Time and activity sequence prediction of business process instances

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

Research output: Contribution to journalArticleAcademicpeer-review

31 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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