Leveraging machine learning for automatic topic discovery and forecasting of process mining research: A literature review
Gyunam Park, Minsu Cho, Jiyoon Lee
January, 2024Abstract
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.
Publication
Expert Systems with Applications
Practical problem
Researchers, students, and practitioners entering process mining face a literature of thousands of papers spread across many venues and need to know which topics dominate, which are rising, and how they connect. Existing reviews are written by groups of experts who pick topics and judge trends by hand, so the results depend on the reviewers’ perspective and offer no systematic way to project where the field is heading. Companies deciding where to invest and tool vendors deciding what to build likewise lack a data-based map of the discipline. A quantitative, reproducible method that treats the literature itself as data can reduce this bias and support forecasting.
Research questions
- What are the main research topics of process mining, and how have their trends changed over time?
- Which process mining topics are mutually cooperative, and how does each research topic relate to the others?
- How much will process mining expand in the near future, and which research topics will be the focus?
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.
Key findings
- Process discovery (451 articles), conformance checking (136) and frameworks and tools are dominant fields; process monitoring, predictive process monitoring, privacy-preserving process mining and event logs are skyrocketing; process model comparison is deep-cut.
- Healthcare is the largest application domain (267 articles); 68% of publications are conference proceedings; the Netherlands and China account for 12.9% and 10.7% of all papers.
- Louvain community detection yields four communities (representative, resource-oriented, performance-oriented, manufacturing and Internet of Things) plus six isolated topics; process discovery and frameworks and tools are the central connectors.
- The LSTM forecaster reaches a root mean squared error of 5.63 ± 1.02 and mean absolute error of 3.98 ± 0.55 under time-series cross-validation, and predicts growth for most fields in 2022 except variant analysis and frameworks and tools.
- Limitations: document preprocessing and field naming still need manual expert steps, and topics the topic model does not surface, notably object-centric process mining, are absent from the trend and forecast analysis.