Efficient Discovery of Understandable Declarative Process Models from Event Logs View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2012

AUTHORS

Fabrizio M. Maggi , R. P. Jagadeesh Chandra Bose , Wil M. P. van der Aalst

ABSTRACT

Process mining techniques often reveal that real-life processes are more variable than anticipated. Although declarative process models are more suitable for less structured processes, most discovery techniques generate conventional procedural models. In this paper, we focus on discovering Declare models based on event logs. A Declare model is composed of temporal constraints. Despite the suitability of declarative process models for less structured processes, their discovery is far from trivial. Even for smaller processes there are many potential constraints. Moreover, there may be many constraints that are trivially true and that do not characterize the process well. Naively checking all possible constraints is computationally intractable and may lead to models with an excessive number of constraints. Therefore, we have developed an Apriori algorithm to reduce the search space. Moreover, we use new metrics to prune the model. As a result, we can quickly generate understandable Declare models for real-life event logs. More... »

PAGES

270-285

References to SciGraph publications

  • 2007-04. Genetic process mining: an experimental evaluation in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2005. Process Mining and Verification of Properties: An Approach Based on Temporal Logic in ON THE MOVE TO MEANINGFUL INTERNET SYSTEMS 2005: COOPIS, DOA, AND ODBASE
  • 2011. Process Mining, Discovery, Conformance and Enhancement of Business Processes in NONE
  • 2011. The Impact of Testcases on the Maintainability of Declarative Process Models in ENTERPRISE, BUSINESS-PROCESS AND INFORMATION SYSTEMS MODELING
  • 2003-02. Vacuity detection in temporal model checking in INTERNATIONAL JOURNAL ON SOFTWARE TOOLS FOR TECHNOLOGY TRANSFER
  • 2007. Inducing Declarative Logic-Based Models from Labeled Traces in BUSINESS PROCESS MANAGEMENT
  • 2007. Fuzzy Mining – Adaptive Process Simplification Based on Multi-perspective Metrics in BUSINESS PROCESS MANAGEMENT
  • 2011. Automatic Verification of Data-Centric Business Processes in BUSINESS PROCESS MANAGEMENT
  • 1998. Mining process models from workflow logs in ADVANCES IN DATABASE TECHNOLOGY — EDBT'98
  • Book

    TITLE

    Active Flow and Combustion Control 2018

    ISBN

    978-3-319-98176-5
    978-3-319-98177-2

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-31095-9_18

    DOI

    http://dx.doi.org/10.1007/978-3-642-31095-9_18

    DIMENSIONS

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