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Computing

, Volume 100, Issue 9, pp 1005–1031 | Cite as

Time and activity sequence prediction of business process instances

  • Mirko Polato
  • Alessandro Sperduti
  • Andrea Burattin
  • Massimiliano de Leoni
Article

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.

Keywords

Process mining Prediction Remaining time Machine learning 

Mathematics Subject Classification

68T01 

Notes

Acknowledgements

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

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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of Padua, Torre ArchimedePadovaItaly
  2. 2.DTU, Technical University of DenmarkKgs. LyngbyDenmark
  3. 3.Eindhoven University of TechnologyEindhovenThe Netherlands

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