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Integrating computer log files for process mining: a genetic algorithm inspired technique

Jan Claes UGent and Geert Poels UGent (2011) LECTURE NOTES IN BUSINESS INFORMATION PROCESSING. 83. p.282-293
abstract
Process mining techniques are applied to single computer log files. But many processes are supported by different software tools and are by consequence recorded into multiple log files. Therefore it would be interesting to find a way to automatically combine such a set of log files for one process. In this paper we describe a technique for merging log files based on a genetic algorithm. We show with a generated test case that this technique works and we give an extended overview of which research is needed to optimise and validate this technique.
Please use this url to cite or link to this publication:
author
organization
year
type
conference
publication status
published
subject
keyword
Business Process Modeling, Process Mining, Tool-Support for Modeling, Process Discovery, Log File Merging
in
LECTURE NOTES IN BUSINESS INFORMATION PROCESSING
Lect. Notes Bus. Inf. Process.
editor
Camille Salinesi and Oscar Pastor
volume
83
issue title
Advanced information systems engineering workshops
pages
282 - 293
publisher
Springer
place of publication
Berlin, Germany
conference name
1st Workshop on Integration of IS Engineering Tools (INISET 2011)
conference location
London, UK
conference start
2011-06-20
conference end
2011-06-24
Web of Science type
Proceedings Paper
Web of Science id
000301989300026
ISSN
1865-1348
ISBN
9783642220555
language
English
UGent publication?
yes
classification
P1
copyright statement
I have transferred the copyright for this publication to the publisher
id
1198364
handle
http://hdl.handle.net/1854/LU-1198364
date created
2011-03-28 22:04:17
date last changed
2015-06-17 09:31:16
@inproceedings{1198364,
  abstract     = {Process mining techniques are applied to single computer log files. But many processes are supported by different software tools and are by consequence recorded into multiple log files. Therefore it would be interesting to find a way to automatically combine such a set of log files for one process. In this paper we describe a technique for merging log files based on a genetic algorithm. We show with a generated test case that this technique works and we give an extended overview of which research is needed to optimise and validate this technique.},
  author       = {Claes, Jan and Poels, Geert},
  booktitle    = {LECTURE NOTES IN BUSINESS INFORMATION PROCESSING},
  editor       = {Salinesi, Camille and Pastor, Oscar},
  isbn         = {9783642220555},
  issn         = {1865-1348},
  keyword      = {Business Process Modeling,Process Mining,Tool-Support for Modeling,Process Discovery,Log File Merging},
  language     = {eng},
  location     = {London, UK},
  pages        = {282--293},
  publisher    = {Springer},
  title        = {Integrating computer log files for process mining: a genetic algorithm inspired technique},
  volume       = {83},
  year         = {2011},
}

Chicago
Claes, Jan, and Geert Poels. 2011. “Integrating Computer Log Files for Process Mining: a Genetic Algorithm Inspired Technique.” In Lecture Notes in Business Information Processing, ed. Camille Salinesi and Oscar Pastor, 83:282–293. Berlin, Germany: Springer.
APA
Claes, J., & Poels, G. (2011). Integrating computer log files for process mining: a genetic algorithm inspired technique. In Camille Salinesi & O. Pastor (Eds.), LECTURE NOTES IN BUSINESS INFORMATION PROCESSING (Vol. 83, pp. 282–293). Presented at the 1st Workshop on Integration of IS Engineering Tools (INISET 2011), Berlin, Germany: Springer.
Vancouver
1.
Claes J, Poels G. Integrating computer log files for process mining: a genetic algorithm inspired technique. In: Salinesi C, Pastor O, editors. LECTURE NOTES IN BUSINESS INFORMATION PROCESSING. Berlin, Germany: Springer; 2011. p. 282–93.
MLA
Claes, Jan, and Geert Poels. “Integrating Computer Log Files for Process Mining: a Genetic Algorithm Inspired Technique.” Lecture Notes in Business Information Processing. Ed. Camille Salinesi & Oscar Pastor. Vol. 83. Berlin, Germany: Springer, 2011. 282–293. Print.