Ensuring Model Consistency in Declarative Process Discovery View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

Claudio Di Ciccio , Fabrizio Maria Maggi , Marco Montali , Jan Mendling

ABSTRACT

Declarative process models define the behaviour of business processes as a set of constraints. Declarative process discovery aims at inferring such constraints from event logs. Existing discovery techniques verify the satisfaction of candidate constraints over the log, but completely neglect their interactions. As a result, the inferred constraints can be mutually contradicting and their interplay may lead to an inconsistent process model that does not accept any trace. In such a case, the output turns out to be unusable for enactment, simulation or verification purposes. In addition, the discovered model contains, in general, redundancies that are due to complex interactions of several constraints and that cannot be solved using existing pruning approaches. We address these problems by proposing a technique that automatically resolves conflicts within the discovered models and is more powerful than existing pruning techniques to eliminate redundancies. First, we formally define the problems of constraint redundancy and conflict resolution. Thereafter, we introduce techniques based on the notion of an automata-product monoid that guarantee the consistency of the discovered models and, at the same time, keep the most interesting constraints in the pruned set. We evaluate the devised techniques on real-world benchmarks. More... »

PAGES

144-159

References to SciGraph publications

  • 2007-10. Mining process models with non-free-choice constructs in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2009. Exploiting Inductive Logic Programming Techniques for Declarative Process Mining in TRANSACTIONS ON PETRI NETS AND OTHER MODELS OF CONCURRENCY II
  • 2012. Patterns for a Log-Based Strengthening of Declarative Compliance Models in INTEGRATED FORMAL METHODS
  • 2010. Probabilistic Declarative Process Mining in KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT
  • 2012. Efficient Discovery of Understandable Declarative Process Models from Event Logs in ACTIVE FLOW AND COMBUSTION CONTROL 2018
  • 2007. Inducing Declarative Logic-Based Models from Labeled Traces in BUSINESS PROCESS MANAGEMENT
  • 2013. A Knowledge-Based Integrated Approach for Discovering and Repairing Declare Maps in ADVANCED INFORMATION SYSTEMS ENGINEERING
  • 2013. Declarative Modeling–An Academic Dream or the Future for BPM? in BUSINESS PROCESS MANAGEMENT
  • Book

    TITLE

    Business Process Management

    ISBN

    978-3-319-23062-7
    978-3-319-23063-4

    From Grant

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-23063-4_9

    DOI

    http://dx.doi.org/10.1007/978-3-319-23063-4_9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1045100521


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