Process Model Forecasting Using Time Series Analysis of Event Sequence Data

Johannes De Smedt, Anton Yeshchenko, Artem Polyvyanny, Jochen De Weerdt, Jan Mendling

Publication: Chapter in book/Conference proceedingContribution to conference proceedings

Abstract

Process analytics is an umbrella of data-driven techniques which includes making predictions for individual process instances or overall process models. At the instance level, various novel techniques have been recently devised, tackling next activity, remaining time, and outcome prediction. At the model level, there is a notable void. It is the ambition of this paper to fill this gap. To this end, 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 future state of the overall process. Such a forecast helps to investigate the consequences of drift 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 forecasting techniques can be applied. Our implementation demonstrates the accuracy of our technique on real-world event log data.
Original languageEnglish
Title of host publicationProcess Model Forecasting Using Time Series Analysis of Event Sequence Data
Editors Aditya Ghose, Jennifer Horkoff, Vítor E. Silva Souza, Jeffrey Parsons, Joerg Evermann
Place of PublicationInternational Conference on Conceptual Modeling
PublisherSpringer
Pages47 - 61
ISBN (Print)978-3-030-89021-6
DOIs
Publication statusPublished - 2021

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