Next-Activity Prediction for Non-stationary Processes with Unseen Data Variability

Amolkirat Singh Mangat, Stefanie Rinderle-Ma

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Predictive Process Monitoring (PPM) enables organizations to predict future states of ongoing process instances such as the remaining time, the outcome, or the next activity. A process in this context represents a coordinated set of activities that are enacted by a process engine in a specific order. The underlying source of data for PPM are event logs (ex post) or event streams (runtime) emitted for each activity. Although plenty of methods have been proposed to leverage event logs/streams to build prediction models, most works focus on stationary processes, i.e., the methods assume the range of data variability encountered in the event log/stream to remain the same over time. Unfortunately, this is not always the case as deviations from the expected process behaviour might occur quite frequently and updating prediction models becomes inevitable eventually. In this paper we investigate non-stationary processes, i.e., the impact of unseen data variability in event streams on prediction models from a structural and behavioural point of view. Strategies and methods are proposed to incorporate unknown data variability and to update recurrent neural network based models continuously in order to accommodate changing process behaviour. The approach is prototypically implemented and evaluated based on real-world data sets.

Original languageEnglish
Title of host publicationEnterprise Design, Operations, and Computing - 26th International Conference, EDOC 2022, Proceedings
EditorsJoão Paulo A. Almeida, Dimka Karastoyanova, Giancarlo Guizzardi, Claudenir M. Fonseca, Marco Montali, Fabrizio Maria Maggi
PublisherSpringer Science and Business Media Deutschland GmbH
Pages145-161
Number of pages17
ISBN (Print)9783031176036
DOIs
StatePublished - 2022
Event26th International Conference on Enterprise Design, Operations, and Computing, EDOC 2022 - Bozen-Bolzano, Italy
Duration: 3 Oct 20227 Oct 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13585 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th International Conference on Enterprise Design, Operations, and Computing, EDOC 2022
Country/TerritoryItaly
CityBozen-Bolzano
Period3/10/227/10/22

Keywords

  • Data variability
  • Event streams
  • Non-stationary prediction
  • Predictive process monitoring

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