Abstract
Process mining is a new emerging discipline related to process management, formal process modelling, and data mining. One of the main tasks of process mining is model synthesis (discovery) based on event logs. A wide range of algorithms for process model discovery, analysis, and enhancement is developed. The real-life event logs often contain noise of different types. In this paper, we describe the main causes of noise in the event logs and study the effect of noise on the performance of process discovery algorithms. The experimental results of application of the main process discovery algorithms to artificial event logs with noise are provided. Specially generated event logs with noise of different types were processed using the four basic discovery techniques. Although modern algorithms can cope with some types of noise, in most cases, their use does not lead to obtaining a satisfactory result. Thus, there is a need for more sophisticated algorithms to deal with noise of different types.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Van der Aalst, W.M.P., Process Mining: Discovery, Conformance and Enhancement of Business Processes, Springer, 2011.
Van der Aalst, W.M.P., Weijters, A.J.M.M., and Maruster, L., Workflow mining: Discovering process models from event logs, IEEE Trans. Knowl. Data Eng., 2004, vol. 16, no. 9, pp. 1128–1142.
Van der Aalst, W.M.P., Adriansyah, A., and Van Dongen, B.F., Replaying history on process models for conformance checking and performance analysis, Wiley Interdiscip. Rev.: Data Mining Knowl. Discovery, 2012, vol. 2, no. 2, pp. 182–192.
Adriansyah, A., Van Dongen, B.F., and Van der Aalst, W.M.P., Conformance checking using cost-based fitness analysis, 15th IEEE International Conference on Enterprise Distributed Object Computing Conference (EDOC), 2011, pp. 55–64.
Adriansyah, A., Van Dongen, B.F., and Van der Aalst, W.M.P., Towards robust conformance checking, in Business Process Management Workshops, Springer, 2011, pp. 122–133.
Adriansyah, A., Munoz-Gama, J., Carmona, J., Van Dongen, B.F., and Van der Aalst, W.M.P., Alignment based precision checking, in Business Process Management Workshops, Springer, 2012, pp. 137–149.
Buijs, J.C.A.M., Van Dongen, B.F., and Van der Aalst, W.M.P., On the role of fitness, precision, generalization and simplicity in process discovery, 20th International Conference on Cooperative Information Systems (CoopIS 2012), 2012.
Van Dongen, B.F., Van der Aalst, W.M.P., Günther, C.W., Rozinat, A., Verbeek, E., and Weijters, T., ProM: The process mining toolkit, in Business Process Management Demonstration Track (BPMDemos2009), Medeiros, A.K.A.D. and Weber, B., Eds., 2009, vol. 489, pp. 1–4.
Kalenkova, A.A. and Lomazova, I.A., Discovery of cancellation regions within process mining techniques, Proceedings of the 22nd International Workshop on Concurrency, Specification and Programming, Warsaw, 2013, pp. 232–244.
Kalenkova, A.A., Lomazova, I.A., and Van der Aalst, W.M.P., Process model discovery: A method based on transition system decomposition, Lect. Notes Comput. Sci., 2014, vol. 8489, pp. 71–90.
Leemans, S.J.J., Fahland, D., and Van der Aalst, W.M.P., Discovering block-structured process models from incomplete event logs, in Tech. Rep. BPM-14-05, Eindhoven University of Technology, 2014.
Munoz-Gama, J., Carmona, J., and Van der Aalst, W.M.P., Conformance checking in the large: Partitioning and topology, Lect. Notes Comput. Sci., 2013, vol. 8094, pp. 130–145.
Verbeek, H.M.W., Buijs, J.C.A.M., Van Dongen, B.F., and Van der Aalst, W.M.P., Prom 6: The process mining toolkit, in Proceedings of BPM Demonstration Track, 2010, vol. 615, pp. 34–39.
Verbeek, H.M.W., Buijs, J.C.A.M., Van Dongen, B.F., and Van der Aalst, W.M.P., XES, XESame, and ProM 6, Lect. Notes Bus. Inf. Process., 2011, vol. 72, pp. 60–75. doi 10.1007/978-3-642-17722-4_5
http://www.xes-standard.org/xesstandarddefinition.
Rogge-Solti, A., Mans, R.S., Van der Aalst, W.M.P., and Weske, M., Repairing event logs using timed process models, Lect. Notes Comput. Sci., 2013, vol. 8186, pp. 705–708.
Rozinat, A., Process mining: Conformance and extension, PhD Thesis, Eindhoven University of Technology, 2010.
Rubin, V.A., Lomazova, I.A., and Van der Aalst, W.M.P., Agile development with software process mining, Proceedings of the 2014 International Conference on Software and System Process (ICSSP 2014), Nanjing, 2014, pp. 70–74.
Rubin, V.A., Mitsyuk, A.A., Lomazova, I.A., and Van der Aalst, W.M.P., Process mining can be applied to software too!, Proceedings of the 8th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM 2014), Torino, 2014, pp. 57:1–57:8.
Shugurov, I. and Mitsyuk, A.A., Generation of a set of event logs with noise, Proceedings of the 8th Spring/Summer Young Researchers’ Colloquium on Software Engineering (SYRCoSE 2014), 2014, pp. 88–95.
http://pais.hse.ru/research/projects/gena.
Van der Werf, J.M.E.M., et al., Process discovery using integer linear programming, in Applications and Theory of Petri Nets, Springer Berlin Heidelberg, 2008, pp. 368–387.
Weijters, A., Van der Aalst, W.M.P., and De Medeiros, A.K.A., Process mining with the heuristics miner-algorithm, Tech. Univ. Eindhoven, Tech. Rep. WP, 2006, vol. 166, pp. 1–34.
Author information
Authors and Affiliations
Corresponding author
Additional information
Original Russian Text © A.A. Mitsyuk, I.S. Shugurov, 2014, published in Modelirovanie i Analiz Informatsionnykh Sistem, 2014, Vol. 21, No. 4, pp. 181–198.
About this article
Cite this article
Mitsyuk, A.A., Shugurov, I.S. On process model synthesis based on event logs with noise. Aut. Control Comp. Sci. 50, 460–470 (2016). https://doi.org/10.3103/S0146411616070154
Received:
Published:
Issue Date:
DOI: https://doi.org/10.3103/S0146411616070154