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Method for Building UML Activity Diagrams from Event Logs

https://doi.org/10.15514/ISPRAS-2019-31(4)-9

Abstract

UML Activity Diagrams are widely used models for representing software processes. Models built from event logs, recorded by information systems, can provide valuable insights into real flows in processes and suggest ways of improving those systems. This paper proposes a novel method for mining UML Activity Diagrams from event logs. The method is based on a framework that consists of three nested stages involving a set of model transformations. The initial model is inferred from an event log using one of the existing mining algorithms. Then the model, if necessary, is transformed into an intermediate form and, finally, converted into the target UML Activity Diagram by the newly proposed algorithm. The transforming algorithms, except one used at the last stage, are parameters of the framework and can be adjusted based on needed or available models. The paper provides examples of the approach application on real life event logs.

About the Authors

Natalia Sergeyevna Zubkova
National Research University Higher School of Economics
Russian Federation
Student enrolled in the «Software Engineering» bachelor’s program, faculty of Computer Science


Sergey Andreevitch Shersakov
National Research University Higher School of Economics
Russian Federation
Research fellow at PAIS Lab of the Faculty of Computer Science


References

1. Van der Aalst W. Data science in action. In Process Mining, Springer, 2016, pp. 3-23.

2. Van Der Aalst W.M.P., Van Dongen B.F. Discovering petri nets from event logs. Lecture Notes in Computer Science, vol. 7480, 2013, pp. 372-422.

3. Van der Aalst W., Rubin V., Verbeek H., van Dongen B., Kindler E., Günther C. Process mining: a two-step approach to balance between underfitting and overfitting. Software and Systems Modeling, vol. 9, no. 1, 2010, pp. 87-111.

4. Agarwal B. Transformation of UML activity diagrams into Petri nets for verification purposes. International Journal of Engineering and Computer Science, vol. 2, no. 3, 2013, pp. 798-805.

5. Arlow J., Neustadt I. UML 2 and the unified process: practical object-oriented analysis and design. Pearson Education, 2005, 624 p.

6. Badouel E., Bernardinello L., Darondeau P. Polynomial algorithms for the synthesis of bounded nets. Lecture Notes in Computer Science, vol. 915, 1995, pp. 364-378.

7. Buijs J.C.A.M., van Dongen B.F., van der Aalst W. M. P. On the role of fitness, precision, generalization and simplicity in process discovery. Lecture Notes in Computer Science, vol. 7565, 2012. pp. 305-322.

8. Carmona J., Cortadella J., Kishinevsky M. A region-based algorithm for discovering Petri nets from event logs. Lecture Notes in Computer Science, vol. 5240, 2008, pp. 358-373.

9. Concurrency in UML. Available at: https://www.omg.org/ocup-2/documents/concurrency_in_uml_version_2.6.pdf. Accessed: 2019-03-05.

10. Cortadella J. et al. Deriving Petri nets from finite transition systems. IEEE Transactions on Computers, vol. 47, no. 8, 1998, pp. 859-882.

11. Davydova K.V., Shershakov S.A. Mining hybrid UML models from event logs of SOA systems. Trudy ISP RAN/Proc. ISP RAS, vol. 29, issue 4, 2017, pp. 155-174. DOI: 10.15514/ISPRAS-2017-29(4)-10.

12. Eshuis R., Wieringa R. A comparison of Petri net and activity diagram variants. In Proc. of the 2nd Int. Coll. on Petri Net Technologies for Modelling Communication Based Systems, 2001, pp. 93-104.

13. Fahland D. Translating uml2 activity diagrams to petri nets. Informatik-Berichte 226, Humboldt-Universitat zu Berlin, 2008.

14. Kalenkova A., van der Aalst W., Lomazova I., Rubin V. Process mining using BPMN: relating event logs and process models, Software and Systems Modeling, 2017, vol. 16, no. 4, pp. 1019-1048.

15. Leemans S.J.J., Fahland D., van der Aalst W.M.P. Discovering block-structured process models from event logs-a constructive approach. Lecture Notes in Computer Science, vol. 2472, 2013, pp. 311-329.

16. Shershakov S.A., Kalenkova A.A., Lomazova I.A. Transition systems reduction: balancing between precision and simplicity. Lecture Notes in Computer Science, vol. 10470, 2017, pp. 119-139.

17. Shunin T., Zubkova N., Shershakov S. Neural Approach to the Discovery Problem in Process Mining. Lecture Notes in Computer Science, vol. 11179, 2018, pp. 261-273.

18. UML specification. Available at: https://www.omg.org/spec/UML/About-UML/. Accessed: 2019-03-01.

19. Weijters A., van Der Aalst W., De Medeiros A.K.A. Process mining with the heuristics miner-algorithm. Technische Universiteit Eindhoven, Tech. Rep. WP, 2006, 34 p.

20. Van der Werf J. M. E. M, van Dongen, B. F., Hurkens, C. A., Serebrenik, A. Process mining with the heuristics miner-algorithm. Lecture Notes in Computer Science, vol. 5062, 2008, pp. 368-387.


Review

For citations:


Zubkova N.S., Shersakov S.A. Method for Building UML Activity Diagrams from Event Logs. Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2019;31(4):139-150. https://doi.org/10.15514/ISPRAS-2019-31(4)-9



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ISSN 2079-8156 (Print)
ISSN 2220-6426 (Online)