Discovery of Multi-perspective Declarative Process Models View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2016

AUTHORS

Stefan Schönig , Claudio Di Ciccio , Fabrizio M. Maggi , Jan Mendling

ABSTRACT

Process discovery is one of the main branches of process mining that allows the user to build a process model representing the process behavior as recorded in the logs. Standard process discovery techniques produce as output a procedural process model (e.g., a Petri net). Recently, several approaches have been developed to derive declarative process models from logs and have been proven to be more suitable to analyze processes working in environments that are less stable and predictable. However, a large part of these techniques are focused on the analysis of the control flow perspective of a business process. Therefore, one of the challenges still open in this field is the development of techniques for the analysis of business processes also from other perspectives, like data, time, and resources. In this paper, we present a full-fledged approach for the discovery of multi-perspective declarative process models from event logs that allows the user to discover declarative models taking into consideration all the information an event log can provide. The approach has been implemented and experimented in real-life case studies. More... »

PAGES

87-103

References to SciGraph publications

  • 2012. Looking into the Future in ON THE MOVE TO MEANINGFUL INTERNET SYSTEMS: OTM 2012
  • 2011. Process Mining, Discovery, Conformance and Enhancement of Business Processes in NONE
  • 2013. Discovering Data-Aware Declarative Process Models from Event Logs in BUSINESS PROCESS MANAGEMENT
  • 2013. Enhancing Declare Maps Based on Event Correlations in BUSINESS PROCESS MANAGEMENT
  • 2015. Ensuring Model Consistency in Declarative Process Discovery in BUSINESS PROCESS MANAGEMENT
  • 2009. Exploiting Inductive Logic Programming Techniques for Declarative Process Mining in TRANSACTIONS ON PETRI NETS AND OTHER MODELS OF CONCURRENCY II
  • 2008. Applying Inductive Logic Programming to Process Mining in INDUCTIVE LOGIC PROGRAMMING
  • 2012. Efficient Discovery of Understandable Declarative Process Models from Event Logs in ACTIVE FLOW AND COMBUSTION CONTROL 2018
  • 2014. Log-Based Understanding of Business Processes through Temporal Logic Query Checking in ON THE MOVE TO MEANINGFUL INTERNET SYSTEMS: OTM 2014 CONFERENCES
  • 2014. Discovering Metric Temporal Business Constraints from Event Logs in PERSPECTIVES IN BUSINESS INFORMATICS RESEARCH
  • 2012. Imperative versus Declarative Process Modeling Languages: An Empirical Investigation in BUSINESS PROCESS MANAGEMENT WORKSHOPS
  • 2016. Efficient and Customisable Declarative Process Mining with SQL in ADVANCED INFORMATION SYSTEMS ENGINEERING
  • 2003-02. Vacuity detection in temporal model checking in INTERNATIONAL JOURNAL ON SOFTWARE TOOLS FOR TECHNOLOGY TRANSFER
  • 2013. A Knowledge-Based Integrated Approach for Discovering and Repairing Declare Maps in ADVANCED INFORMATION SYSTEMS ENGINEERING
  • Book

    TITLE

    Service-Oriented Computing

    ISBN

    978-3-319-46294-3
    978-3-319-46295-0

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-46295-0_6

    DOI

    http://dx.doi.org/10.1007/978-3-319-46295-0_6

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1031130383


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