Online Conformance Checking Using Behavioural Patterns
New and compelling regulations (e.g., the GDPR in Europe) impose tremendous pressure on organizations, in order to adhere to standard procedures, processes, and practices. The field of conformance checking aims to quantify the extent to which the execution of a process, captured within recorded corresponding event data, conforms to a given reference process model. Existing techniques assume a post-mortem scenario, i.e. they detect deviations based on complete executions of the process. This limits their applicability in an online setting. In such context, we aim to detect deviations online (i.e., in-vivo), in order to provide recovery possibilities before the execution of a process instance is completed. Also, current techniques assume cases to start from the initial stage of the process, whereas this assumption is not feasible in online settings. In this paper, we present a generic framework for online conformance checking, in which the underlying process is represented in terms of behavioural patterns and no assumption on the starting point of cases is needed. We instantiate the framework on the basis of Petri nets, with an accompanying new unfolding technique. The approach is implemented in the process mining tool ProM, and evaluated by means of several experiments including a stress-test and a comparison with a similar technique.
KeywordsConformance checking Online processing Behavioural patterns Stream processing Petri nets Unfoldings
This work has been partially supported by MINECO and FEDER funds under grant TIN2017-86727-C2-1-R.
- 2.Adriansyah, A.: Aligning observed and modeled behavior. Ph.D. thesis, Technische Universiteit Eindhoven (2014)Google Scholar
- 3.vanden Broucke, S.K.L.M., Munoz-Gama, J., Carmona, J., Baesens, B., Vanthienen, J.: Event-based real-time decomposed conformance analysis. In: Meersman, R., et al. (eds.) OTM 2014. LNCS, vol. 8841, pp. 345–363. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45563-0_20CrossRefGoogle Scholar
- 5.Burattin, A., Cimitile, M., Maggi, F.M., Sperduti, A.: Online discovery of declarative process models from event streams. IEEE TSC 8(6), 833–846 (2015)Google Scholar
- 6.Burattin, A., Sperduti, A., van der Aalst, W.M.: Control-flow discovery from event streams. In: Proceedings of the IEEE CEC, pp. 2420–2427 (2014)Google Scholar
- 8.van Dongen, B., Carmona, J., Chatain, T., Taymouri, F.: Aligning modeled and observed behavior: a compromise between computation complexity and quality. In: Dubois, E., Pohl, K. (eds.) CAiSE 2017. LNCS, vol. 10253, pp. 94–109. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59536-8_7CrossRefGoogle Scholar
- 9.Jouck, T., Depaire, B.: PTandLogGenerator: a generator for artificial event data. In: Proceedings of the BPM Demo Track, pp. 23–27 (2016)Google Scholar
- 16.Song, M.: Organizational mining in business process management. Ph.D. thesis, Pohang University of Science and Technology, Pohang, South Korea (2006)Google Scholar
- 21.van Zelst, S.J., Bolt, A., van Dongen, B.F.: Tuning alignment computation: an experimental evaluation. In: Proceedings of ATAED, pp. 6–20 (2017)Google Scholar
- 22.van Zelst, S.J., Bolt, A., Hassani, M., van Dongen, B.F., van der Aalst, W.M.P.: Online conformance checking: relating event streams to process models using prefix-alignments. Int. J. Data Sci. Anal. (2017). https://doi.org/10.1007/s41060-017-0078-6