Abstract
Declarative process mining is the set of techniques aimed at extracting behavioural constraints from event logs. These constraints are inherently of a reactive nature, in that their activation restricts the occurrence of other activities. In this way, they are prone to the principle of ex falso quod libet: they can be satisfied even when not activated. As a consequence, constraints can be mined that are hardly interesting to users or even potentially misleading. In this paper, we build on the observation that users typically read and write temporal constraints as if-statements with an explicit indication of the activation condition. Our approach is called Janus, because it permits the specification and verification of reactive constraints that, upon activation, look forward into the future and backwards into the past of a trace. Reactive constraints are expressed using Linear-time Temporal Logic with Past on Finite Traces (LTLp\(_f\)). To mine them out of event logs, we devise a time bi-directional valuation technique based on triplets of automata operating in an on-line fashion. Our solution proves efficient, being at most quadratic w.r.t. trace length, and effective in recognising interestingness of discovered constraints.
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Acknowledgements
The work of Alessio Cecconi, Claudio Di Ciccio, and Jan Mendling has been funded by the Austrian FFG grant 861213 (CitySPIN) and from the EU H2020 programme under MSCA-RISE agreement 645751 (RISE_BPM). Giuseppe De Giacomo has been partially supported by the Sapienza project “Immersive Cognitive Environments”.
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Cecconi, A., Di Ciccio, C., De Giacomo, G., Mendling, J. (2018). Interestingness of Traces in Declarative Process Mining: The Janus LTLp\(_f\) Approach. 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_8
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