Iskra: A Tool for Process Model Repair
https://doi.org/10.15514/ISPRAS-2015-27(3)-16
Abstract
This paper is dedicated to a tool whose aim is to facilitate process mining experiments and evaluation of the repair algorithms. Process mining is a set of approaches which provides solutions and algorithms for discovery and analysis of business process models based on event logs. Process mining has three main areas of interest: model discovery, conformance checking and enhancement. The paper focuses on the latter. The goal of enhancement process is to refine an existing process model in order to make it adhere event logs. The particular approach of enhancement considered in the paper is called decomposed model repair. Although the paper is devoted to the implementation part of the approach, theoretical preliminaries essential for domain understanding are provided. Moreover, a typical use case of the tool is shown as well as guides to extending the tool and enriching it with extra algorithms and functionality. Finally, other solutions which can be used for implementation of repair schemes are considered, pros and cons of using them are mentioned.
About the Authors
I. Shugurov
Laboratory of Process-Aware Information Systems, National Research University Higher School of Economics
Russian Federation
Russian Federation
A. Mitsyuk
Laboratory of Process-Aware Information Systems, National Research University Higher School of Economics
Russian Federation
Russian Federation
References
1. Wil M. P. van der Aalst. Process mining: discovery, conformance and enhancement of business processes. Springer, 2011.
2. IEEE Task Force on Process Mining. Process mining manifesto. Business Process Management Workshops, ser. Lecture Notes in Business Information Processing, F. Daniel, K. Barkaoui, and S. Dustdar, Eds., vol. 99. Springer-Verlag, Berlin, 2012, pp. 169-194.
3. W. M. P. van der Aalst, A. Adriansyah, and B. F. van Dongen. Replaying history on process models for conformance checking and performance analysis. Wiley Interdisc. Rew.: Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 182-192, 2012.
4. A. Rozinat and W. M. van der Aalst. Conformance checking of processes based on monitoring real behavior. Information Systems, vol. 33, no. 1, pp. 64-95, 2008.
5. D. Fahland and W. van der Aalst. Repairing process models to reflect reality. Business Process Management, ser. Lecture Notes in Computer Science, A. Barros, A. Gal, and E. Kindler, Eds. Springer Berlin Heidelberg, 2012, vol. 7481, pp. 229-245.
6. W. M. P. van der Aalst. Decomposing petri nets for process mining: A generic approach. Distributed and Parallel Databases, vol. 31, no. 4, pp. 471-507, 2013.
7. W. M. Van Der Aalst. A general divide and conquer approach for process mining. Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on. IEEE, 2013, pp. 1-10.
8. Prom framework. [Online]. Available: http://www.promtools.org/doku.php, accessed 2015-06-25.
9. F. Buschmann, R. Meunier, H. Rohnert, P. Sommerlad, and M. Stal. Pattern-oriented Software Architecture: A System of Patterns. New York, NY, USA: John Wiley & Sons, Inc., 1996.
10. W. van der Aalst, T. Weijters, and L. Maruster. Workflow mining: Discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 9, pp. 1128-1142, 2004.
11. J. van der Werf, B. van Dongen, C. Hurkens, and A. Serebrenik. Process discovery using integer linear programming. Applications and Theory of Petri Nets, ser. Lecture Notes in Computer Science, K. van Hee and R. Valk, Eds. Springer Berlin Heidelberg, 2008, vol. 5062, pp. 368-387.
12. W. van der Aalst, A. de Medeiros, and A. Weijters. Genetic process mining. Applications and Theory of Petri Nets 2005, ser. Lecture Notes in Computer Science, G. Ciardo and P. Darondeau, Eds. Springer Berlin Heidelberg, 2005, vol. 3536, pp. 48-69.
13. A. Weijters, W. M. van Der Aalst, and A. A. De Medeiros. Process mining with the heuristics miner-algorithm. Technische Universiteit Eindhoven, Tech. Rep. WP, vol. 166, pp. 1-34, 2006.
14. S. Leemans, D. Fahland, and W. van der Aalst. Discovering blockstructured process models from incomplete event logs. Application and Theory of Petri Nets and Concurrency, ser. Lecture Notes in Computer Science, G. Ciardo and E. Kindler, Eds. Springer International Publishing, 2014, vol. 8489, pp. 91-110.
15. J. Munoz-Gama, J. Carmona, and W. M. van der Aalst. Single-entry single-exit decomposed conformance checking. Information Systems, vol. 46, pp. 102 - 122, 2014.
16. E. Gamma, R. Helm, R. Johnson, and J. Vlissides. Design Patterns: Elements of Reusable Object-oriented Software. Boston, MA, USA: Addison-Wesley Longman Publishing Co., Inc., 1995.
17. W. van der Aalst. Decomposing process mining problems using passages. Application and Theory of Petri Nets, ser. Lecture Notes in Computer Science, S. Haddad and L. Pomello, Eds. Springer Berlin Heidelberg, 2012, vol. 7347, pp. 72-91.
18. Beck. Test Driven Development: By Example. Boston, MA, USA: Addison-Wesley Longman Publishing Co., Inc., 2002.
19. Rapidprom. [Online]. Available: http://www.rapidprom.org/, accessed 2015-06-25.
20. Rapidminer. [Online]. Available: https://rapidminer.com/, accessed 2015-06-25.
21. S. Shershakov. DPMine/C: C++ library and graphical frontend for DPMine workflow language. Proceedings of the Spring/Summer Young Researchers Colloquium on Software Engineering, vol. 8, 2014.
Review
For citations:
Shugurov I., Mitsyuk A. Iskra: A Tool for Process Model Repair. Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2015;27(3):237-254. (In Russ.) https://doi.org/10.15514/ISPRAS-2015-27(3)-16
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