Abstract
Process mining techniques rely on event logs: the extraction of a process model (discovery) takes an event log as the input, the adequacy of a process model (conformance) is checked against an event log, and the enhancement of a process model is performed by using available data in the log. Several notations and formalisms for event log representation have been proposed in the recent years to enable efficient algorithms for the aforementioned process mining problems. In this paper we show how Conditional Partial Order Graphs (CPOGs), a recently introduced formalism for compact representation of families of partial orders, can be used in the process mining field, in particular for addressing the problem of compact and easy-to-comprehend representation of event logs with data. We present algorithms for extracting both the control flow as well as the relevant data parameters from a given event log and show how CPOGs can be used for efficient and effective visualisation of the obtained results. We demonstrate that the resulting representation can be used to reveal the hidden interplay between the control and data flows of a process, thereby opening way for new process mining techniques capable of exploiting this interplay. Finally, we present open-source software support and discuss current limitations of the proposed approach.
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Notes
- 1.
This paper is an extended version of [6].
- 2.
We assume a total order on the set of event attributes.
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Acknowledgments
The authors would like to thank Alessandro de Gennaro and Danil Sokolov for their help with the integration of the developed process mining tools into Workcraft. Many organisations supported this research work: Andrey Mokhov was supported by Royal Society Research Grant ‘Computation Alive’ and EPSRC project UNCOVER (EP/K001698/1); Josep Carmona was partially supported by funds from the Spanish Ministry for Economy and Competitiveness (MINECO) and the European Union (FEDER funds) under grant COMMAS (ref. TIN2013-46181-C2-1-R); Jonathan Beaumont is currently a PhD student sponsored by a scholarship from the School of Electrical and Electronic Engineering, Newcastle University, UK.
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Mokhov, A., Carmona, J., Beaumont, J. (2016). Mining Conditional Partial Order Graphs from Event Logs. In: Koutny, M., Desel, J., Kleijn, J. (eds) Transactions on Petri Nets and Other Models of Concurrency XI. Lecture Notes in Computer Science(), vol 9930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-53401-4_6
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