Discovering business area effects to process mining analysis using clustering and influence analysis

Teemu Lehto*, Markku Hinkka

*Corresponding author for this work

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

Abstract

A common challenge for improving business processes in large organizations is that business people in charge of the operations are lacking a fact-based understanding of the execution details, process variants, and exceptions taking place in business operations. While existing process mining methodologies can discover these details based on event logs, it is challenging to communicate the process mining findings to business people. In this paper, we present a novel methodology for discovering business areas that have a significant effect on the process execution details. Our method uses clustering to group similar cases based on process flow characteristics and then influence analysis for detecting those business areas that correlate most with the discovered clusters. Our analysis serves as a bridge between BPM people and business people, facilitating the knowledge sharing between these groups. We also present an example analysis based on publicly available real-life purchase order process data.

Original languageEnglish
Title of host publicationBusiness Information Systems - 23rd International Conference, BIS 2020, Proceedings
EditorsWitold Abramowicz, Gary Klein
Pages236-248
Number of pages13
DOIs
Publication statusPublished - 2020
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Business Information Systems - Colorado Springs, United States
Duration: 8 Jun 202010 Jun 2020
Conference number: 23

Publication series

NameLecture Notes in Business Information Processing
PublisherSpringer
Volume389 LNBIP
ISSN (Print)1865-1348
ISSN (Electronic)1865-1356

Conference

ConferenceInternational Conference on Business Information Systems
Abbreviated titleBIS
CountryUnited States
CityColorado Springs
Period08/06/202010/06/2020

Keywords

  • Business area
  • Classification rule mining
  • Clustering
  • Contribution
  • Data mining
  • Influence analysis
  • Process mining

Fingerprint Dive into the research topics of 'Discovering business area effects to process mining analysis using clustering and influence analysis'. Together they form a unique fingerprint.

  • Cite this

    Lehto, T., & Hinkka, M. (2020). Discovering business area effects to process mining analysis using clustering and influence analysis. In W. Abramowicz, & G. Klein (Eds.), Business Information Systems - 23rd International Conference, BIS 2020, Proceedings (pp. 236-248). (Lecture Notes in Business Information Processing; Vol. 389 LNBIP). https://doi.org/10.1007/978-3-030-53337-3_18