Skip to main content
Top
Published in: Business & Information Systems Engineering 6/2019

23-04-2018 | Research Paper

Generating Artificial Data for Empirical Analysis of Control-flow Discovery Algorithms

A Process Tree and Log Generator

Authors: Toon Jouck, Prof. Dr. Benoît Depaire

Published in: Business & Information Systems Engineering | Issue 6/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Within the process mining domain, research on comparing control-flow (CF) discovery techniques has gained importance. A crucial building block of empirical analysis of CF discovery techniques is obtaining the appropriate evaluation data. Currently, there is no answer to the question of how to collect such evaluation data. The paper introduces a methodology for generating artificial event data (GED) and an implementation called the Process Tree and Log Generator. The GED methodology and its implementation provide users with full control over the characteristics of the generated event data and an integration within the ProM framework. Unlike existing approaches, there is no tradeoff between including long-term dependencies and soundness of the process. The contributions of the paper provide a solution for a necessary step in the empirical analysis of CF discovery algorithms.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Show more products
Footnotes
1
Noise is defined in this paper as incorrect behavior in the log (see Sect. 4.4).
 
2
Invisible activities are endpoints in the tree and hence are never selected to be replaced.
 
3
Reducing parent and child loop nodes could cause the parent loop node to have more than three children.
 
4
A descendant is a node reachable by repeatedly going from parent to child.
 
5
Tree \(PT_1 = \circlearrowleft ^{k}(\times (a,b),c,d)\) is not trace equivalent to tree \(PT_2 = \times (\circlearrowleft ^{k}(a,c,d),\circlearrowleft ^{k}(b,c,d))\).
 
6
Notice that activity e can be repeated once.
 
8
These mean relative frequencies of the operator types were calculated before the trees were reduced.
 
9
The scalability of the log generation is outside the scope of this paper as the focus is on model generation with LT dependencies.
 
Literature
go back to reference Jouck T, Depaire B (2016) PTandLogGenerator: a generator for artificial event data. In: Proceedings of the BPM Demo Track 2016 (BPMD 2016), CEUR workshop proceedings, Rio de Janeiro, vol 1789, pp 23–27. http://ceur-ws.org/Vol-1789/. Accessed 05 Jan 2018 Jouck T, Depaire B (2016) PTandLogGenerator: a generator for artificial event data. In: Proceedings of the BPM Demo Track 2016 (BPMD 2016), CEUR workshop proceedings, Rio de Janeiro, vol 1789, pp 23–27. http://​ceur-ws.​org/​Vol-1789/​. Accessed 05 Jan 2018
go back to reference Weijters A, Ribeiro J (2011) Flexible heuristics miner (FHM). In: 2011 IEEE symposium on computational intelligence and data mining (CIDM), pp 310–317 Weijters A, Ribeiro J (2011) Flexible heuristics miner (FHM). In: 2011 IEEE symposium on computational intelligence and data mining (CIDM), pp 310–317
Metadata
Title
Generating Artificial Data for Empirical Analysis of Control-flow Discovery Algorithms
A Process Tree and Log Generator
Authors
Toon Jouck
Prof. Dr. Benoît Depaire
Publication date
23-04-2018
Publisher
Springer Fachmedien Wiesbaden
Published in
Business & Information Systems Engineering / Issue 6/2019
Print ISSN: 2363-7005
Electronic ISSN: 1867-0202
DOI
https://doi.org/10.1007/s12599-018-0541-5

Other articles of this Issue 6/2019

Business & Information Systems Engineering 6/2019 Go to the issue

Premium Partner