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Multi-perspective Comparison of Business Process Variants Based on Event Logs

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Conceptual Modeling (ER 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11157))

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Abstract

A process variant represents a collection of cases with certain shared characteristics, e.g. cases that exhibit certain levels of performance. The comparison of business process variants based on event logs is a recurrent operation in the field of process mining. Existing approaches focus on comparing variants based on directly-follows relations such as “a task directly follows another one” or a “resource directly hands-off to another resource”. This paper presents a more general approach to log-based process variant comparison based on so-called perspective graphs. A perspective graph is a graph-based abstraction of an event log where a node represents any entity referred to in the log (e.g. task, resource, location) and an arc represents a relation between these entities within or across cases (e.g. directly-follows, co-occurs, hands-off to, works-together with). Statistically significant differences between two perspective graphs are captured in a so-called differential perspective graph, which allows us to compare two logs from any perspective. The paper illustrates the approach and compares it to an existing baseline using real-life event logs.

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Notes

  1. 1.

    (vv(e)) is abbreviated to v(e) when it is clear from the context for the purpose of readability.

  2. 2.

    Executable and source code are available from http://apromore.org/platform/tools.

  3. 3.

    https://data.4tu.nl/repository/collection:event_logs_real.

  4. 4.

    10.4121/uuid:500573e6-accc-4b0c-9576-aa5468b10cee.

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Acknowledgements

This research is partly funded by the Australian Research Council (DP150103356) and the Estonian Research Council.

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Correspondence to Hoang Nguyen .

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Nguyen, H., Dumas, M., La Rosa, M., ter Hofstede, A.H.M. (2018). Multi-perspective Comparison of Business Process Variants Based on Event Logs. In: Trujillo, J., et al. Conceptual Modeling. ER 2018. Lecture Notes in Computer Science(), vol 11157. Springer, Cham. https://doi.org/10.1007/978-3-030-00847-5_32

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  • DOI: https://doi.org/10.1007/978-3-030-00847-5_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00846-8

  • Online ISBN: 978-3-030-00847-5

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