Abstract
While models and event logs are readily available in modern organizations, their quality can seldom be trusted. Raw event recordings are often noisy, incomplete, and contain erroneous recordings. The quality of process models, both conceptual and data-driven, heavily depends on the inputs and parameters that shape these models, such as domain expertise of the modelers and the quality of execution data. The mentioned quality issues are specifically a challenge for conformance checking. Conformance checking is the process mining task that aims at coping with low model or log quality by comparing the model against the corresponding log, or vice versa. The prevalent assumption in the literature is that at least one of the two can be fully trusted. In this work, we propose a generalized conformance checking framework that caters for the common case, when one does neither fully trust the log nor the model. In our experiments we show that our proposed framework balances the trust in model and log as a generalization of state-of-the-art conformance checking techniques.
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Notes
- 1.
The approach has been implemented in the GeneralizedConformance plugin in ProM. A screencast demonstrating the usage of the plugin is provided here: http://andreas.solti.de/generalized-conformance-checking/.
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Acknowledgments
This work was partially supported by the European Union’s Seventh Framework Programme (FP7/2007-2013) grant 612052 (SERAMIS) and the German Research Foundation (DFG), grant WE 4891/1-1.
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Rogge-Solti, A., Senderovich, A., Weidlich, M., Mendling, J., Gal, A. (2016). In Log and Model We Trust? A Generalized Conformance Checking Framework. In: La Rosa, M., Loos, P., Pastor, O. (eds) Business Process Management. BPM 2016. Lecture Notes in Computer Science(), vol 9850. Springer, Cham. https://doi.org/10.1007/978-3-319-45348-4_11
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DOI: https://doi.org/10.1007/978-3-319-45348-4_11
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