98581-3_12","featured":false,"id":"2021-ML4PM","markdown_url":"https://www.gyunam.com/publication/2021-ml4pm/index.md","number":"","pages":"154–166","pdf":"https://www.gyunam.com/publication/2021-ml4pm/paper.pdf","projects":[],"publisher":"Springer","research_lines":["prescriptive-process-analytics"],"series":"LNBIP","summary":"","tags":[],"title":"Event Log Sampling for Predictive Monitoring","type":"paper-conference","url":"https://www.gyunam.com/publication/2021-ml4pm/","venue":"International Workshop on Leveraging Machine Learning in Process Mining (ML4PM)","venue_short":"","volume":"433","year":2021},{"abstract":"A Digital Twin of an Organization (DTO) is a mirrored representation of an organization, aiming to improve the business process of the organization by providing a transparent view over the process and automating management actions to deal with existing and potential risks. Unlike wide applications of digital twins to product design and predictive maintenance, no concrete realizations of DTOs for business process improvement have been studied. In this work, we aim to realize DTOs using action-oriented process mining, a collection of techniques to evaluate violations of constraints and produce the required actions. To this end, we suggest a digital twin interface model as a transparent representation of an organization describing the current state of business processes and possible configurations in underlying information systems. By interacting with the representation, process analysts can elicit constraints and actions that will be continuously monitored and triggered by an action engine to improve business processes. We have implemented a web service to support it and evaluated the feasibility of the proposed approach by conducting a case study using an artificial information system supporting an order handling process.","agent_summary":"","authors":["Gyunam Park","Wil M.P. van der Aalst"],"bibtex":"@inproceedings{DBLP:conf/icpm/ParkA21,\n  author    = {Gyunam Park and Wil M. P. van der Aalst},\n  editor    = {Claudio Di Ciccio and Chiara Di Francescomarino and Pnina Soffer},\n  title     = {Realizing {A} Digital Twin of An Organization Using Action-oriented Process Mining},\n  booktitle = {3rd International Conference on Process Mining, {ICPM} 2021, Eindhoven, The Netherlands, October 31 - Nov. 4, 2021},\n  pages     = {104--111},\n  publisher = {{IEEE}},\n  year      = {2021},\n  url       = {https://doi.org/10.1109/ICPM53251.2021.9576846},\n  doi       = {10.1109/ICPM53251.2021.9576846}\n}","code":"","contributions":["A digital twin interface model (DT-IM) formalized as an object-centric Petri net extended with valves (configurable system parameters) and guards encoding routing and resource allocation rules.","Formal definitions of the operational view (marking, object values, diagnostics) and the control view (configurations and actions), together with an operation engine and an action engine.","Taxonomies of constraint violations (compliance- versus performance-oriented) and management actions (inflow, routing, resource allocation, capacity) to support eliciting action patterns.","A containerized web service (`dtween`, on GitHub) that builds DT-IMs from Object-Centric Event Log (OCEL) data, updates states by token-based replay, visualizes them, and evaluates action patterns.","A proof-of-concept case study with a publicly available artificial information system that supports an order handling process with 14 resources."],"date":"2021-11-01","doi":"10.1109/ICPM53251.2021.9576846","featured":true,"id":"2021-ICPM","markdown_url":"https://www.gyunam.com/publication/2021-icpm/index.md","number":"","pages":"104–111","pdf":"https://www.gyunam.com/publication/2021-icpm/paper.pdf","projects":[],"publisher":"IEEE","research_lines":["prescriptive-process-analytics"],"series":"","summary":"","tags":["Conference Proceedings","Action-Oriented Process Mining","ICPM2021"],"title":"Realizing A Digital Twin of An Organization Using Action-oriented Process Mining","type":"paper-conference","url":"https://www.gyunam.com/publication/2021-icpm/","venue":"2021 International Conference on Process Mining (ICPM)","venue_short":"","volume":"","year":2021},{"abstract":"The extraction, transformation, and loading of event logs from information systems is the first and the most expensive step in process mining. In particular, extracting event logs from popular ERP systems such as SAP poses major challenges, given the size and the structure of the data. Open-source support for ETL is scarce, while commercial process mining vendors maintain connectors to ERP systems supporting ETL of a limited number of business processes in an ad-hoc manner. In this paper, we propose an approach to facilitate event data extraction from SAP ERP systems. In the proposed approach, we store event data in the format of object-centric event logs that efficiently describe executions of business processes supported by ERP systems. To evaluate the feasibility of the proposed approach, we have developed a tool implementing it and conducted case studies with a real-life SAP ERP system","agent_summary":"","authors":["Alessandro Berti","Gyunam Park","Majid Rafiei","and Wil van der Aalst"],"bibtex":"@inproceedings{DBLP:conf/icpm/BertiPRA21,\n  author    = {Alessandro Berti and Gyunam Park and Majid Rafiei and Wil M. P. van der Aalst},\n  editor    = {Jorge Munoz{-}Gama and Xixi Lu},\n  title     = {An Event Data Extraction Approach from {SAP} {ERP} for Process Mining},\n  booktitle = {Process Mining Workshops - {ICPM} 2021 International Workshops, Eindhoven, The Netherlands, October 31 - November 4, 2021, Revised Selected Papers},\n  series    = {Lecture Notes in Business Information Processing},\n  volume    = {433},\n  pages     = {255--267},\n  publisher = {Springer},\n  year      = {2021},\n  url       = {https://doi.org/10.1007/978-3-030-98581-3\\_19},\n  doi       = {10.1007/978-3-030-98581-3\\_19}\n}","code":"","contributions":[],"date":"2021-11-01","doi":"10.1007/978-3-030-98581-3_19","featured":false,"id":"2021-PQMI","markdown_url":"https://www.gyunam.com/publication/2021-pqmi/index.md","number":"","pages":"255–267","pdf":"","projects":[],"publisher":"Springer","research_lines":["object-centric-process-mining"],"series":"LNBIP","summary":"","tags":[],"title":"An Event Data Extraction Approach from SAP ERP for Process Mining","type":"paper-conference","url":"https://www.gyunam.com/publication/2021-pqmi/","venue":"6th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI)","venue_short":"","volume":"433","year":2021},{"abstract":"In semiconductor manufacturing, data-driven methodologies have enabled the resolution of various issues, particularly yield management and enhancement. Yield, one of the crucial key performance indicators in semiconductor manufacturing, is mostly affected by production resources, i.e., equipment involved in the process. There is a lot of research on finding the correlation between yield and the status of resources. However, in general, multiple resources are engaged in production processes, which may cause multicollinearity among resources. Therefore, it is important to discover resource paths that are positively or negatively associated with yield. This article proposes a systematic methodology for discovering a resource-oriented transition system model in a semiconductor manufacturing process to identify resource paths resulting in high and low yield. The proposed method is based on the model-based analysis (i.e., finite state machine mining) in process mining and statistical analyses. We conducted an empirical study with real-life data from one of the leading semiconductor manufacturing companies to validate the proposed approach.","agent_summary":"","authors":["Minsu Cho","Gyunam Park","Minseok Song","Jinyoun Lee","Byeongeon Lee","Euiseok Kum"],"bibtex":"@article{ieee-2020,\n  author    = {Cho, Minsu and Gyunam Park and Song, Minseok and Lee, Jinyoun and Lee, Byeongeon and Kum, Euiseok},\n  journal   = {IEEE Transactions on Semiconductor Manufacturing},\n  title     = {Discovery of Resource-Oriented Transition Systems for Yield Enhancement in Semiconductor Manufacturing},\n  year      = {2021},\n  volume    = {34},\n  number    = {1},\n  pages     = {17-24},\n  doi       = {10.1109/TSM.2020.3045686}\n}","code":"","contributions":["Formalizes a yield-enhanced event log in which each case (wafer) carries a mandatory yield attribute, and a resource-oriented finite state machine (FSM) whose states and events are the resources of process steps.","A four-phase methodology: data preparation, FSM construction with best- and worst-case paths overlaid, statistical model simplification, and evaluation and interpretation.","A best- and worst-case identification scheme that selects a normality test (Kolmogorov-Smirnov) and then uses either mean and standard deviation or median and median absolute deviation bands with a user-defined weight.","A statistical simplification pipeline (ANOVA with linear contrasts, or Kruskal-Wallis with Mann-Whitney U tests) that labels resources high-class, low-class, or insignificant and merges insignificant resources per step with three merging variants.","An empirical study on real manufacturing execution system data from one of the largest Korean semiconductor manufacturers, with a confusion-matrix evaluation protocol for best, moderate, and worst cases."],"date":"2021-09-24","doi":"10.1109/TSM.2020.3045686","featured":false,"id":"2020-IEEE-TSM","markdown_url":"https://www.gyunam.com/publication/2020-ieee-tsm/index.md","number":"1","pages":"17–24","pdf":"https://www.gyunam.com/publication/2020-ieee-tsm/paper.pdf","projects":[],"publisher":"IEEE","research_lines":["process-discovery-and-diagnostics"],"series":"","summary":"","tags":["Journal Article","Manufacturing","IEEE-TSM"],"title":"Discovery of Resource-Oriented Transition Systems for Yield Enhancement in Semiconductor Manufacturing","type":"article-journal","url":"https://www.gyunam.com/publication/2020-ieee-tsm/","venue":"IEEE Transactions on Semiconductor Manufacturing","venue_short":"","volume":"34","year":2021},{"abstract":"A digital twin of an organization (DTO) is a digital replication of an organization used to analyze weaknesses in business processes and support operational decision-making by simulating different scenarios. As a key enabling technology of DTO, business process simulation provides techniques to design and implement simulation models that replicate real-life business processes. Existing approaches have been focusing on providing highly flexible design tools and data-driven evidence to improve the accuracy of simulation models. Provided with such tools and evidence, business analysts are required to reflect comprehensive aspects of reality with subjective judgments, including the design of ERP systems and the organizational interaction with the system. However, given the complexity of ERP systems, it is infeasible and error-prone to manually incorporate the business logic and data restrictions of the system into simulation models, impeding the faithfulness and reliability of the following analysis. In this work, we propose a framework to integrate ERP systems in business process simulation to overcome this limitation and ensure the reliability of the simulation results. The framework is implemented in ProM using the SAP ERP system and CPN Tools.","agent_summary":"","authors":["Gyunam Park","Wil M.P. van der Aalst"],"bibtex":"@inproceedings{DBLP:conf/caise/ParkA21,\n  author    = {Gyunam Park and Wil M. P. van der Aalst},\n  editor    = {Adriano Augusto and Asif Gill and Selmin Nurcan and Iris Reinhartz{-}Berger and Rainer Schmidt and Jelena Zdravkovic},\n  title     = {Towards Reliable Business Process Simulation: {A} Framework to Integrate {ERP} Systems},\n  booktitle = {Enterprise, Business-Process and Information Systems Modeling - 22nd International Conference, {BPMDS} 2021, and 26th International Conference, {EMMSAD} 2021, Held at CAiSE 2021, Melbourne, VIC, Australia, June 28-29, 2021, Proceedings},\n  series    = {Lecture Notes in Business Information Processing},\n  volume    = {421},\n  pages     = {112--127},\n  publisher = {Springer},\n  year      = {2021},\n  url       = {https://doi.org/10.1007/978-3-030-79186-5\\_8},\n  doi       = {10.1007/978-3-030-79186-5\\_8}\n}","code":"","contributions":["A framework with three components: an Enterprise Resource Planning (ERP) system abstracted as an object model updated by transactions, a simulation engine producing commands, and a transformation engine translating commands into executions.","Formal definitions of object models, transactions, executions, commands, transaction mappings and parameter mappings that underpin the integration.","An implementation using SAP ERP ECC 6.0 as the system, CPN Tools (Colored Petri Nets) as the simulation engine, and a `ProM` plug-in (ERPSimulator package) that converts XML commands into SAP Remote Function Calls.","A proof of concept that simulates three order-to-cash scenarios (low conversion rate, manual price changes, order cancellations), with CPN files, commands and a manual released on GitHub."],"date":"2021-07-17","doi":"10.1007/978-3-030-79186-5_8","featured":false,"id":"2021-BPMDS","markdown_url":"https://www.gyunam.com/publication/2021-bpmds/index.md","number":"","pages":"112–127","pdf":"https://www.gyunam.com/publication/2021-bpmds/paper.pdf","projects":[],"publisher":"Springer","research_lines":["prescriptive-process-analytics"],"series":"LNBIP","summary":"","tags":["Conference Proceedings","Simulation","BPMDS2021"],"title":"Towards Reliable Business Process Simulation: A Framework to Integrate ERP Systems","type":"paper-conference","url":"https://www.gyunam.com/publication/2021-bpmds/","venue":"International Conference on Business Process Modeling, Development and Support","venue_short":"","volume":"421","year":2021},{"abstract":"The application of process mining techniques to real-life information systems is often challenging. Considering a Purchase to Pay (P2P) process, several case notions such as order and item are involved, interacting with each other. Therefore, creating an event log where events need to relate to a single case (i.e., process instance) leads to convergence (i.e., the duplication of an event related to different cases) and divergence (i.e., the inability to separate events within the same case) problems. To avoid such problems, object-centric event logs have been proposed, where each event can be related to different objects. These can be exploited by a new set of process mining techniques. This paper describes OCEL (Object-Centric Event Log), a generic and scalable format for the storage of object-centric event logs. The implementation of the format can use either JSON or XML, and tool support is provided.","agent_summary":"","authors":["Anahita Farhang Ghahfarokhi","Gyunam Park","Alessandro Berti","Wil M.P. van der Aalst"],"bibtex":"@inproceedings{DBLP:conf/adbis/GhahfarokhiPBA21,\n  author    = {Anahita Farhang Ghahfarokhi and Gyunam Park and Alessandro Berti and Wil M. P. van der Aalst},\n  editor    = {Ladjel Bellatreche and Marlon Dumas and Panagiotis Karras and Raimundas Matulevicius and Ahmed Awad and Matthias Weidlich and Mirjana Ivanovic and Olaf Hartig},\n  title     = {{OCEL:} {A} Standard for Object-Centric Event Logs},\n  booktitle = {New Trends in Database and Information Systems - {ADBIS} 2021 Short Papers, Doctoral Consortium and Workshops: DOING, SIMPDA, MADEISD, MegaData, CAoNS, Tartu, Estonia, August 24-26, 2021, Proceedings},\n  series    = {Communications in Computer and Information Science},\n  volume    = {1450},\n  pages     = {169--175},\n  publisher = {Springer},\n  year      = {2021},\n  url       = {https://doi.org/10.1007/978-3-030-85082-1\\_16},\n  doi       = {10.1007/978-3-030-85082-1\\_16}\n}","code":"","contributions":["The OCEL (Object-Centric Event Log) standard: a meta-model in which a log holds events and objects, each event references a set of objects of different types, and object attributes are stored once rather than per event.","Support for strongly typed and nested elements (lists and maps) and global default elements for logs, events and objects.","Serializations in JSON and XML with validation schemas, specified formally at ocel-standard.org.","Tool support in Python (the ocel-support library on GitHub) and Java (`ProM` OCELStandard package) for import, export, schema validation and flattening to traditional event logs.","Example object-centric event logs extracted from the SAP IDES system in both serializations."],"date":"2021-07-17","doi":"10.1007/978-3-030-85082-1_16","featured":false,"id":"2021-SIMPDA","markdown_url":"https://www.gyunam.com/publication/2021-simpda/index.md","number":"","pages":"169–175","pdf":"https://www.gyunam.com/publication/2021-simpda/paper.pdf","projects":[],"publisher":"Springer","research_lines":["object-centric-process-mining"],"series":"CCIS","summary":"","tags":["Conference Proceedings","Object-Centric Process Mining","SIMPDA2021"],"title":"OCEL: A Standard for Object-Centric Event Logs","type":"paper-conference","url":"https://www.gyunam.com/publication/2021-simpda/","venue":"European Conference on Advances in Databases and Information Systems","venue_short":"","volume":"1450","year":2021},{"abstract":"Context-aware process mining aims at extending a contemporary approach with process contexts for realistic process modeling. Regarding this discipline, there have been several attempts to combine process discovery and predictive process modeling and context information, e.g., time and cost. The focus of this paper is to develop a new method for deriving a quality-aware resource model. It first generates a resource-oriented transition system and identifies the quality-based superior and inferior cases. The quality-aware resource model is constructed by integrating these two results, and we also propose a model simplification method based on statistical analyses for better resource model visualization. This paper includes tooling support for our method, and one of the case studies on a semiconductor manufacturing process is presented to validate the usefulness of the proposed approach. We expect our work is practically applicable to a range of fields, including manufacturing and healthcare systems.","agent_summary":"","authors":["Minsu Cho","Gyunam Park","Minseok Song","Jinyoun Lee","Euiseok Kum"],"bibtex":"@article{appliedsciences-2020,\n  author    = {Minsu Cho and Gyunam Park and Minseok Song and Jinyoun Lee and Euiseok Kum},\n  title     = {Quality-Aware Resource Model Discovery},\n  journal   = {Applied Sciences},\n  volume    = {11},\n  year      = {2021},\n  number    = {12}\n}","code":"","contributions":["A five-step method for discovering quality-aware resource models: resource-oriented transition system discovery, superior and inferior case identification, integrated model construction, statistical analysis of resources, and model simplification.","Formalizes case-annotated transition systems and the superior-case ratio, inferior-case ratio, between-sum and between-difference measures used to rank and color states and arcs.","An algorithm that classifies cases as superior, inferior or moderate using a Kolmogorov-Smirnov normality test followed by mean and standard deviation or median and median absolute deviation bands.","A statistical procedure per activity (ANOVA with linear contrasts, or Kruskal-Wallis with Mann-Whitney U tests) that labels resources as superior, inferior or moderate, with three merging rules for simplification.","A Python tool with a graphical user interface covering import, preprocessing, model mining, statistical analysis, interactive filtering and highlighting, and export."],"date":"2021-06-21","doi":"10.3390/app11125730","featured":false,"id":"2021-Applied-Sciences","markdown_url":"https://www.gyunam.com/publication/2021-applied-sciences/index.md","number":"12","pages":"","pdf":"https://www.gyunam.com/publication/2021-applied-sciences/paper.pdf","projects":[],"publisher":"MDPI","research_lines":["process-discovery-and-diagnostics"],"series":"","summary":"","tags":["Journal Article","Prediction","DSS"],"title":"Quality-Aware Resource Model Discovery","type":"article-journal","url":"https://www.gyunam.com/publication/2021-applied-sciences/","venue":"Applied Sciences","venue_short":"","volume":"11","year":2021},{"abstract":"Object-centric process mining provides a set of techniques for the analysis of event data where events are associated to several objects. To store Object-Centric Event Logs (OCELs), the JSON-OCEL and JSON-XML formats have been recently proposed. However, the proposed implementations of the OCEL are file-based. This means that the entire file needs to be parsed in order to apply process mining techniques, such as the discovery of object-centric process models. In this paper, we propose a database storage for the OCEL format using the MongoDB document database. Since documents in MongoDB are equivalent to JSON objects, the current JSON implementation of the standard could be translated straightforwardly in a series of MongoDB collections.","agent_summary":"","authors":["Alessandro Berti","Anahita Farhang Ghahfarokhi","Gyunam Park","Wil M. P. van der Aalst"],"bibtex":"@article{DBLP:journals/corr/abs-2202-05639,\n author = {Alessandro Berti and\nAnahita Farhang Ghahfarokhi and\nGyunam Park and\nWil M. P. van der Aalst},\n bibsource = {dblp computer science bibliography, https://dblp.org},\n biburl = {https://dblp.org/rec/journals/corr/abs-2202-05639.bib},\n eprint = {2202.05639},\n eprinttype = {arXiv},\n journal = {CoRR},\n timestamp = {Fri, 18 Feb 2022 12:23:53 +0100},\n title = {A Scalable Database for the Storage of Object-Centric Event Logs},\n url = {https://arxiv.org/abs/2202.05639},\n volume = {abs/2202.05639},\n year = {2022}\n}","code":"","contributions":["A MongoDB schema for the OCEL standard that translates the JSON-OCEL events, objects and global sections into document collections.","An indexing scheme (identifier, activity and a multikey index on the object map for events; identifier and type for objects) to speed up queries.","Database-level aggregations that compute object lifecycles, event and object statistics, and object-centric directly-follows graphs without exporting the data.","A Python tool in the `ocel-support` repository (GitHub OCEL-standard/ocel-support) that imports and exports JSON/XML-OCEL to and from MongoDB with example scripts.","A scalability assessment on synthetic logs from 1 million to 100 million events."],"date":"2021-04-01","doi":"","featured":false,"id":"2021-ICPM-Demo","markdown_url":"https://www.gyunam.com/publication/2021-icpm-demo/index.md","number":"","pages":"","pdf":"https://www.gyunam.com/publication/2021-icpm-demo/paper.pdf","projects":[],"publisher":"","research_lines":["object-centric-process-mining"],"series":"","summary":"","tags":[],"title":"A Scalable Database for the Storage of Object-Centric Event Logs","type":"article-journal","url":"https://www.gyunam.com/publication/2021-icpm-demo/","venue":"CoRR","venue_short":"","volume":"","year":2021},{"abstract":"Process mining provides techniques to extract process-centric knowledge from event data available in information systems. These techniques have been successfully adopted to solve process-related problems in diverse industries. In recent years, the attention of the process mining discipline has shifted to supporting continuous process management and actual process improvement. To this end, techniques for operational support, including predictive process monitoring, have been actively studied to monitor and influence running cases. However, the conversion from insightful diagnostics to actual actions is still left to the user (i.e., the “action part” is missing and outside the scope of today’s process mining tools). In this paper, we propose a general framework for *action-oriented process mining* that supports the continuous management of operational processes and the automated execution of actions to improve the process. As proof of concept, the framework is implemented in *ProM*.","agent_summary":"","authors":["Gyunam Park","Wil M.P. van der Aalst"],"bibtex":"@inproceedings{DBLP:conf/bpm/ParkA20,\n  author    = {Gyunam Park and Wil M. P. van der Aalst},\n  editor    = {Adela del{-}R{\\'{\\i}}o{-}Ortega and Henrik Leopold and Fl{\\'{a}}via Maria Santoro},\n  title     = {A General Framework for Action-Oriented Process Mining},\n  booktitle = {Business Process Management Workshops - {BPM} 2020 International Workshops, Seville, Spain, September 13-18, 2020, Revised Selected Papers},\n  series    = {Lecture Notes in Business Information Processing},\n  volume    = {397},\n  pages     = {206--218},\n  publisher = {Springer},\n  year      = {2020},\n  url       = {https://doi.org/10.1007/978-3-030-66498-5\\_16},\n  doi       = {10.1007/978-3-030-66498-5\\_16}\n}","code":"","contributions":["A general framework for action-oriented process mining with two components: a constraint monitor that turns an event stream into a constraint instance stream, and an action engine that turns it into an action instance stream of transactions.","Formalizes event streams over multiple object classes (a conventional event log being a special case), time windows, time moments, contexts, constraint formulas, constraints, transactions, action formulas and their instances.","Distinguishes three kinds of actions: process-level valves, instance-level adaptors and alerts.","A `ProM` plug-in (package ActionOrientedProcessMining) with an XML-based Object-Centric Log format, a Constraint Formula Language, an Action Formula Language and an Action Instance Stream format, released on GitHub (gyunamister/ActionOrientedProcessMining).","A proof of concept with a simulated order handling information system and a gateway that translates action instances into system transactions."],"date":"2021-01-19","doi":"10.1007/978-3-030-66498-5_16","featured":false,"id":"2021-BPM","markdown_url":"https://www.gyunam.com/publication/2021-bpm/index.md","number":"","pages":"206–218","pdf":"https://www.gyunam.com/publication/2021-bpm/paper.pdf","projects":[],"publisher":"Springer","research_lines":["prescriptive-process-analytics"],"series":"LNBIP","summary":"","tags":["Conference Proceedings","Action-Oriented Process Mining","BPM2020"],"title":"A General Framework for Action-Oriented Process Mining","type":"paper-conference","url":"https://www.gyunam.com/publication/2021-bpm/","venue":"2020 International Conference on Business Process Management","venue_short":"","volume":"397","year":2021},{"abstract":"Predictive business process monitoring (PBPM) deals with predicting a process's future behavior based on historical event logs to support a process's execution. Many of the recent techniques utilize a machine-learned model to predict which event type is the next most likely. Beyond PBPM, prescriptive BPM aims at finding optimal actions based on considering relevant key performance indicators. Existing techniques are geared towards the outcome prediction and deal with alarms for interventions or interventions that do not represent process events. In this paper, we argue that the next event prediction is insufficient for practitioners. Accordingly, this research-in-progress paper proposes a technique for determining next best actions that represent process events. We conducted an intermediate evaluation to test the usefulness and the quality of our technique compared to the most frequently cited technique for predicting next events. The results show a higher usefulness for process participants than a next most likely event.","agent_summary":"","authors":["Sven Weinzierl","Sandra Zilker","Matthias Stierle","Gyunam Park","Martin Matzner"],"bibtex":"@inproceedings{DBLP:conf/wirtschaftsinformatik/WeinzierlZSMP20,\n  author    = {Sven Weinzierl and Sandra Zilker and Matthias Stierle and Martin Matzner and Gyunam Park},\n  editor    = {Norbert Gronau and Moreen Heine and Hanna Krasnova and K. Poustcchi},\n  title     = {From predictive to prescriptive process monitoring: Recommending the next best actions instead of calculating the next most likely events},\n  booktitle = {Entwicklungen, Chancen und Herausforderungen der Digitalisierung: Proceedings der 15. Internationalen Tagung Wirtschaftsinformatik, {WI} 2020, Potsdam, Germany, March 9-11, 2020. Zentrale Tracks},\n  pages     = {364--368},\n  publisher = {{GITO} Verlag},\n  year      = {2020},\n  url       = {https://doi.org/10.30844/wi\\_2020\\_c12-weinzierl},\n  doi       = {10.30844/wi\\_2020\\_c12-weinzierl}\n}","code":"","contributions":[],"date":"2020-03-01","doi":"10.30844/wi_2020_c12-weinzierl","featured":false,"id":"2020-WI","markdown_url":"https://www.gyunam.com/publication/2020-wi/index.md","number":"","pages":"364–368","pdf":"","projects":[],"publisher":"GITO Verlag","research_lines":["prescriptive-process-analytics"],"series":"","summary":"","tags":["Conference Proceedings","Recommendation","WI2020"],"title":"From predictive to prescriptive process monitoring: Recommending the next best actions instead of calculating the next most likely events","type":"paper-conference","url":"https://www.gyunam.com/publication/2020-wi/","venue":"14th Internationale Tagung Wirtschaftsinformatik","venue_short":"","volume":"","year":2020},{"abstract":"Online operational support is gaining increasing interest due to the availability of real-time data and sufficient computing power, such as predictive business process monitoring. Predictive business process monitoring aims at providing timely information that enables proactive and corrective actions to improve process enactments and mitigate risks. There are a handful of research works focusing on the predictions at the instance level. However, it is more practical to predict the performance of processes at the process model level and detect potential weaknesses in the process to facilitate the proactive actions that will improve the process execution. Thus, in this paper, we propose a novel method to predict the future performances of a business process at the process model level. More in detail, we construct an annotated transition system and generate a process representation matrix from it. Based on the process representation matrix, we build performance prediction models using deep neural networks that consider both spatial and temporal dependencies present in the underlying business process. To validate the proposed method, we performed case studies on three real-life logs.","agent_summary":"","authors":["Gyunam Park","Minseok Song"],"bibtex":"@article{DBLP:journals/dss/ParkS20,\n  author    = {Gyunam Park and Minseok Song},\n  title     = {Predicting performances in business processes using deep neural networks},\n  journal   = {Decis. Support Syst.},\n  volume    = {129},\n  year      = {2020},\n  url       = {https://doi.org/10.1016/j.dss.2019.113191},\n  doi       = {10.1016/j.dss.2019.113191}\n}","code":"","contributions":["Formalizes an annotated transition system in which states and transitions carry multisets of waiting, processing, and sojourn time measurements obtained by replaying the event log.","Two process representation matrices: a two-dimensional matrix (model elements by time windows) and a three-dimensional matrix (from-state by to-state by time window) encoding directly-follows topology.","Three prediction models adapted from traffic congestion forecasting: a convolutional neural network (CNN), a long short-term memory network (LSTM), and a long-term recurrent convolutional network (LRCN).","Two baseline families for model-level performance prediction: per-element statistical regressors (linear regression, random forest, support vector regression) and nearest-snapshot search with Euclidean, Chebyshev, and cosine distance.","A Python implementation with source code and supplementary material released on GitHub (`gyunamister/performance_prediction`), evaluated on three real-life logs."],"date":"2020-01-02","doi":"10.1016/j.dss.2019.113191","featured":true,"id":"2020-DSS","markdown_url":"https://www.gyunam.com/publication/2020-dss/index.md","number":"","pages":"","pdf":"https://www.gyunam.com/publication/2020-dss/paper.pdf","projects":[],"publisher":"Elsevier","research_lines":["prescriptive-process-analytics"],"series":"","summary":"","tags":["Journal Article","Prediction","DSS"],"title":"Predicting performances in business processes using deep neural networks","type":"article-journal","url":"https://www.gyunam.com/publication/2020-dss/","venue":"Decision Support Systems","venue_short":"","volume":"129","year":2020},{"abstract":"Predictive business process monitoring aims at providing the predictions about running instances by analyzing logs of completed cases of a business process. Recently, a lot of research focuses on increasing productivity and efficiency in a business process by forecasting potential problems during its executions. However, most of the studies lack suggesting concrete actions to improve the process. They leave it up to the subjective judgment of a user. In this paper, we propose a novel method to connect the results from predictive business process monitoring to actual business process improvements. More in detail, we optimize the resource allocation in a non-clairvoyant online environment, where we have limited information required for scheduling, by exploiting the predictions. The proposed method integrates offline prediction model construction that predicts the processing time and the next activity of an ongoing instance using LSTM with online resource allocation that is extended from the minimum cost and maximum flow algorithm. To validate the proposed method, we performed experiments using an artificial event log and a real-life event log from a global financial organization.","agent_summary":"","authors":["Gyunam Park","Minseok Song"],"bibtex":"@inproceedings{DBLP:conf/icpm/ParkS19,\n  author    = {Gyunam Park and Minseok Song},\n  title     = {Prediction-based Resource Allocation using {LSTM} and Minimum Cost and Maximum Flow Algorithm},\n  booktitle = {International Conference on Process Mining, {ICPM} 2019, Aachen, Germany, June 24-26, 2019},\n  pages     = {121--128},\n  publisher = {{IEEE}},\n  year      = {2019},\n  url       = {https://doi.org/10.1109/ICPM.2019.00027},\n  doi       = {10.1109/ICPM.2019.00027}\n}","code":"","contributions":["Formulates resource allocation in business processes as a non-clairvoyant online job shop scheduling problem that minimizes total weighted completion time under limited information.","A two-phase method: offline training of a multi-task Long Short-Term Memory (LSTM) network that predicts processing time and next activity from one-hot encoded prefixes, and online scheduling driven by an event stream.","A scheduling algorithm that builds a bipartite graph of ready and predicted work items versus resources, with edge cost (processing time plus readiness penalty) divided by instance weight, and solves minimum cost maximum flow with the network simplex method.","Pseudo-assignments that reserve resources for predicted high-weight work items before they are released, followed by execution of the matches whose instance and resource are both available.","An evaluation against a WeightGreedy first-come-first-served baseline on a simulated emergency department log and the BPI Challenge 2012 loan application log."],"date":"2019-06-26","doi":"10.1109/ICPM.2019.00027","featured":false,"id":"2019-ICPM","markdown_url":"https://www.gyunam.com/publication/2019-icpm/index.md","number":"","pages":"121–128","pdf":"https://www.gyunam.com/publication/2019-icpm/paper.pdf","projects":[],"publisher":"IEEE","research_lines":["prescriptive-process-analytics"],"series":"","summary":"","tags":["Conference Proceedings","Recommendation","ICPM2019"],"title":"Prediction-based Resource Allocation using LSTM and Minimum Cost and Maximum Flow Algorithm","type":"paper-conference","url":"https://www.gyunam.com/publication/2019-icpm/","venue":"2019 International Conference on Process Mining","venue_short":"","volume":"","year":2019}],"kind":"publications","source":"https://www.gyunam.com/"}