Crowd-based mining of reusable process model patterns

Carlos Rodríguez, Florian Daniel, Fabio Casati

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


Process mining is a domain where computers undoubtedly outperform humans. It is a mathematically complex and computationally demanding problem, and event logs are at too low a level of abstraction to be intelligible in large scale to humans. We demonstrate that if instead the data to mine from are models (not logs), datasets are small (in the order of dozens rather than thousands or millions), and the knowledge to be discovered is complex (reusable model patterns), humans outperform computers. We design, implement, run, and test a crowd-based pattern mining approach and demonstrate its viability compared to automated mining. We specifically mine mashup model patterns (we use them to provide interactive recommendations inside a mashup tool) and explain the analogies with mining business process models. The problem is relevant in that reusable model patterns encode valuable modeling and domain knowledge, such as best practices or organizational conventions, from which modelers can learn and benefit when designing own models.

Original languageEnglish
Title of host publicationBusiness Process Management - 12th International Conference, BPM 2014, Proceedings
PublisherSpringer Verlag
Number of pages16
Volume8659 LNCS
ISBN (Print)9783319101712
Publication statusPublished - 2014
Externally publishedYes
Event12th International Conference on Business Process Management, BPM 2014 - Haifa, Israel
Duration: 7 Sep 201411 Sep 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8659 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th International Conference on Business Process Management, BPM 2014


  • Crowdsourcing
  • Mashups
  • Model patterns
  • Pattern mining

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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