Classifying Process Instances Using Recurrent Neural Networks

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

Researchers

Research units

  • QPR Software Plc

Abstract

Process Mining consists of techniques where logs created by operative systems are transformed into process models. In process mining tools it is often desired to be able to classify ongoing process instances, e.g., to predict how long the process will still require to complete, or to classify process instances to different classes based only on the activities that have occurred in the process instance thus far. Recurrent neural networks and its subclasses, such as Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), have been demonstrated to be able to learn relevant temporal features for subsequent classification tasks. In this paper we apply recurrent neural networks to classifying process instances. The proposed model is trained in a supervised fashion using labeled process instances extracted from event log traces. This is the first time we know of GRU having been used in classifying business process instances. Our main experimental results shows that GRU outperforms LSTM remarkably in training time while giving almost identical accuracies to LSTM models. Additional contributions of our paper are improving the classification model training time by filtering infrequent activities, which is a technique commonly used, e.g., in Natural Language Processing (NLP).

Details

Original languageEnglish
Title of host publicationBusiness Process Management Workshops - BPM 2018 International Workshops, Revised Papers
EditorsFlorian Daniel, Quan Z. Sheng, Hamid Motahari
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Business Process Management - Sydney, Australia
Duration: 9 Sep 201814 Sep 2018
Conference number: 16

Publication series

NameLecture Notes in Business Information Processing
PublisherSpringer
Volume342
ISSN (Print)1865-1348
ISSN (Electronic)1865-1356

Workshop

WorkshopInternational Conference on Business Process Management
Abbreviated titleBPM
CountryAustralia
CitySydney
Period09/09/201814/09/2018

    Research areas

  • Classification, Deep learning, Gated recurrent unit, Long short-term memory, Machine learning, Natural language processing, Prediction, Process mining, Recurrent neural networks

ID: 31028798