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An Eye into the Future: Leveraging A-priori Knowledge in Predictive Business Process Monitoring

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Business Process Management (BPM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10445))

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Abstract

Predictive business process monitoring aims at leveraging past process execution data to predict how ongoing (uncompleted) process executions will unfold up to their completion. Nevertheless, cases exist in which, together with past execution data, some additional knowledge (a-priori knowledge) about how a process execution will develop in the future is available. This knowledge about the future can be leveraged for improving the quality of the predictions of events that are currently unknown. In this paper, we present two techniques - based on Recurrent Neural Networks with Long Short-Term Memory (LSTM) cells - able to leverage knowledge about the structure of the process execution traces as well as a-priori knowledge about how they will unfold in the future for predicting the sequence of future activities of ongoing process executions. The results obtained by applying these techniques on six real-life logs show an improvement in terms of accuracy over a plain LSTM-based baseline.

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Notes

  1. 1.

    Note that, in order to prevent overflow in the computation, the estimated probability for sequences of activities is computed as the sum of the logarithm of the probabilities of the next activities rather than as the product of the probabilities of the next activities.

  2. 2.

    https://data.mendeley.com/datasets/39bp3vv62t/1.

  3. 3.

    The number of rules selected has been determined empirically to allow them to be satisfied in around 50% of the traces of the testing set.

  4. 4.

    We set bSize to 3 and, for the coefficient in charge of weakening the probabilities of activities in a cycle, we used the exponential formula (\(e^{j}\), where j is the number of cycle repetitions).

  5. 5.

    We used an architecture characterized by two LSTM layers. The algorithm used is the Adam learning algorithm with categorical cross entropy loss and the dropout coefficient has been set to 0.2.

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Acknowledgments

This research has been partially carried out within the Euregio IPN12 KAOS, which is funded by the “European Region Tyrol-South Tyrol-Trentino”(EGTC) under the first call for basic research projects.

Author information

Authors and Affiliations

  1. FBK-IRST, Via Sommarive 18, 38050, Trento, Italy

    Chiara Di Francescomarino, Chiara Ghidini & Giulio Petrucci

  2. University of Tartu, Ulikooli 18, 50090, Tartu, Estonia

    Fabrizio Maria Maggi & Anton Yeshchenko

  3. University of Trento, Via Sommarive 14, 38050, Trento, Italy

    Giulio Petrucci

Authors
  1. Chiara Di Francescomarino
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  2. Chiara Ghidini
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  3. Fabrizio Maria Maggi
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  4. Giulio Petrucci
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  5. Anton Yeshchenko
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Correspondence to Chiara Di Francescomarino .

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Editors and Affiliations

  1. Department of Computer Science, Universitat Politècnica de Catalunya, Barcelona, Spain

    Josep Carmona

  2. Department of Computer Science, University of Paderborn, Paderborn, Germany

    Gregor Engels

  3. Department of Supply Chain and Information Systems, Pennsylvania State University, University Park, Pennsylvania, USA

    Akhil Kumar

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Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A. (2017). An Eye into the Future: Leveraging A-priori Knowledge in Predictive Business Process Monitoring. In: Carmona, J., Engels, G., Kumar, A. (eds) Business Process Management. BPM 2017. Lecture Notes in Computer Science(), vol 10445. Springer, Cham. https://doi.org/10.1007/978-3-319-65000-5_15

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  • DOI: https://doi.org/10.1007/978-3-319-65000-5_15

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