Improving Merging Conditions for Recomposing Conformance Checking

  • Wai Lam Jonathan LeeEmail author
  • Jorge Munoz-Gama
  • H. M. W. Verbeek
  • Wil M. P. van der Aalst
  • Marcos Sepúlveda
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 342)


Efficient conformance checking is a hot topic in the field of process mining. Much of the recent work focused on improving the scalability of alignment-based approaches to support the larger and more complex processes. This is needed because process mining is increasingly applied in areas where models and logs are “big”. Decomposition techniques are able to achieve significant performance gains by breaking down a conformance problem into smaller ones. Moreover, recent work showed that the alignment problem can be resolved in an iterative manner by alternating between aligning a set of decomposed sub-components before merging the computed sub-alignments and recomposing sub-components to fix merging issues. Despite experimental results showing the gain of applying recomposition in large scenarios, there is still a need for improving the merging step, where log traces can take numerous recomposition steps before reaching the required merging condition. This paper contributes by defining and structuring the recomposition step, and proposes strategies with significant performance improvement on synthetic and real-life datasets over both the state-of-the-art decomposed and monolithic approaches.


Recomposition Conformance checking Process mining 



This work is partially supported by CONICYT-PCHA/ Doctorado Nacional/2017-21170612, FONDECYT Iniciación 11170092, CONICYT Apoyo a la Formación de Redes Internacionales Para Investigadores en Etapa Inicial REDI170136, the Vicerrectoría de Investigación de la Pontificia Universidad Católica de Chile/Concurso Estadías y Pasantías Breves 2016, and the Departamento de Ciencias de la Computación UC/Fond-DCC-2017-0001. The authors would like to thank Alfredo Bolt for his comments on the data generation details.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Wai Lam Jonathan Lee
    • 1
    Email author
  • Jorge Munoz-Gama
    • 1
  • H. M. W. Verbeek
    • 2
  • Wil M. P. van der Aalst
    • 3
  • Marcos Sepúlveda
    • 1
  1. 1.Pontificia Universidad Católica de ChileSantiagoChile
  2. 2.Eindhoven University of TechnologyEindhovenThe Netherlands
  3. 3.RWTH Aachen UniversityAachenGermany

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