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Parallel Flexible Heuristic Miner for Process Discovery

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Abstract

Process discovery aims to construct a business process model by extracting valuable information from event logs. To extract process models from event logs, it is essential to have a stable and scalable implementation of the computationally demanding process discovery techniques through parallel computing. This is because the amount of data contained in event logs is growing at an exponential rate. The current study bases its computing strategy on this subject and suggests using the OpenMP Application Programming Interface (API) to construct the Flexible Heuristic Miner (FHM) algorithm for process discovery. The performance of the suggested approach is evaluated based on the speedup factor through a number of different experiments. This was accomplished by conducting an in-depth analysis of the steps that are involved in the FHM algorithm. As a result of this analysis, a suitable parallel programming framework has been proposed to reduce the execution time by making use of data and task parallelism.

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References

  1. Aalst W, Adriansyah A, Medeiros AKA, Arcieri F, Baier T, Blickle T, Bose JC, Brand P, Brandtjen R, Buijs J, et al. Process mining manifesto. In: Business process management workshops. Springer; 2012. p. 169–194.

  2. Van Dongen B, Alves de Medeiros A, Wen L. Process mining: overview and outlook of petri net discovery algorithms. Transactions on petri nets and other models of concurrency II. 2009; p. 225–242.

  3. Günther C, van der Aalst W. Fuzzy mining-adaptive process simplification based on multi-perspective metrics. In: Business Process Management. 2007; p. 328–43.

  4. Rozinat A, Van der Aalst WM. Conformance checking of processes based on monitoring real behavior. Inf Syst. 2008;33(1):64–95.

    Article  Google Scholar 

  5. Van der Aalst WM. Process mining. Berlin, Heidelberg: Springer; 2011.

    Book  MATH  Google Scholar 

  6. van der Aalst WM, Verbeek HE. Process mining in web services: the websphere case. IEEE Data Eng Bull. 2008;31(3):45–8.

    Google Scholar 

  7. Tiwari A, Turner CJ, Majeed B. A review of business process mining: state-of-the-art and future trends. Bus Process Manage J. 2008;14(1):5–22.

    Article  Google Scholar 

  8. Verbeek H, van der Aalst W.M. Decomposed process mining: the ilp case. In: International conference on business process management. Springer; 2014. p. 264–76.

  9. Günther CW, Rozinat A. Disco: discover your processes. BPM (Demos). 2012;940:40–4.

    Google Scholar 

  10. Van der Aalst W, Weijters T, Maruster L. Workflow mining: discovering process models from event logs. IEEE Trans Knowl Data Eng. 2004;16(9):1128–42.

    Article  Google Scholar 

  11. Weijters AJ, Van der Aalst WM. Rediscovering workflow models from event-based data using little thumb. Integr Comput Aided Eng. 2003;10(2):151–62.

    Article  Google Scholar 

  12. Weijters A, van Der Aalst WM, De Medeiros AA.: Process mining with the heuristics miner-algorithm. Technische Universiteit Eindhoven Tech Rep WP. 2006;166:1–34

  13. Weijters A, Ribeiro J. Flexible heuristics miner (fhm). In: Computational intelligence and data mining (CIDM), 2011 IEEE symposium on. IEEE; 2011. pp. 310–17.

  14. Chapman B, Jost G, Van Der Pas R. Using OpenMP: portable shared memory parallel programming, vol. 10. Cambridge: MIT Press; 2008.

    Google Scholar 

  15. Wen L, van der Aalst WM, Wang J, Sun J. Mining process models with non-free-choice constructs. Data Min Knowl Discov. 2007;15(2):145–80.

    Article  MathSciNet  Google Scholar 

  16. Schimm G. Mining exact models of concurrent workflows. Comput Ind. 2004;53(3):265–81.

    Article  Google Scholar 

  17. Van Der Aalst WM, Rubin V, Verbeek HM, van Dongen BF, Kindler E, Günther CW. Process mining: a two-step approach to balance between underfitting and overfitting. Softw Syst Model. 2010;9(1):87–111.

    Article  Google Scholar 

  18. Bergenthum R, Desel J, Lorenz R, Mauser S. Process mining based on regions of languages. In: International conference on business process management. Springer; 2007. p. 375–83.

  19. Ferreira DR, Gillblad D. Discovering process models from unlabelled event logs. In: International conference on business process management. Springer; 2009. p. 143–158.

  20. Van der Aalst WM, Weijters A. Process mining: a research agenda. Comput Ind. 2004;53(3):231–44.

    Article  Google Scholar 

  21. de MEDEIROS AK, Weijters AJ, van der Aalst WM. Genetic process mining: an experimental evaluation. Data Min Knowl Discov. 2007;14(2):245–304.

    Article  MathSciNet  Google Scholar 

  22. Turner CJ, Tiwari A, Mehnen J. A genetic programming approach to business process mining. In: Proceedings of the 10th annual conference on genetic and evolutionary computation. ACM; 2008. p. 1307–14.

  23. Greco G, Guzzo A, Pontieri L, Sacca D. Discovering expressive process models by clustering log traces. IEEE Trans Knowl Data Eng. 2006;18(8):1010–27.

    Article  Google Scholar 

  24. Song M, Günther CW, Aalst WM. Trace clustering in process mining. In: Business process management workshops. Springer; 2009. p. 109–20.

  25. Bose RJC, van der Aalst WM. Context aware trace clustering: towards improving process mining results. In: Proceedings of the 2009 SIAM international conference on data mining. SIAM; 2009. p. 401–12 .

  26. Günther CW, Rozinat A, Van Der Aalst WM. Activity mining by global trace segmentation. In: International conference on business process management. Springer; 2009. p. 128–39.

  27. Goedertier S, Martens D, Vanthienen J, Baesens B. Robust process discovery with artificial negative events. J Mach Learn Res. 2009;10:1305–40.

    MathSciNet  MATH  Google Scholar 

  28. Allen LV, Tilbury DM. Anomaly detection using model generation for event-based systems without a preexisting formal model. IEEE Trans Syst Man Cybern Part A Syst Hum. 2012;42(3):654–68.

    Article  Google Scholar 

  29. Sun SX, Zeng Q, Wang H. Process-mining-based workflow model fragmentation for distributed execution. IEEE Trans Syst Man Cybern Part A Syst Hum. 2011;41(2):294–310.

    Article  Google Scholar 

  30. Van Der Aalst WM. Decomposing process mining problems using passages. In: International conference on application and theory of petri nets and concurrency. Springer; 2012. p. 72–91.

  31. Van der Aalst WM. Decomposing petri nets for process mining: a generic approach. Distrib Parallel Databases. 2013;31(4):471–507.

    Article  Google Scholar 

  32. Van der Aalst WM. Process mining in the large: a tutorial. Switzerland: Springer; 2014. p. 33–76.

  33. Van Der Aalst WM. A general divide and conquer approach for process mining. In: Computer science and information systems (FedCSIS), 2013 federated conference on. IEEE; 2013. p. 1–10.

  34. Burattin A, Sperduti A, van der Aalst WM. Heuristics miners for streaming event data. arXiv preprint. 2012. arXiv:1212.6383

  35. van Eck ML, Sidorova N, van der Aalst WM. Discovering and exploring state-based models for multi-perspective processes. In: International conference on business process management. Springer; 2016. p. 142–57.

  36. van der Aalst WM, Kalenkova A, Rubin V, Verbeek E. Process discovery using localized events. In: International conference on applications and theory of petri nets and concurrency. Springer; 2015. p. 287–308.

  37. Leemans M, van der Aalst WM. Process mining in software systems: discovering real-life business transactions and process models from distributed systems. In: Model driven engineering languages and systems (MODELS), 2015 ACM/IEEE 18th international conference on. IEEE; 2015. p. 44–53.

  38. Jablonski S, Röglinger M, Schönig S, Wyrtki KM. Multi-perspective clustering of process execution traces. Enterprise Model Inf Syst Archit (EMISAJ). 2019;14:1–2.

    Google Scholar 

  39. Andrews R, Goel K, Corry P, Burdett R, Wynn MT, Callow D. Process data analytics for hospital case-mix planning. J Biomed Inf. 2022;129: 104056.

    Article  Google Scholar 

  40. Goel K, Leemans SJ, Martin N, Wynn MT. Quality-informed process mining: a case for standardised data quality annotations. ACM Trans Knowl Discov Data (TKDD). 2022;16(5):1–47.

    Article  Google Scholar 

  41. Issahaku F.-l.Y, Fang X, Bashiru Danwana S, Bankas EK, Lu K. A novel process of parsing event-log activities for process mining based on information content. Electronics. 2023;12(2):289.

    Article  Google Scholar 

  42. Wijnhoven F, Hoffmann P, Bemthuis R, Boksebeld J. Using process mining for workarounds analysis in context: learning from a small and medium-sized company case. Int J Inf Manage Data Insights. 2023;3(1): 100163.

    Google Scholar 

  43. Pacheco P. An introduction to parallel programming. Elsevier, Burlington, Massachusetts: Morgan Kaufmann; 2021.

    Google Scholar 

  44. Quinn MJ. Parallel programming in C with MPI and OpenMP. New York: Mc-Graw Hill; 2003.

    Google Scholar 

  45. Grama A. Introduction to parallel computing. Boston: Addison-Wesley Professional, Pearson Education; 2003.

    Google Scholar 

  46. Hennessy JL, Patterson DA. Computer architecture-a quantitative approach. Burlington, Massachusetts: Morgan Kaufmann, Elsevier; 2003.

    MATH  Google Scholar 

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Funding

This study is not funded by any organization or institute.

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Author notes

  1. Palak Lunia and Sachi Nandan have contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science and Engineering, IIIT Bhubaneswar, Gothapatna, Bhubaneswar, Odisha, 751003, India

    Muktikanta Sahu & Palak Lunia

  2. School of Computer Science and Engineering, VIT-AP University, Beside AP Secretariat Amaravati, Amaravati, Andhra Pradesh, 522237, India

    Sachi Nandan Mohanty

Authors
  1. Muktikanta Sahu
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  2. Palak Lunia
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  3. Sachi Nandan Mohanty
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Correspondence to Muktikanta Sahu.

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This article is part of the topical collection “Research Trends in Communication and Network Technologies” guest edited by Anshul Verma, Pradeepika Verma and Kiran Kumar Pattanaik.

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Sahu, M., Lunia, P. & Mohanty, S.N. Parallel Flexible Heuristic Miner for Process Discovery. SN COMPUT. SCI. 4, 524 (2023). https://doi.org/10.1007/s42979-023-01948-1

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