Process model forecasting and change exploration using time series analysis of event sequence data

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dc.contributor.authorDe Smedt, Johannes
dc.contributor.authorYeshchenko, Anton
dc.contributor.authorPolyvyanyy, Artem
dc.contributor.authorDe Weerdt, Jochen
dc.contributor.authorMendling, Jan
dc.date.accessioned2024-05-02T15:07:56Z
dc.date.available2024-05-02T15:07:56Z
dc.date.issued2023
dc.description.abstractProcess analytics is a collection of data-driven techniques for, among others, making predictions for individual process instances or overall process models. At the instance level, various novel techniques have been recently devised, tackling analytical tasks such as next activity, remaining time, or outcome prediction. However, there is a notable void regarding predictions at the process model level. It is the ambition of this article to fill this gap. More specifically, we develop a technique to forecast the entire process model from historical event data. A forecasted model is a will-be process model representing a probable description of the overall process for a given period in the future. Such a forecast helps, for instance, to anticipate and prepare for the consequences of upcoming process drifts and emerging bottlenecks. Our technique builds on a representation of event data as multiple time series, each capturing the evolution of a behavioural aspect of the process model, such that corresponding time series forecasting techniques can be applied. Our implementation demonstrates the feasibility of process model forecasting using real-world event data. A user study using our Process Change Exploration tool confirms the usefulness and ease of use of the produced process model forecasts.
dc.description.sponsorshipThis research was supported by the Research Foundation Flanders, Belgium under grant G039923N and KU Leuven, Belgium under project 3H200414. The research by Artem Polyvyanyy was in part supported by the Australian Research Council under project DP220101516. The research by Jan Mendling was supported by the Einstein Foundation Berlin under grant EPP-2019-524 and by the German Federal Ministry of Education and Research under grant 16DII133.
dc.identifier.citationDe Smedt, J., Yeshchenko, A., Polyvyanyy, A., De Weerdt, J., & Mendling, J. (2023). Process model forecasting and change exploration using time series analysis of event sequence data. Data & Knowledge Engineering, 145, 102145. https://doi.org/10.1016/j.datak.2023.102145
dc.identifier.doihttps://doi.org/10.1016/j.datak.2023.102145
dc.identifier.issn0169-023X
dc.identifier.urihttps://www.weizenbaum-library.de/handle/id/675
dc.language.isoeng
dc.rightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectProcess model forecasting
dc.subjectPredictive process modelling
dc.subjectProcess mining
dc.subjectTime series analysis
dc.subjectUser study
dc.titleProcess model forecasting and change exploration using time series analysis of event sequence data
dc.typeArticle
dc.type.statuspublishedVersion
dcmi.typeText
dcterms.bibliographicCitation.urlhttps://doi.org/10.1016/j.datak.2023.102145
local.researchgroupSicherheit und Transparenz digitaler Prozesse
local.researchtopicDigitale Infrastrukturen in der Demokratie
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