An experimental evaluation of the generalizing capabilities of process discovery techniques and black-box sequence models

N. Tax, S.J. van Zelst, I. Teinemaa

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

    2 Citations (Scopus)
    1 Downloads (Pure)

    Abstract

    A plethora of automated process discovery techniques have been developed which aim to discover a process model based on event data originating from the execution of business processes. The aim of the discovered process models is to describe the control-flow of the underlying business process. At the same time, a variety of sequence modeling techniques have been developed in the machine learning domain, which aim at finding an accurate, not necessarily interpretable, model describing sequence data. Both approaches ultimately aim to find a model that generalizes the behavior observed, i.e., they describe behavior that is likely to be part of the underlying distribution, whilst disallowing unlikely behavior. While the generalizing capabilities of process discovery algorithms have been studied before, a comparison, in terms of generalization, w.r.t. sequence models is not yet explored. In this paper we present an experimental evaluation of the generalizing capabilities of automated process discovery techniques and black-box sequence models, on the basis of next activity prediction. We compare a range of process discovery and sequence modeling techniques on a range of real-life datasets from the business process management domain. Our results indicate that LSTM neural networks more accurately describe previously unseen traces (i.e., test traces) than existing process discovery methods.
    Original languageEnglish
    Title of host publicationEnterprise, Business-Process and Information Systems Modeling
    Subtitle of host publication19th International Conference, BPMDS 2018, 23rd International Conference, EMMSAD 2018, Held at CAiSE 2018, Tallinn, Estonia, June 11-12, 2018, Proceedings
    EditorsIris Reinhartz-Berger, Sergio Guerreiro, Wided Guedria, Rainer Schmidt, Palash Bera, Jens Gulden
    Place of PublicationDordrecht
    PublisherSpringer
    Pages165-180
    Number of pages16
    ISBN (Electronic)978-3-319-91704-7
    ISBN (Print)978-3-319-91703-0
    DOIs
    Publication statusPublished - 16 May 2018
    Event19th International Conference on Enterprise, Business-Process and Information Systems Modeling (BPMDS 2018) - Tallinn, Estonia
    Duration: 11 Jun 201812 Jun 2018
    Conference number: 19

    Publication series

    NameLecture Notes in Business Information Processing
    PublisherSpringer
    ISSN (Print)1865-1356

    Conference

    Conference19th International Conference on Enterprise, Business-Process and Information Systems Modeling (BPMDS 2018)
    Abbreviated titleBPMDS 2018
    CountryEstonia
    CityTallinn
    Period11/06/1812/06/18

    Keywords

    • Behavioral generalization
    • Next activity prediction
    • Process discovery
    • Process mining
    • Sequence modeling

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