mlsanityData to 14 Oct 2026

Tailoring Machine Learning for Process Mining

arXiv 2306.10341 ↗First posted 17 Jun 2023cs.LG cs.AI cs.DBcited by 4

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Abstract (arXiv)

Machine learning models are routinely integrated into process mining pipelines to carry out tasks like data transformation, noise reduction, anomaly detection, classification, and prediction. Often, the design of such models is based on some ad-hoc assumptions about the corresponding data distributions, which are not necessarily in accordance with the non-parametric distributions typically observed with process data. Moreover, the learning procedure they follow ignores the constraints concurrency imposes to process data. Data encoding is a key element to smooth the mismatch between these assumptions but its potential is poorly exploited. In this paper, we argue that a deeper insight into the issues raised by training machine learning models with process data is crucial to ground a sound integration of process mining and machine learning. Our analysis of such issues is aimed at laying the foundation for a methodology aimed at correctly aligning machine learning with process mining requirements and stimulating the research to elaborate in this direction.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 24 of its 116 references that are in the map. First posted on arXiv 17 Jun 2023.

Builds on

24 of 116 references.

References · 24

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12 more
  1. 2021A systematic literature review on state-of-the-art deep learning methods for process predictioncited by 32
  2. 2020Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Predictioncited by 31
  3. 2023Large Language Models for Business Process Management: Opportunities and Challengescited by 24
  4. 2020Process Mining Meets Causal Machine Learning: Discovering Causal Rules from Event Logscited by 15
  5. 2016Event Abstraction for Process Mining using Supervised Learning Techniquescited by 9
  6. 2021One Model to Rule them All: Towards Zero-Shot Learning for Databasescited by 9
  7. 2020Low-Rank Covariance Function Estimation for Multidimensional Functional Datacited by 8
  8. 2020Handling Concept Drift for Predictions in Business Process Miningcited by 7
  9. 2020Adversarial System Variant Approximation to Quantify Process Model Generalizationcited by 6
  10. 2023Trace Encoding in Process Mining: a survey and benchmarkingcited by 6
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  12. 2023An End-to-End Approach for Online Decision Mining and Decision Drift Analysis in Process-Aware Information Systems: Extended Versioncited by 2

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Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoringcited by 5
  2. 2025HGCN(O): A Self-Tuning GCN HyperModel Toolkit for Outcome Prediction in Event-Sequence Datacited by 4
  3. 2025Leveraging GPT-4o Efficiency for Detecting Rework Anomaly in Business Processescited by 0
  4. 2025Leveraging Duration Pseudo-Embeddings in Multilevel LSTM and GCN Hypermodels for Outcome-Oriented PPMcited by 0