Process Modelling Based on Event Logs

• Autorzy: Brzychczy E.

Identyfikatory

DOI

10.2478/mape-2018-0049

• Języki publikacji: EN

Abstrakty

EN

Process modelling is a very important stage in a Business Process Management cycle enabling process analysis and its redesign. Many sources of information for process modelling purposes exist. It may be an analysis of documentation related directly or indirectly to the process being analysed, observations or participation in the process. Nowadays, for this purpose, it is increasingly proposed to use the event logs from organization’s IT systems. Event logs could be analysed with process mining techniques to create process models expressed by various notations (i.e. Petri Nets, BPMN, EPC). Process mining enables also conformance checking and enhancement analysis of the processes. In the paper issues related to process modelling and process mining are briefly discussed. A case study, an example of delivery process modelling with process mining technique is presented.

Słowa kluczowe

EN

process modelling; process mining; event logs; Petri net; model discovery

• Wydawca: STE GROUP ; De Gruyter
• Czasopismo: Multidisciplinary Aspects of Production Engineering
• Rocznik: 2018
• Tom: Vol. 1, Iss. 1
• Strony: 385--392
• Opis fizyczny: Bibliogr. 18 poz., fig., tab.

Twórcy

autor

Brzychczy E.

• AGH University of Science and Technology, Poland

Bibliografia

• 1. Aguilar-Savén, R. S. (2004). Business process modelling: Review and framework, International Journal of Production Economics, 90(2), pp. 129-149.
• 2. Alotaibi, Y. (2016). Business process modelling challenges and solutions: a literature review. Journal of Intelligent Manufacturing, 27(4), pp. 701–723.
• 3. Augusto, A., Conforti, R., Dumas, M., La Rosa, M., Maggi, F., Marrella, A., Mecella, M. and Soo, A. (2017). Automated Discovery of Process from Event Logs: Review and Benchmark. arXiv:1705.02288
• 4. Buijs, J. C., van Dongen, B. F., and van der Aalst W.M.P (2014). Quality dimensions in process discovery: The importance of fitness, precision, generalization and simplicity. International Journal of Cooperative Information Systems, 23(1) p. 1440001.
• 5. Business Process Model and Notation™ (BPMN™) 2.0.2 Object Management Group, 2013 (http://www.omg.org/spec/BPMN/index.htm).
• 6. Czekaj, S. (2017). Analysis of the parcels delivery process in a selected company using process mining techniques. BSc., AGH University of Science and Technology (in Polish).
• 7. Dumas, M., La Rosa, M., Mendling, J. and Reijers, H. (2018): Fundamentals of Business Process Management, Berlin Heidelberg, Springer-Verlag.
• 8. Günther, C.W and van der Aalst, W.M.P. (2006). Mining Activity Clusters from Low-Level Event Logs. BETA Working Paper Series, WP 165, Eindhoven University of Technology, Eindhoven.
• 9. Leemans, S. J., Fahland, D. and van der Aalst, W.M.P (2013). Discovering block-structured proces models from event logs containing infrequent behavior. In: International Conference on Business Process Management. Springer, pp. 66–78.
• 10. List, B. and Korherr, B. (2006). An evaluation of conceptual business process modelling languages. In: Proceedings of the 2006 ACM symposium on Applied computing (SAC '06). ACM, New York, USA, pp. 1532-1539.
• 11. Munoz-Gama, J. (2016). Conformance checking and diagnosis in process mining – comparing observed and modeled processes. Lecture notes in business information processing, vol 270. Springer, Cham
• 12. Scheer, A.-W., Thomas, O. and Adam, O. (2005). Process Modeling using Event-Driven Process Chains. In: M. Dumas, W. M. P. van der Aalst and A. H. M. Ter Hofstede, eds., Process-Aware Information Systems: Bridging People and Software through Process Technology. Hoboken, NJ, USA: John Wiley & Sons, Inc.
• 13. Szpyrka, M. (2008). Petri nets in design and analysis of concurrent systems. Warszawa: WNT (in Polish).
• 14. The Process Mining Manifesto by the IEEE Task Force on Process Mining, In: F. Daniel, K. Barkaoui, S. Dustdar, eds., BPM 2011 Workshops, Part I, LNBIP 99, pp. 169–194. Berlin: Springer-Verlag, 2012.
• 15. Van der Aalst, W.M.P. (2009). Process-Aware Information Systems: Lessons to Be Learned from Process Mining. In: Transactions on Petri Nets and Other Models of Concurrency II, Lecture Notes in Computer Science, vol. 5460, Berlin: Springer-Verlag, pp. 1-26.
• 16. Van der Aalst, W.M.P. (2016). Process Mining: Data Science in Action. Berlin: Springer-Verlag.
• 17. Van der Aalst, W.M.P., Weijters, A.J.M.M. and Maruster, L. (2004). Workflow Mining: Discovering Process Models from Event Logs. IEEE Transactions on Knowledge and Data Engineering, 16(9), pp.1128-1142.
• 18. Weijters, A. and Ribeiro J. (2011). Flexible heuristics miner (fhm). IEEE Symposium on Computational Intelligence and Data Mining, IEEE, pp. 310–317.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).