Practical problem
Before anyone can predict, recommend or simulate, they need an accurate picture of how a process actually runs. Discovered models are either sound but too permissive, because block-structured miners cannot express real branching, or faithful to branching but unsound. Variant lists and performance views drown analysts in detail: thousands of variants, dense timelines in which batching and reordering hide among noise, and deviation scores that ignore the context, such as a pandemic wave or a staff shortage, that explains them. In manufacturing, which machines a product passes through matters for yield and quality, yet single-resource statistics are confounded by the many paths through hundreds of interchangeable machines. This line develops the descriptive and diagnostic side of process mining: discovery, model decomposition, performance and deviation diagnostics, and resource analysis. It started in industry projects with Samsung Electronics (2017 to 2020) on resource mining in semiconductor manufacturing and now centres on the Partially Ordered Workflow Language. It is the counterpart of prescriptive process analytics, which acts on the insight.
Research questions
The line has three pillars.
Sound yet expressive discovery. Soundness guarantees and expressiveness have been traded against each other.
- How can process models be discovered that are guaranteed sound and still express non-block-structured decisions and loops, and how can existing workflow nets be reused in such a hierarchical language?
- How can large models be decomposed into coherent fragments so that conformance checking scales and diagnoses point to a recognisable subprocess?
Diagnostics. Patterns, deviations and resource effects should be detected rather than read off by an expert.
- How can temporal performance patterns such as batching and reordering, and deviations that depend on context, be detected automatically?
- Which resources and resource paths explain yield and quality differences when products pass through hundreds of interchangeable machines?
Non-classical traces. Process mining applies beyond business process logs.
- Which behaviours become visible when process mining is applied to learners’ click streams or to the reasoning steps of large language models?
Contributions
The contributions so far fall into five groups, each linked to the publication in which it appears.
Expressive yet sound discovery with the Partially Ordered Workflow Language.
- Choice graphs, a generalization of the exclusive-choice operator to non-block-structured decisions, the POWL 2.0 language and an Inductive Miner extension with a choice graph cut that provably preserves the fitness guarantee, in the powl Python library (BPM 2025).
- A recursive algorithm that translates safe and sound workflow nets into POWL by identifying exclusive-choice, loop and partial-order patterns, with proofs that each step preserves soundness and language and completeness on the class of semi-block-structured nets (Petri Nets 2025).
- A robust inductive discovery algorithm for POWL 2.0 with several mining strategies for choice graphs that range in strictness to resolve the ambiguity between genuine loops and hidden concurrency (Process Science, 2026).
Decomposition and conformance.
- Isolated subnets of a workflow net, an integer linear programming formulation that extracts them around a seed transition into a hierarchical decomposition, proofs that the sub-workflow nets inherit soundness and free-choiceness, and a construction of valid decompositions for divide-and-conquer conformance checking (CAiSE 2026).
- Conformance checking and performance analysis on object-centric directly-follows graphs, aligning per object type and mapping moves back onto the graph, in the Explori tool (BPM Forum 2024).
Performance and deviation diagnostics.
- PerSpect, an interactive tool that reimplements the Performance Spectrum as the basis of a pattern-oriented workflow, with automated batch detection by DBSCAN clustering, propagating filters and side-by-side comparison of spectra (CAiSE Forum 2026).
- Context-aware deviation detection as a four-class labeling problem, a context ontology with intrinsic, relational and external contexts, and a post-processing framework that plugs in any score-based detector, in the contect web service (BPM Forum 2022).
Resource analysis in manufacturing.
- Resource-oriented transition systems for yield enhancement: a finite state machine whose states are the machines of each process step, overlaid with best- and worst-case wafer paths and simplified by statistical tests, evaluated on data from one of the largest Korean semiconductor manufacturers (IEEE Transactions on Semiconductor Manufacturing, 2021).
- Quality-aware resource model discovery, a five-step method that ranks resources as superior, inferior or moderate from case-annotated transition systems, with a Python tool for interactive analysis (Applied Sciences, 2021).
Process mining on non-classical traces.
- A taxonomy of nine learning cohorts derived from engagement, direction, repetitiveness and orderliness, with learners mapped to cohorts by dynamic time warping clustering and trend tests on 18,405 learners of an online education platform (Education and Information Technologies, 2024).
- PMLRM-Bench and a reasoning analysis pipeline that classifies the chain-of-thought steps of 17 large reasoning models by type and effect, with a case study that reconfigures a model through system prompts (BPM Workshops 2025).