On the Discovery of Explainable and Accurate Behavioral Models for Complex Lowly-structured Business Processes

Francesco Folino, Massimo Guarascio, Luigi Pontieri

Abstract

Process discovery (i.e. the automated induction of a behavioral process model from execution logs) is an important tool for business process analysts/managers, who can exploit the extracted knowledge in key process improvement and (re-)design tasks. Unfortunately, when directly applied to the logs of complex and/or lowly-structured processes, such techniques tend to produce low-quality workflow schemas, featuring both poor readability ("spaghetti-like") and low fitness (i.e. low ability to reproduce log traces). Trace clustering methods alleviate this problem, by helping detect different execution scenarios, for which simpler and more fitting workflow schemas can be eventually discovered. However, most of these methods just focus on the sequence of activities performed in each log trace, without fully exploiting all non-structural data (such as cases data and environmental variables) available in many real logs, which might well help discover more meaningful (context-related) process variants. In order to overcome these limitations, we propose a two-phase clustering-based process discovery approach, where the clusters are inherently defined through logical decision rules over context data, ensuring a satisfactory trade-off is between the readability/explainability of the discovered clusters, and the behavioral fitness of the workflow schemas eventually extracted from them. The approach has been implemented in a system prototype, which supports the discovery, evaluation and reuse of such multi-variant process models. Experimental results on a real-life log confirmed its capability to achieve compelling performances w.r.t. state-of-the-art clustering approaches, in terms of both fitness and explainability.

References

  1. Alves de Medeiros, A. K., van der Aalst, W. M. P., and Weijters, A. J. M. M. (2008). Quantifying process equivalence based on observed behavior. Data & Knowledge Engineering, 64(1):55-74.
  2. Blockeel, H. and Raedt, L. D. (1998). Top-down induction of first-order logical decision trees. Artificial Intelligence, 101(1-2):285-297.
  3. Bose, R. P. J. C. and van der Aalst, W. M. P. (2009a). Context aware trace clustering: Towards improving process mining results. In Proc. of SIAM International Conference on Data Mining, pages 401-412.
  4. Bose, R. P. J. C. and van der Aalst, W. M. P. (2009b). Trace clustering based on conserved patterns: Towards achieving better process models. In Business Process Management Workshops, pages 170-181.
  5. Buijs, J., van Dongen, B., and van der Aalst, W. (2012). On the role of fitness, precision, generalization and simplicity in process discovery. In On the Move to Meaningful Internet Systems: OTM 2012, volume 7565, pages 305-322.
  6. Cramer, H. (1999). Mathematical Methods of Statistics. Princeton University Press.
  7. de Medeiros, A. A. (2006). Genetic Process Mining. Phd thesis, Eindhoven University of Technology.
  8. De Weerdt, J., van den Broucke, S., Sand Vanthienen, J., and Baesens, B. (2013). Active trace clustering for improved process discovery. IEEE Trans. on Knowl. and Data Eng., 25(12):2708-2720.
  9. Ferreira, D., Zacarias, M., Malheiros, M., and Ferreira, P. (2007). Approaching process mining with sequence clustering: Experiments and findings. In Proc. of 5th Int. Conf. on Business Process Management (BPM'07), pages 360-374.
  10. Folino, F., Greco, G., Guzzo, A., and Pontieri, L. (2008). Discovering multi-perspective process models: The case of loosely-structured processes. In ICEIS 2008, Revised Selected Papers, pages 130-143.
  11. Folino, F., Greco, G., Guzzo, A., and Pontieri, L. (2011). Mining usage scenarios in business processes: Outlier-aware discovery and run-time prediction. Data & Knowledge Engineering, 70(12):1005- 1029.
  12. Folino, F., Guarascio, M., and Pontieri, L. (2012). Discovering context-aware models for predicting business process performances. In Proc. of 20th Intl. Conf. on Cooperative Information Systems (CoopIS'12), pages 287-304.
  13. Greco, G., Guzzo, A., Pontieri, L., and Saccà, D. (2006). Discovering expressive process models by clustering log traces. IEEE Trans. Knowl. and Data Eng., 18(8).
  14. Kunze, M., Weidlich, M., and Weske, M. (2011). Behavioral similarity: A proper metric. In Proc. of 9th Int. Conf. on Business Process Management (BPM'11), pages 166-181.
  15. Rozinat, A. and van der Aalst, W. M. P. (2008). Conformance checking of processes based on monitoring real behavior. Information Systems, 33(1):64-95.
  16. Song, M., Günther, C. W., and van der Aalst, W. (2008). Trace clustering in process mining. In Proc. of Business Process Management Workshops (BPI'08), pages 109-120.
  17. Steeman, W. (2013). BPI challenge 2013, closed problems.
  18. van der Aalst, W. (1998). The application of Petri nets to worflow management. Journal of Circuits, Systems, and Computers, 8(1):21-66.
  19. van Der Aalst, W., Adriansyah, A., and van Dongen, B. (2011). Causal nets: a modeling language tailored towards process discovery. In Proc. of the 22nd Intl. Conf. on Concurrency Theory, CONCUR'11, pages 28-42, Berlin, Heidelberg. Springer-Verlag.
  20. van der Aalst, W. M. P., van Dongen, B. F., Herbst, J., Maruster, L., Schimm, G., and Weijters, A. J. M. M. (2003). Workflow mining: a survey of issues and approaches. Data & Knowledge Engineering, 47(2):237-267.
  21. Weidlich, M., Polyvyanyy, A., Desai, N., Mendling, J., and Weske, M. (2011). Process compliance analysis based on behavioural profiles. Information Systems, 36(7):1009-1025.
  22. Weijters, A. J. M. M. and Ribeiro, J. T. S. (2011). Flexible heuristics miner (FHM). In Proc. of IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2011), pages 310-317.
  23. Weijters, A. J. M. M. and van der Aalst, W. M. P. (2003). Rediscovering workflow models from event-based data using little thumb. Integrated Computer-Aided Engineering, 10(2):151-162.
Download


Paper Citation


in Harvard Style

Folino F., Guarascio M. and Pontieri L. (2015). On the Discovery of Explainable and Accurate Behavioral Models for Complex Lowly-structured Business Processes . In Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-096-3, pages 206-217. DOI: 10.5220/0005470602060217


in Bibtex Style

@conference{iceis15,
author={Francesco Folino and Massimo Guarascio and Luigi Pontieri},
title={On the Discovery of Explainable and Accurate Behavioral Models for Complex Lowly-structured Business Processes},
booktitle={Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2015},
pages={206-217},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005470602060217},
isbn={978-989-758-096-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - On the Discovery of Explainable and Accurate Behavioral Models for Complex Lowly-structured Business Processes
SN - 978-989-758-096-3
AU - Folino F.
AU - Guarascio M.
AU - Pontieri L.
PY - 2015
SP - 206
EP - 217
DO - 10.5220/0005470602060217