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Extracting Event Data from Databases to Unleash Process Mining

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BPM - Driving Innovation in a Digital World

Part of the book series: Management for Professionals ((MANAGPROF))

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Abstract

Increasingly organizations are using process mining to understand the way that operational processes are executed. Process mining can be used to systematically drive innovation in a digitalized world. Next to the automated discovery of the real underlying process, there are process-mining techniques to analyze bottlenecks, to uncover hidden inefficiencies, to check compliance, to explain deviations, to predict performance, and to guide users towards “better” processes. Dozens (if not hundreds) of process-mining techniques are available and their value has been proven in many case studies. However, process mining stands or falls with the availability of event logs. Existing techniques assume that events are clearly defined and refer to precisely one case (i.e. process instance) and one activity (i.e., step in the process). Although there are systems that directly generate such event logs (e.g., BPM/WFM systems), most information systems do not record events explicitly. Cases and activities only exist implicitly. However, when creating or using process models “raw data” need to be linked to cases and activities. This paper uses a novel perspective to conceptualize a database view on event data. Starting from a class model and corresponding object models it is shown that events correspond to the creation, deletion, or modification of objects and relations. The key idea is that events leave footprints by changing the underlying database. Based on this an approach is described that scopes, binds, and classifies data to create “flat” event logs that can be analyzed using traditional process-mining techniques.

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Notes

  1. 1.

    We use the term “digitalize” to emphasize the transformational character of digitized data.

  2. 2.

    For example, http://www.win.tue.nl/ieeetfpm/doku.php?id=shared:process_mining_case_studies lists over 20 successful case studies in industry.

  3. 3.

    Increasingly systems mark deleted objects as not relevant (a so-called soft delete) rather than deleting them. In this way all intermediate states of the database can be reconstructed. Moreover, marking objects as deleted instead of completely removing them from the database is often more natural, e.g., concerts are not deleted—they are canceled, employees are not deleted—they are fired, etc.

  4. 4.

    \( \mathcal{P}(X) \) is the powerset of X, i.e., \( Y\in \mathcal{P}(X) \) if \( Y\subseteq X \).

  5. 5.

    \(f\;\in\;X\nrightarrow\;Y\) is a partial function, i.e., the domain of f may be any subset of X: \( dom(f)\subseteq X \).

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Acknowledgements

This work was supported by the Basic Research Program of the National Research University Higher School of Economics (HSE) in Moscow.

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Authors and Affiliations

  1. Architecture of Information Systems, Eindhoven University of Technology, 513, 5600 MB, Eindhoven, The Netherlands

    Wil M. P. van der Aalst

  2. International Laboratory of Process-Aware Information Systems, National Research University Higher School of Economics (HSE), 33 Kirpichnaya Street, Moscow, Russia

    Wil M. P. van der Aalst

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  1. Wil M. P. van der Aalst
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Correspondence to Wil M. P. van der Aalst .

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Editors and Affiliations

  1. Institute of Information Systems, University of Liechtenstein, Vaduz, Liechtenstein

    Jan vom Brocke

  2. Institute of Information Systems, University of Liechtenstein, Vaduz, Liechtenstein

    Theresa Schmiedel

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van der Aalst, W.M.P. (2015). Extracting Event Data from Databases to Unleash Process Mining. In: vom Brocke, J., Schmiedel, T. (eds) BPM - Driving Innovation in a Digital World. Management for Professionals. Springer, Cham. https://doi.org/10.1007/978-3-319-14430-6_8

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