Exploiting Event Log Event Attributes in RNN Based Prediction

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


Research units

  • QPR Software Plc
  • University of Helsinki


In predictive process analytics, current and historical process data in event logs are used to predict future. E.g., to predict the next activity or how long a process will still require to complete. Recurrent neural networks (RNN) and its subclasses have been demonstrated to be well suited for creating prediction models. Thus far, event attributes have not been fully utilized in these models. The biggest challenge in exploiting them in prediction models is the potentially large amount of event attributes and attribute values. We present a novel clustering technique which allows for trade-offs between prediction accuracy and the time needed for model training and prediction. As an additional finding, we also find that this clustering method combined with having raw event attribute values in some cases provides even better prediction accuracy at the cost of additional time required for training and prediction.


Original languageEnglish
Title of host publicationNew Trends in Databases and Information Systems - ADBIS 2019 Short Papers, Workshops BBIGAP, QAUCA, SemBDM, SIMPDA, M2P, MADEISD, and Doctoral Consortium 2019, Proceedings
EditorsTatjana Welzer, Vili Podgorelec, Aida Kamišalic Latific, Johann Eder, Robert Wrembel, Mikolaj Morzy, Mirjana Ivanovic, Johann Gamper, Theodoros Tzouramanis, Jérôme Darmont
Publication statusPublished - 1 Jan 2019
MoE publication typeA4 Article in a conference publication
EventEuropean Conference on Advances in Databases and Information Systems - Bled, Slovenia
Duration: 8 Sep 201911 Sep 2019
Conference number: 23

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


ConferenceEuropean Conference on Advances in Databases and Information Systems
Abbreviated titleADBIS

    Research areas

  • Gated Recurrent Unit, Prediction, Predictive process analytics, Process mining, Recurrent neural networks

ID: 37821919