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Process Discovery of Business Processes Using Temporal Causal Relation

Journal of Information Systems Engineering and Business Intelligence · Vol. 5 No. 2 (2019) · pp. 183–194

DOI
10.20473/jisebi.5.2.183-194
ISSN
2443-2555, 2598-6333
Published
2019-10-24
Publisher
Universitas Airlangga
Language
eng

Abstract

Background: Nowadays, enterprise computing manages business processes which has grown up rapidly. This situation triggers the production of a massive event log. One type of event log is double timestamp event log. The double timestamp has a start time and complete time of each activity executed in the business process. It also has a close relationship with temporal causal relation. The temporal causal relation is a pattern of event log that occurs from each activity performed in the process.Objective: In this paper, seven types of temporal causal relation between activities were presented as an extended version of relations used in the double timestamp event log. Since the event log was not always executed sequentially, therefore using temporal causal relation, the event log was divided into several small groups to determine the relations of activities and to mine the business process.Methods: In these experiments, the temporal causal relation based on time interval which were presented in Gantt chart also determined whether each case could be classified as sequential or parallel relations. Then to obtain the business process, each temporal causal relation was combined into one business process based on the timestamp of activity in the event log.Results: The experimental results, which were implemented in two real-life event logs, showed that using temporal causal relation and double timestamp event log could discover business process models.Conclusion: Considering the findings, this study concludes that business process models and their sequential and parallel AND, OR, XOR relations can be discovered by using temporal causal relation and double timestamp event log.Keywords:Business Process, Process Discovery, Process Mining, Temporal Causal Relation, Double Timestamp Event Log

Keywords

Collaboration network of the authors

8 authors · 22 co-authorship links · 11 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Airlangga
  • Shih Chien University
  • National Cheng Kung University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Eva HariyantiUniversitas Airlangga1
Indra Kharisma RaharjanaUniversitas Airlangga1
Arya PrasetyaUniversitas Airlangga1
Anindya Wita WisesaUniversitas Airlangga1
Aditya NugrohoShih Chien University1
Afifah NurrosyidahNational Cheng Kung University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://e-journal.unair.ac.id/JISEBI/oai, first harvested 29 Okt 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Process Discovery of Business Processes Using Temporal Causal Relation</dc:title>
	<dc:creator>Effendi, Yutika Amelia</dc:creator>
	<dc:creator>Nuzulita, Nania</dc:creator>
	<dc:subject xml:lang="en-US">Business Process</dc:subject>
	<dc:subject xml:lang="en-US">Process Mining</dc:subject>
	<dc:subject xml:lang="en-US">Process Discovery</dc:subject>
	<dc:subject xml:lang="en-US">Temporal Causal Relation</dc:subject>
	<dc:subject xml:lang="en-US">Double Timestamp Event Log</dc:subject>
	<dc:description xml:lang="en-US">Background: Nowadays, enterprise computing manages business processes which has grown up rapidly. This situation triggers the production of a massive event log. One type of event log is double timestamp event log. The double timestamp has a start time and complete time of each activity executed in the business process. It also has a close relationship with temporal causal relation. The temporal causal relation is a pattern of event log that occurs from each activity performed in the process.Objective: In this paper, seven types of temporal causal relation between activities were presented as an extended version of relations used in the double timestamp event log. Since the event log was not always executed sequentially, therefore using temporal causal relation, the event log was divided into several small groups to determine the relations of activities and to mine the business process.Methods: In these experiments, the temporal causal relation based on time interval which were presented in Gantt chart also determined whether each case could be classified as sequential or parallel relations. Then to obtain the business process, each temporal causal relation was combined into one business process based on the timestamp of activity in the event log.Results: The experimental results, which were implemented in two real-life event logs, showed that using temporal causal relation and double timestamp event log could discover business process models.Conclusion: Considering the findings, this study concludes that business process models and their sequential and parallel AND, OR, XOR relations can be discovered by using temporal causal relation and double timestamp event log.Keywords:Business Process, Process Discovery, Process Mining, Temporal Causal Relation, Double Timestamp Event Log</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Airlangga</dc:publisher>
	<dc:date>2019-10-24</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://e-journal.unair.ac.id/JISEBI/article/view/15040</dc:identifier>
	<dc:identifier>10.20473/jisebi.5.2.183-194</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Information Systems Engineering and Business Intelligence; Vol. 5 No. 2 (2019): October; 183-194</dc:source>
	<dc:source>2443-2555</dc:source>
	<dc:source>2598-6333</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://e-journal.unair.ac.id/JISEBI/article/view/15040/8505</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2019 The Authors. Published by Universitas Airlangga.</dc:rights>
</oai_dc:dc>