Mining process models with non-free-choice constructs View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2007-10

AUTHORS

Lijie Wen, Wil M. P. van der Aalst, Jianmin Wang, Jiaguang Sun

ABSTRACT

Process mining aims at extracting information from event logs to capture the business process as it is being executed. Process mining is particularly useful in situations where events are recorded but there is no system enforcing people to work in a particular way. Consider for example a hospital where the diagnosis and treatment activities are recorded in the hospital information system, but where health-care professionals determine the “careflow.” Many process mining approaches have been proposed in recent years. However, in spite of many researchers’ persistent efforts, there are still several challenging problems to be solved. In this paper, we focus on mining non-free-choice constructs, i.e., situations where there is a mixture of choice and synchronization. Although most real-life processes exhibit non-free-choice behavior, existing algorithms are unable to adequately deal with such constructs. Using a Petri-net-based representation, we will show that there are two kinds of causal dependencies between tasks, i.e., explicit and implicit ones. We propose an algorithm that is able to deal with both kinds of dependencies. The algorithm has been implemented in the ProM framework and experimental results shows that the algorithm indeed significantly improves existing process mining techniques. More... »

PAGES

145-180

References to SciGraph publications

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  • 1996. An incremental interactive algorithm for regular grammar inference in GRAMMATICAL INTERFERENCE: LEARNING SYNTAX FROM SENTENCES
  • 2006. Conformance Testing: Measuring the Fit and Appropriateness of Event Logs and Process Models in BUSINESS PROCESS MANAGEMENT WORKSHOPS
  • 2000. A Machine Learning Approach to Workflow Management in MACHINE LEARNING: ECML 2000
  • 2004. Mining Expressive Process Models by Clustering Workflow Traces in ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING
  • 2001-07. Learning DFA from Simple Examples in MACHINE LEARNING
  • 2006. Detecting Implicit Dependencies Between Tasks from Event Logs in FRONTIERS OF WWW RESEARCH AND DEVELOPMENT - APWEB 2006
  • 1990-03. Partial (set) 2-structures in ACTA INFORMATICA
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10618-007-0065-y

    DOI

    http://dx.doi.org/10.1007/s10618-007-0065-y

    DIMENSIONS

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