Supporting Provenance and Data Awareness in Exploratory Process Mining

Francesca Zerbato*, Andrea Burattin, Hagen Völzer, Paul Nelson Becker, Elia Boscaini, Barbara Weber

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Abstract

Like other analytic fields, process mining is complex and knowledge-intensive and, thus, requires the substantial involvement of human analysts. The analysis process unfolds into many steps, producing multiple results and artifacts that analysts need to validate, reproduce and potentially reuse. We propose a system supporting the validation, reproducibility, and reuse of analysis results via analytic provenance and data awareness. This aims at increasing the transparency and rigor of exploratory process mining analysis as a basis for its stepwise maturation. We outline the purpose of the system, describe the problems it addresses, derive requirements and propose a design satisfying these requirements. We then demonstrate the feasibility of the central aspects of the design.
Original languageEnglish
Title of host publicationProceedings of CAiSE: International Conference on Advanced Information Systems Engineering
Volume13901
PublisherSpringer
Publication date2023
Pages454–470
ISBN (Print)978-3-030-79381-4
ISBN (Electronic)978-3-030-79382-1
DOIs
Publication statusPublished - 2023
Event35th International Conference on Advanced Information Systems Engineering - Zaragoza, Spain
Duration: 12 Jun 202316 Jun 2023
Conference number: 35

Conference

Conference35th International Conference on Advanced Information Systems Engineering
Number35
Country/TerritorySpain
CityZaragoza
Period12/06/202316/06/2023

Keywords

  • Process Mining
  • Exploratory Analysis
  • System Requirements and Design
  • Analytic Provenance
  • Data Awareness
  • User Support

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