Predictive business process monitoring with LSTM neural networks

N. Tax, I. Verenich, M. La Rosa, M. Dumas

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    153 Citations (Scopus)
    3 Downloads (Pure)

    Abstract

    Predictive business process monitoring methods exploit logs of completed cases of a process in order to make predictions about running cases thereof. Existing methods in this space are tailor-made for specific prediction tasks. Moreover, their relative accuracy is highly sensitive to the dataset at hand, thus requiring users to engage in trial-and-error and tuning when applying them in a specific setting. This paper investigates Long Short-Term Memory (LSTM) neural networks as an approach to build consistently accurate models for a wide range of predictive process monitoring tasks. First, we show that LSTMs outperform existing techniques to predict the next event of a running case and its timestamp. Next, we show how to use models for predicting the next task in order to predict the full continuation of a running case. Finally, we apply the same approach to predict the remaining time, and show that this approach outperforms existing tailor-made methods.
    Original languageEnglish
    Title of host publicationAdvanced Information Systems Engineering : 29th International Conference, CAiSE 2017, Essen Germany, June 12-16, 2017. Proceedings
    EditorsKlaus Pohl, Eric Dubois
    PublisherSpringer
    Pages477-492
    Number of pages16
    ISBN (Electronic)978-3-319-59536-8
    ISBN (Print)978-3-319-59535-1
    DOIs
    Publication statusPublished - 2017
    Event29th International Conference on Advanced Information Systems Engineering (CAiSE 2017) - Essen, Germany
    Duration: 12 Jun 201716 Jun 2017
    Conference number: 29
    http://caise2017.paluno.de/welcome/

    Publication series

    NameLNCS
    PublisherSpringer
    Volume10253
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference29th International Conference on Advanced Information Systems Engineering (CAiSE 2017)
    Abbreviated titleCAiSE'17
    Country/TerritoryGermany
    CityEssen
    Period12/06/1716/06/17
    Internet address

    Fingerprint

    Dive into the research topics of 'Predictive business process monitoring with LSTM neural networks'. Together they form a unique fingerprint.

    Cite this