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Time and activity sequence prediction of business process instances


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.

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  1. We assume this representation according to the Unix epoch time.

  2. We assume that a fixed order is always available for attribute’s values (for example, the lexicographical order).

  3. Source code available at

  4. The log is a part of the the full log provided by Eindhoven University of Technology.

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The work reported in this paper is supported by the Eurostars-Eureka project PROMPT (E!6696).

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Correspondence to Mirko Polato.

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Polato, M., Sperduti, A., Burattin, A. et al. Time and activity sequence prediction of business process instances. Computing 100, 1005–1031 (2018).

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  • Process mining
  • Prediction
  • Remaining time
  • Machine learning

Mathematics Subject Classification

  • 68T01