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-
4. Fuzzy Miner. (2009, June 17). Retrieved from http://www.processmining.org/online/fuzzyminer
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). CEUR-WS.org.
6. van Steen, M. & Tanenbaum, A.S. (2017). Distributed Systems, 3rd ed., distributed-systems.net.
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 https://fluxicon.com/blog/2010/10/prom-tips-mining-algorithm/
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 http://www.xes-standard.org/openxes/start
15. de Leoni, M. & Mannhardt, F. (2015, Feb 13). Road Traffic Fine Management Process. Retrieved from https://data.4tu.nl/repository/uuid:270fd440-1057-4fb9-89a9-b699b47990f5
16. Wen, L., van der Aalst, W.M.P., Wang, J., & Sun, J. (2007). Mining process models with nonfree-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