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Process mining using BPMN: relating event logs and process models

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Abstract

Process-aware information systems (PAIS) are systems relying on processes, which involve human and software resources to achieve concrete goals. There is a need to develop approaches for modeling, analysis, improvement and monitoring processes within PAIS. These approaches include process mining techniques used to discover process models from event logs, find log and model deviations, and analyze performance characteristics of processes. The representational bias (a way to model processes) plays an important role in process mining. The BPMN 2.0 (Business Process Model and Notation) standard is widely used and allows to build conventional and understandable process models. In addition to the flat control flow perspective, subprocesses, data flows, resources can be integrated within one BPMN diagram. This makes BPMN very attractive for both process miners and business users, since the control flow perspective can be integrated with data and resource perspectives discovered from event logs. In this paper, we describe and justify robust control flow conversion algorithms, which provide the basis for more advanced BPMN-based discovery and conformance checking algorithms. Thus, on the basis of these conversion algorithms low-level models (such as Petri nets, causal nets and process trees) discovered from event logs using existing approaches can be represented in terms of BPMN. Moreover, we establish behavioral relations between Petri nets and BPMN models and use them to adopt existing conformance checking and performance analysis techniques in order to visualize conformance and performance information within a BPMN diagram. We believe that the results presented in this paper can be used for a wide variety of BPMN mining and conformance checking algorithms. We also provide metrics for the processes discovered before and after the conversion to BPMN structures. Cases for which conversion algorithms produce more compact or more complicated BPMN models in comparison with the initial models are identified.

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Notes

  1. In order to give an intuitive example, we consider a simple and expressive synthetic event log. The analysis of process models discovered from real-life event logs is demonstrated in Sect. 9.

  2. Note that due to the free-choice structure of \(\mathord {\textit{PN}}\), postsets either coincide or do not intersect.

  3. Note that we consider system nets without hanging places.

  4. All the places have the same place node \(\mathcal {N}\), obtained on the previous step of the algorithm.

  5. These logs exclude user and commercial sensible information like names, credit cards and invoices.

  6. All the models presented in this section are discovered using ProM framework

  7. By CSR1, CSR2, CSR3 and CSR4 we denote event logs of the Computer Reservation System, containing only positive cases and/or booking flight event.

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Authors and Affiliations

  1. National Research University Higher School of Economics, Moscow, Russia

    Anna A. Kalenkova, Wil M. P. van der Aalst, Irina A. Lomazova & Vladimir A. Rubin

  2. Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands

    Wil M. P. van der Aalst

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  1. Anna A. Kalenkova
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  2. Wil M. P. van der Aalst
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  3. Irina A. Lomazova
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  4. Vladimir A. Rubin
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Correspondence to Anna A. Kalenkova.

Additional information

Communicated by Prof. Ulrich Frank.

This work is supported by the Scientific Fund of the National Research University Higher School of Economics and is supported by Russian Fund for Basic Research (Project 15-37-21103).

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Kalenkova, A.A., van der Aalst, W.M.P., Lomazova, I.A. et al. Process mining using BPMN: relating event logs and process models. Softw Syst Model 16, 1019–1048 (2017). https://doi.org/10.1007/s10270-015-0502-0

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