Attention mechanism in predictive business process monitoring

Abdulrahman Jalayer, Mohsen Kahani, Amin Beheshti, Asef Pourmasoumi, Hamid Reza Motahari-Nezhad

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

Abstract

Business process monitoring techniques have been investigated in depth over the last decade to enable organizations to deliver process insight. Recently, a new stream of work in predictive business process monitoring leveraged deep learning techniques to unlock the potential business value locked in process execution event logs. These works use Recurrent Neural Networks, such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), and suffer from misinformation and accuracy as they use the last hidden state (as the context vector) for the purpose of predicting the next event. On the other hand, in operational processes, traces may be very long, which makes the above methods inappropriate for analyzing them. In addition, in predicting the next events in a running case, some of the previous events should be given a higher priority. To address these shortcomings, in this paper, we present a novel approach inspired by the notion of attention mechanism, utilized in Natural Language Processing and, particularly, in Neural Machine Translation. Our proposed approach uses all hidden states to accurately predict future behavior and the outcome of individual activities. Experimental evaluation of real-world event logs revealed that the use of attention mechanisms in the proposed approach leads to a more accurate prediction.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 24th International Enterprise Distributed Object Computing Conference, EDOC 2020
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages181-186
Number of pages6
ISBN (Electronic)9781728164731
DOIs
Publication statusPublished - 2020
Event24th IEEE International Enterprise Distributed Object Computing Conference, EDOC 2020 - Eindhoven, Netherlands
Duration: 5 Oct 20208 Oct 2020

Publication series

NameIEEE International Enterprise Distributed Object Computing Conference-EDOC
PublisherIEEE COMPUTER SOC
ISSN (Print)2325-6354

Conference

Conference24th IEEE International Enterprise Distributed Object Computing Conference, EDOC 2020
CountryNetherlands
CityEindhoven
Period5/10/208/10/20

Keywords

  • Attention Mechanism
  • Business Process Management
  • Deep Learning
  • LSTM
  • Predictive Process Monitoring
  • Process Mining
  • Seq2Seq

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