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Experimenting and Assessing a Probabilistic Business Process Deviance Mining Framework Based on Ensemble Learning

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Enterprise Information Systems (ICEIS 2017)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 321))

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Abstract

Business Process Intelligence (BPI) and Process Mining, two very active research areas of research, share a great interest towards the issue of discovering an effective Deviance Detection Model (DDM), computed via accessing log data. The DDM model allows us to understand whether novel instances of the target business process are deviant or not, thus becoming extremely useful in modern application scenarios such as cybersecurity and fraud detection. In this chapter, we further and significantly extend our previous line of work that has originated, across years, an innovative ensemble-learning framework for mining business process deviances, whose main benefit is that of introducing a sort of multi-view learning scheme. One of the most relevant achievements of this extended work consists in proposing an alternative meta-learning method for probabilistically combining the predictions of different base DDMs, and putting all together in a conceptual system architecture oriented to support common Business Process Management (BPM) scenarios. In addition to this, we here envisage the combination of this approach with a deviance explanation methodology that leverages and extends a previous method still proposed by us in previous research. Basically, the latter method allows to discover accurate and readable deviance-aware trace clusters defined in terms of descriptive rules over both properties and behavioral aspects of the traces. We complement our analytical contributions with a comprehensive experimental assessment and analysis, even in comparison with a state-of-the-art DDM discovery approach. The experimental results we derive confirm flexibility, reliability and effectiveness of the proposed business process deviance mining framework.

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Notes

  1. 1.

    With regard to the labelling scheme of Fig. 1 (and assuming that the base DDMs appear in CL in the same left-to-right order as in the figure), it is \(CL[q]=c_{i,j}\) iff \(q= (i-1) \times k + j\) and \(PL[q] = P_i\) iff \(i = {\lfloor (q-1) /k \rfloor +1}\).

  2. 2.

    Similar encodings are used by recent process mining approaches [11, 14, 35] to obtain an abstract representation for a given log.

  3. 3.

    With a little abuse of notation, we take the freedom of denoting both the pattern and the corresponding selector by the same symbol.

  4. 4.

    For better readability, we will omit \(L,\delta ,\gamma ,\sigma \) and maxLen when they are clear from the context.

  5. 5.

    Available at https://fluxicon.com/disco/.

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Author information

Authors and Affiliations

  1. DIA Department, University of Trieste, Trieste, Italy

    Alfredo Cuzzocrea

  2. ICAR-CNR, Rende, CS, Italy

    Alfredo Cuzzocrea, Francesco Folino, Massimo Guarascio & Luigi Pontieri

Authors
  1. Alfredo Cuzzocrea
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  2. Francesco Folino
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  3. Massimo Guarascio
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  4. Luigi Pontieri
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Corresponding author

Correspondence to Alfredo Cuzzocrea .

Editor information

Editors and Affiliations

  1. MODESTE/ESEO, Angers, France

    Slimane Hammoudi

  2. Warsaw University of Technology, Warsaw, Poland

    Michał Śmiałek

  3. MODESTE/ESEO, Angers, France

    Olivier Camp

  4. INSTICC, Polytechnic Institute of Setúbal, Setúbal, Poland

    Joaquim Filipe

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Cuzzocrea, A., Folino, F., Guarascio, M., Pontieri, L. (2018). Experimenting and Assessing a Probabilistic Business Process Deviance Mining Framework Based on Ensemble Learning. In: Hammoudi, S., Śmiałek, M., Camp, O., Filipe, J. (eds) Enterprise Information Systems. ICEIS 2017. Lecture Notes in Business Information Processing, vol 321. Springer, Cham. https://doi.org/10.1007/978-3-319-93375-7_6

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