Process discovery method for distributed software systems with web interface

: pp. 70-77
Lviv Polytechnic National University, Lviv, Ukraine
Lviv Polytechnic National University, Institute of Computer Sciences and Information Technologies, ACS Department

As an applied discipline, process mining emerged about two decades ago, and its methods have been increasingly used in practice for recent few years. What differentiates process mining from the conventional data mining is considering process nature of the analyzed data. Rapid development of the process mining software market niche has risen relevance of such task as scalability of process mining methods. Adaptation of the process discovery method, called Fuzzy Miner, to distributed software systems with web interface has been proposed by the authors. To address the scalability requirements, the calculation procedures are implemented on different part of the system: the most computer resource consuming algorithms are executed on the server side whilst less resource consuming calculations are placed on the client side. In turns, the server-side components belong either to the data layer or service layer. The data layer is accountable for storing event data in XES format and measuring process metrics. Building a process graph and communication with the client web application is the responsibility of the service layer. The purpose of the client-side web application is to render a process graph generated in the server-side. The calculation logic is covered with unit and integration tests so that its correctness is checked automatically. In order to reduce total cost of ownership of the system, it is implemented with free software. From the performed calculations and comparison of the outcomes with the results received by means of the existing ProM 6.8 plugins (Fuzzy Miner and Alpha++ Miner), it can be concluded that the proposed adaptation of the Fuzzy Miner method ensures representation of the behavior seen in an event log (like the ProM 6.8 plugins successfully do). In turns, from the software architecture standpoint, the proposed solution demonstrates better scalability characteristics, i.e., ability to increase capacity in order to handle bigger amount of event data, in comparison with the mentioned above ProM plugins.

1. Burattin, A. (2015). Process Mining Techniques in Business Environments. Cham: Springer. doi: 10.1007/978-3-319-17482-2

2. van der Aalst, W.M.P., et al. (2012). Process mining manifesto. In F. Daniel, K. Barkaoui, S. Dustdar (Ed.), Business Process Management Workshops. BPM 2011 International Workshops. Lecture Notes in Business Information Processing, vol. 99 (pp. 169-194) Berlin, Heidelberg: Springer. doi: 10.1007/978-3-642-28108-2_19

3. Günther, Ch.W. & van der Aalst, W.M.P. (2007). Fuzzy Mining - Adaptive Process Simplification Based on Multi-perspective Metrics. In G. Alonso, P. Dadam, M. Rosemann (Ed.) Proceedings of the 5th International Conference on Business Process Management. BPM 2007. Lecture Notes in Computer Science, vol. 4714 (pp. 328-343) Berlin, Heidelberg: Springer. doi: 10.1007/978-3-540-75183-0_24

4. Fuzzy Miner. (2009, June 17). Retrieved from

5. Verbeek, H.M.W., Buijs J.C.A.M., van Dongen B.F., & van der Aalst, W.M.P. (2010). ProM 6: the process mining toolkit. In M. La Rosa (Ed.), Proceedings of the Business Process Management 2010 Demonstration Track, vol. 615 (pp. 34-39).

6. van Steen, M. & Tanenbaum, A.S. (2017). Distributed Systems, 3rd ed.,

7. van der Aalst, W.M.P. (2016). Process mining: data science in action, 2nd ed. Berlin: Springer. doi: 10.1007/978-3-662-49851-4

8. Turner, C.J., Tiwari, A., Olaiya, R., & Xu, Y. (2012). Business Process Mining: From Theory to Practice, Business Process Management Journal, 18(3), pp. 493-512. doi: 10.1108/14637151211232669

9. Rozinat, A. (2010, Oct 18). ProM Tips - Which Mining Algorithm Should You Use?, Fluxicon. Retrieved from

10. Günther, Ch.W., Rozinat, A. (2012). Disco: Discover Your Processes. Proceedings of the Demonstration Track of the 10th International Conference on Business Process Management (BPM 2012), vol. 940 (pp. 40-44). Tallinn, Estonia.

11. Batyuk, A. & Voityshyn, V. (2018). Process Mining: Applied Discipline and Software Implementations, KPI Science News, 5, pp. 22-36. doi: 10.20535/1810-0546.2018.5.146178

12. Bass, L., Clements, P., & Kazman, R. (2012). Software Architecture in Practice, 3rd ed. Addison-Wesley Professional.

13. IEEE Standard for eXtensible Event Stream (XES) for Achieving Interoperability in Event Logs and Event Streams. (2016). IEEE Std 1849-2016.

14. OpenXES. (2017, June 16). Retrieved from

15. de Leoni, M. & Mannhardt, F. (2015, Feb 13). Road Traffic Fine Management Process. Retrieved from

16. Wen, L., van der Aalst, W.M.P., Wang, J., & Sun, J. (2007). Mining process models with non-free-choice constructs, Data Mining and Knowledge Discovery, 15(2), pp. 145-180.

17. Batyuk, A. & Voityshyn, V. (2018). Software Architecture Design of the Information Technology for Real-Time Business Process Monitoring, ECONTECHMOD, 7(3), pp. 13-22.

18. Teslyuk, T., Tsmots, I., Teslyuk, V., Medykovskyy, M., & Opotyak, Y. (2017). Architecture of the management system of energy efficiency of technological processes at the enterprise. 2017 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT) (pp. 429-433). Lviv. doi: 10.1109/STC-CSIT.2017.8098822

19. Mulesa, O., Geche, F., Batyuk, A., Buchok, V. (2018). Development of Combined Information Technology for Time Series Prediction. In Shakhovska, N., Stepashko, V. (Ed.), Advances in Intelligent Systems and Computing II. CSIT 2017. Advances in Intelligent Systems and Computing, vol. 689. Cham: Springer. doi: 10.1007/978-3-319-70581-1_26