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Evaluating Predictive Business Process Monitoring Approaches on Small Event Logs

Title data

Käppel, Martin ; Jablonski, Stefan ; Schönig, Stefan:
Evaluating Predictive Business Process Monitoring Approaches on Small Event Logs.
In: Paiva, Ana C. R. ; Cavalli, Ana Rosa ; Martins, Paula Ventura ; Pérez-Castillo, Ricardo (ed.): Quality of Information and Communications Technology : 14th International Conference, QUATIC 2021, Algarve, Portugal, September 8–11, 2021, Proceedings. - Cham, Schweiz : Springer , 2021 . - pp. 167-182 . - (Communications in Computer and Information Science ; 1439 )
ISBN 978-3-030-85346-4

Official URL: Volltext

Abstract in another language

Predictive business process monitoring is concerned with the prediction how a running process instance will unfold up to its completion at runtime. Most of the proposed approaches rely on a wide number of machine learning techniques. In the last years numerous studies revealed that these methods can be successfully applied for different prediction targets. However, these techniques require a qualitatively and quantitatively sufficient dataset. Unfortunately, there are many situations in business process management where only a quantitatively insufficient dataset is available. The problem of insufficient data in the context of BPM is still neglected. Hence, none of the comparative studies investigates the performance of predictive business process monitoring techniques in environments with small datasets. In this paper an evaluation framework for comparing existing approaches with regard to their suitability for small datasets is developed and exemplarily applied to state-of-the-art approaches in next activity prediction.

Further data

Item Type: Article in a book
Refereed: Yes
Keywords: Process Mining; Predictive business process monitoring; Small Sample Learning; Process Prediction
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science IV
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science IV > Chair Applied Computer Science IV - Univ.-Prof. Dr.-Ing. Stefan Jablonski
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
Date Deposited: 30 Aug 2021 09:05
Last Modified: 30 Aug 2021 09:05
URI: https://eref.uni-bayreuth.de/id/eprint/66873