Redo log process mining in real life: data challenges & opportunities

E. González López de Murillas, G.E. Hoogendoorn, H.A. Reijers

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

2 Citations (Scopus)
1 Downloads (Pure)

Abstract

Data extraction and preparation are the most time-consuming phases of any process mining project. Due to the variability on the sources of event data, it remains a highly manual process in most of the cases. Moreover, it is very difficult to obtain reliable event data in enterprise systems that are not process-aware. Some techniques, like redo log process mining, try to solve these issues by automating the process as much as possible, and enabling event extraction in systems that are not process aware. This paper presents the challenges faced by redo log, and traditional process mining, comparing both approaches at theoretical and practical levels. Finally, we demonstrate that the data obtained with redo log process mining in a real-life environment is, at least, as valid as the one extracted by the traditional approach.

Original languageEnglish
Title of host publicationBusiness Process Management Workshops
Subtitle of host publicationBPM 2017 International Workshops, Barcelona, Spain, September 10-11, 2017, Revised Papers
EditorsE. Teniente, M. Weidlich
Place of PublicationDordrecht
PublisherSpringer
Pages573-587
Number of pages15
ISBN (Electronic)978-3-319-74030-0
ISBN (Print)978-3-319-74029-4
DOIs
Publication statusPublished - 2018
Event15th International Conference on Business Process Management (BPM 2017) - Barcelona, Spain
Duration: 10 Sep 201715 Sep 2017
Conference number: 15
https://bpm2017.cs.upc.edu/

Publication series

NameLecture Notes in Business Information Processing
Volume308
ISSN (Print)1865-1348

Conference

Conference15th International Conference on Business Process Management (BPM 2017)
Abbreviated titleBPM 2017
CountrySpain
CityBarcelona
Period10/09/1715/09/17
Internet address

Keywords

  • Data quality
  • Databases
  • Event logs
  • Process mining
  • Redo logs

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