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How do I update my model? On the resilience of Predictive Process Monitoring models to change.

Authors
  • Rizzi, Williams1, 2
  • Di Francescomarino, Chiara1
  • Ghidini, Chiara1
  • Maggi, Fabrizio Maria2
  • 1 Fondazione Bruno Kessler (FBK), Trento, Italy. , (Italy)
  • 2 Free University of Bozen-Bolzano, Bolzano, Italy. , (Italy)
Type
Published Article
Journal
Knowledge and Information Systems
Publisher
Springer-Verlag
Publication Date
Jan 01, 2022
Volume
64
Issue
5
Pages
1385–1416
Identifiers
DOI: 10.1007/s10115-022-01666-9
PMID: 35340819
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Existing well-investigated Predictive Process Monitoring techniques typically construct a predictive model based on past process executions and then use this model to predict the future of new ongoing cases, without the possibility of updating it with new cases when they complete their execution. This can make Predictive Process Monitoring too rigid to deal with the variability of processes working in real environments that continuously evolve and/or exhibit new variant behaviours over time. As a solution to this problem, we evaluate the use of three different strategies that allow the periodic rediscovery or incremental construction of the predictive model so as to exploit new available data. The evaluation focuses on the performance of the new learned predictive models, in terms of accuracy and time, against the original one, and uses a number of real and synthetic datasets with and without explicit Concept Drift. The results provide an evidence of the potential of incremental learning algorithms for predicting process monitoring in real environments. © The Author(s) 2022.

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