Decision mining in ProM

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

172 Citations (Scopus)

Abstract

Process-aware Information Systems typically log events (e.g., in transaction logs or audit trails) related to the actual business process executions. Proper analysis of these execution logs can yield important knowledge that can help organizations to improve the quality of their services. Starting from a process model, which can be discovered by conventional process mining algorithms, we analyze how data attributes influence the choices made in the process based on past process executions. Decision mining, also referred to as decision point analysis, aims at the detection of data dependencies that affect the routing of a case. In this paper we describe how machine learning techniques can be leveraged for this purpose, and we present a Decision Miner implemented within the ProM framework.
Original languageEnglish
Title of host publicationBusiness Process Management (Proceedings 4th International Conference, BPM 2006, Vienna, Austria, September 5-7, 2006)
EditorsS. Dustdar, J.L. Fiadeiro, A. Sheth
Place of PublicationBerlin
PublisherSpringer
Pages420-425
ISBN (Print)3-540-38901-6
DOIs
Publication statusPublished - 2006
Event4th International Conference on Business Process Management (BPM 2006), September 5-7, 2006, Vienna, Austria - Vienna, Austria
Duration: 5 Sep 20067 Sep 2006

Publication series

NameLecture Notes in Computer Science
Volume4102
ISSN (Print)0302-9743

Conference

Conference4th International Conference on Business Process Management (BPM 2006), September 5-7, 2006, Vienna, Austria
Abbreviated titleBPM 2006
CountryAustria
CityVienna
Period5/09/067/09/06
OtherInternational Conference on Business Process Management ; 4 (Vienna) : 2006.09.05-07

Fingerprint Dive into the research topics of 'Decision mining in ProM'. Together they form a unique fingerprint.

Cite this