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Automatic Root Cause Identification Using Most Probable Alignments

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Business Process Management Workshops (BPM 2017)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 308))

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Abstract

In many organizational contexts, it is important that behavior conforms to the intended behavior as specified by process models. Non-conforming behavior can be detected by aligning process actions in the event log to the process model. A probable alignment indicates the most likely root cause for non-conforming behavior. Unfortunately, available techniques do not always return the most probable alignment and, therefore, also not the most probable root cause. Recognizing this limitation, this paper introduces a method for computing the most probable alignment. The core idea of our approach is to use the history of an event log to assign probabilities to the occurrences of activities and the transitions between them. A theoretical evaluation demonstrates that our approach improves upon existing work.

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Notes

  1. 1.

    A conjugate prior means that the prior is from the same family of distributions as the posterior. The conjugate prior for the categorical distribution is the Dirichlet distribution [7].

  2. 2.

    The probability of a transition given a marking uses the same logic as in Stochastic Petri Nets [13].

  3. 3.

    To calculate probabilities Alizadeh et al. [5] use the fitted event log (\(\mathcal {L}_{\text {fit}}\)) of Table 1 (first 6 rows). Process executions are mapped onto a state, for which a state representation function is used: either a sequence, multiset or set abstraction. As a cost function \(g=1+\log {(\frac{1}{\theta })}\) is used.

  4. 4.

    Alizadeh et al. [5] find the outcome \(\langle \)A,B,B,B,B,C\(\rangle \) if the state representation function is a set abstraction. Our technique obtains the same result (see Fig. 7b).

  5. 5.

    Note for both variants \(\mathcal {L}_{\text {fit}}=\emptyset \), so the technique in [5] does not work.

References

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Acknowledgement

We thank Massimiliano de Leoni for validating our understanding of [5].

Author information

Authors and Affiliations

  1. Department of Computer Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Marie Koorneef, Henrik Leopold & Hajo A. Reijers

  2. Institute for Information Business, Vienna University of Economics and Business, Vienna, Austria

    Andreas Solti

  3. Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands

    Hajo A. Reijers

Authors
  1. Marie Koorneef
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  2. Andreas Solti
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  3. Henrik Leopold
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  4. Hajo A. Reijers
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Corresponding author

Correspondence to Henrik Leopold .

Editor information

Editors and Affiliations

  1. Department of Service and Information System Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain

    Ernest Teniente

  2. Humboldt-Universität zu Berlin, Berlin, Berlin, Germany

    Matthias Weidlich

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Koorneef, M., Solti, A., Leopold, H., Reijers, H.A. (2018). Automatic Root Cause Identification Using Most Probable Alignments. In: Teniente, E., Weidlich, M. (eds) Business Process Management Workshops. BPM 2017. Lecture Notes in Business Information Processing, vol 308. Springer, Cham. https://doi.org/10.1007/978-3-319-74030-0_15

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  • DOI: https://doi.org/10.1007/978-3-319-74030-0_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74029-4

  • Online ISBN: 978-3-319-74030-0

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