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Deep Learning Process Prediction with Discrete and Continuous Data Features

Title data

Schönig, Stefan ; Jasinski, Richard ; Ackermann, Lars ; Jablonski, Stefan:
Deep Learning Process Prediction with Discrete and Continuous Data Features.
In: ENASE 2018 : Proceedings of the 13th International Conference on Evaluation of Novel Approaches to Software Engineering. - s.l. , 2018 . - pp. 314-319
ISBN 978-989-758-300-1
DOI: https://doi.org/10.5220/0006772003140319

Abstract in another language

Process prediction is a well known method to support participants in performing business processes. These methods use event logs of executed cases as a knowledge base to make predictions for running instances. A range of such techniques have been proposed for different tasks, e.g., for predicting the next activity or the remaining time of a running instance. Neural networks with Long Short-Term Memory architectures have turned out to be highly customizable and precise in predicting the next activity in a running case. Current research, however, focuses on the prediction of future activities using activity labels and resource information while further event log information, in particular discrete and continuous event data is neglected. In this paper, we show how prediction accuracy can significantly be improved by incorporating event data attributes. We regard this extension of conventional algorithms as a substantial contribution to the field of activity prediction. The new approach has been validated with a recent real-life event log.

Further data

Item Type: Article in a book
Refereed: Yes
Institutions of the University: 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
Faculties
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
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
Date Deposited: 16 Jan 2018 11:41
Last Modified: 17 Apr 2019 07:30
URI: https://eref.uni-bayreuth.de/id/eprint/41777