Abstract
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.
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Notes
- 1.
- 2.
If \(\textsf {obs}\) has no key c, \(\textsf {obs}(c)\) returns the empty set. If \(\textsf {inc}\) has no key c then \(\textsf {inc}(c)\) returns 0.
- 3.
\(P_{\min }(b)\) and \(P_{\max }(b)\) refer to the min./max. number of distinct patterns to be seen before b.
- 4.
In general, not all Petri nets can be reversed for computing the minimal number of relations to reach the end. Hence, for computing confidence, we assume in the realization of the framework presented in Sect. 6 a proper subclass, i.e., sound workflow nets.
- 5.
- 6.
Models and streams available at https://doi.org/10.5281/zenodo.1194057.
- 7.
This limitation only affects Sect. 6: it is possible to manually define the behavioural patterns.
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Acknowledgements
This work has been partially supported by MINECO and FEDER funds under grant TIN2017-86727-C2-1-R.
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Burattin, A., van Zelst, S.J., Armas-Cervantes, A., van Dongen, B.F., Carmona, J. (2018). Online Conformance Checking Using Behavioural Patterns. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds) Business Process Management. BPM 2018. Lecture Notes in Computer Science(), vol 11080. Springer, Cham. https://doi.org/10.1007/978-3-319-98648-7_15
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