Time and activity sequence prediction of business process instances

Mirko Polato, Alessandro Sperduti, Andrea Burattin, Massimiliano de Leoni

    Research output: Contribution to journalArticleAcademicpeer-review

    48 Citations (Scopus)

    Abstract

    The ability to know in advance the trend of running process instances, with respect to different features, such as the expected completion time, would allow business managers to timely counteract to undesired situations, in order to prevent losses. Therefore, the ability to accurately predict future features of running business process instances would be a very helpful aid when managing processes, especially under service level agreement constraints. However, making such accurate forecasts is not easy: many factors may influence the predicted features. Many approaches have been proposed to cope with this problem but, generally, they assume that the underlying process is stationary. However, in real cases this assumption is not always true. In this work we present new methods for predicting the remaining time of running cases. In particular we propose a method, assuming process stationarity, which achieves state-of-the-art performances and two other methods which are able to make predictions even with non-stationary processes. We also describe an approach able to predict the full sequence of activities that a running case is going to take. All these methods are extensively evaluated on different real case studies.

    Original languageEnglish
    Pages (from-to)1005–1031
    Number of pages27
    JournalComputing
    Volume100
    Issue number9
    DOIs
    Publication statusPublished - 1 Sep 2018

    Keywords

    • Machine learning
    • Prediction
    • Process mining
    • Remaining time

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