Process discovery using in-database minimum self distance abstractions

Alifah Syamsiyah, Sander J.J. Leemans

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

Abstract

Process executions generate event data that are typically stored in legacy information systems, such as databases. However, process discovery, which requires such event data, is performed in main memory. To bridge this gap, existing techniques must transform and extract event data, which can be expensive steps. This issue has been addressed by processing the event data directly in their origin. However, existing methods rely only on the simplest event data abstraction: the Directly Follows (DF) abstraction. This paper improves upon these existing works by considering another abstraction, the Minimum Self Distance (MSD) abstraction, which enables discovery of a larger class of models than the DF alone. That is, we propose IMw, a process discovery technique without logs and uses both the MSD and DF abstractions. Furthermore, this work proposes an approach to compute the MSD abstraction in-database, thus avoiding the need for transforming and moving event data. We evaluate IMw with real-life logs, and the experimental results show that IMw with in-database abstraction is faster than the traditional approach, aware of dynamic updates on event data, and able to discover models with pareto-optimal results, compared to existing techniques.

Original languageEnglish
Title of host publication35th Annual ACM Symposium on Applied Computing, SAC 2020
PublisherAssociation for Computing Machinery, Inc
Pages26-35
Number of pages10
ISBN (Electronic)9781450368667
DOIs
Publication statusPublished - 30 Mar 2020
Event35th Annual ACM Symposium on Applied Computing, SAC 2020 - Brno, Czech Republic
Duration: 30 Mar 20203 Apr 2020

Conference

Conference35th Annual ACM Symposium on Applied Computing, SAC 2020
Country/TerritoryCzech Republic
CityBrno
Period30/03/203/04/20

Keywords

  • In-database abstraction
  • Minimum self distance
  • Process discovery

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