Item type:Journal Article,

Generating Artificial Data for Empirical Analysis of Control-flow Discovery Algorithms

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Date

2019, 2019

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Business & Information Systems Engineering: Vol. 61, No. 6

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Springer

Abstract

Within the process mining domain, research on comparing control-flow (CF) discovery techniques has gained importance. A crucial building block of empirical analysis of CF discovery techniques is obtaining the appropriate evaluation data. Currently, there is no answer to the question of how to collect such evaluation data. The paper introduces a methodology for generating artificial event data (GED) and an implementation called the Process Tree and Log Generator. The GED methodology and its implementation provide users with full control over the characteristics of the generated event data and an integration within the ProM framework. Unlike existing approaches, there is no tradeoff between including long-term dependencies and soundness of the process. The contributions of the paper provide a solution for a necessary step in the empirical analysis of CF discovery algorithms.

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Jouck, Toon; Depaire, Benoît (2019): Generating Artificial Data for Empirical Analysis of Control-flow Discovery Algorithms. Business & Information Systems Engineering: Vol. 61, No. 6. DOI: 10.1007/s12599-018-0541-5. Springer. PISSN: 1867-0202. pp. 695-712

Keywords

Artificial event logs, Empirical analysis, Process discovery

Citation

DOI

10.1007/s12599-018-0541-5

URI

URI

http://dx.doi.org/10.1007/s12599-018-0541-5
 https://dl.gi.de/handle/20.500.12116/30647

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BISE 61(6) - December 2019

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Number of citations to item: 16

  • Dominique Sommers, Natalia Sidorova, Boudewijn F. van Dongen (2024): Assessing Process Mining Techniques: a Ground Truth Approach, In: 2024 6th International Conference on Process Mining (ICPM), doi:10.1109/icpm63005.2024.10680619
  • Andrea Maldonado, Christian M. M. Frey, Gabriel Marques Tavares, Nikolina Rehwald, Thomas Seidl (2024): GEDI: Generating Event Data with Intentional Features for Benchmarking Process Mining, In: Lecture Notes in Computer Science, doi:10.1007/978-3-031-70396-6_13
  • Ariba Siddiqui, Wil M. P. van der Aalst, Daniel Schuster (2025): Computing Alignments for Partially-Ordered Traces Through Petri Net Unfoldings, In: Lecture Notes in Computer Science, doi:10.1007/978-3-031-94634-9_20
  • Greg Van Houdt, Massimiliano de Leoni, Niels Martin, Benoît Depaire (2024): An empirical evaluation of unsupervised event log abstraction techniques in process mining, In: Information Systems, doi:10.1016/j.is.2023.102320
  • Sebastiaan J. van Zelst, Sander J. J. Leemans (2020): Translating Workflow Nets to Process Trees: An Algorithmic Approach, In: Algorithms 11(13), doi:10.3390/a13110279
  • Humam Kourani, Gyunam Park, Wil M. P. van der Aalst (2025): Translating Workflow Nets into the Partially Ordered Workflow Language, In: Lecture Notes in Computer Science, doi:10.1007/978-3-031-94634-9_12
  • Wil M. P. van der Aalst, Jörg Becker, Martin Bichler, Hans Ulrich Buhl, Jens Dibbern, Ulrich Frank, Ulrich Hasenkamp, Armin Heinzl, Oliver Hinz, Kai-Lung Hui, Matthias Jarke, Dimitris Karagiannis, Natalia Kliewer, Wolfgang König, Jan Mendling, Peter Mertens, Matti Rossi, Stefan Voss, Christof Weinhardt, Robert Winter, Jelena Zdravkovic (2018): Views on the Past, Present, and Future of Business and Information Systems Engineering, In: Business & Information Systems Engineering 6(60), doi:10.1007/s12599-018-0561-1
  • Julian Theis, Houshang Darabi (2020): Adversarial System Variant Approximation to Quantify Process Model Generalization, In: IEEE Access, doi:10.1109/access.2020.3033450
  • Xinjian Fang, Duoqin Li, Ke Lu (2024): Optimal alignment of business process models based on log probability distribution, doi:10.21203/rs.3.rs-3852504/v1
  • Marco Comuzzi, Jonghyeon Ko, Fabrizio Maggi (2025): A Language to Model and Simulate Data Quality Issues in Process Mining, In: Journal of Data and Information Quality 2(17), doi:10.1145/3743144
  • Andrea Burattin, Barbara Re, Lorenzo Rossi, Francesco Tiezzi (2022): A Purpose-Guided Log Generation Framework, In: Lecture Notes in Computer Science, doi:10.1007/978-3-031-16103-2_14
  • Toon Jouck, Massimiliano de Leoni, Benoît Depaire (2019): A Framework to Evaluate and Compare Decision-Mining Techniques, In: Lecture Notes in Business Information Processing, doi:10.1007/978-3-030-11641-5_38
  • Florian Baer, Kurt Sandkuhl, Michael Leyer, Birger Lantow (2020): DESERV IT: A Method for Devolving Service Tasks in IT Services, In: Business & Information Systems Engineering 4(63), doi:10.1007/s12599-020-00655-y
  • Shameer K. Pradhan, Mieke Jans, Niels Martin (2025): Getting the Data in Shape for Your Process Mining Analysis: An In-Depth Analysis of the Pre-Analysis Stage, In: ACM Computing Surveys 6(57), doi:10.1145/3712587
  • Dominique Sommers, Natalia Sidorova, Boudewijn van Dongen (2025): A ground truth approach for assessing process mining techniques, In: Process Science 1(2), doi:10.1007/s44311-025-00006-8
  • Manal Laghmouch, Benoît Depaire, Nicola Gigante, Mieke Julie Jans, Marco Montali (2025): Generating and Specializing Ground Truth Declarative Process Models for Process Mining Algorithm Evaluation, doi:10.2139/ssrn.5178728
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