Inducing Declarative Logic-Based Models from Labeled Traces

  • Evelina Lamma
  • Paola Mello
  • Marco Montali
  • Fabrizio Riguzzi
  • Sergio Storari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4714)


In this work we propose an approach for the automatic discovery of logic-based models starting from a set of process execution traces. The approach is based on a modified Inductive Logic Programming algorithm, capable of learning a set of declarative rules.

The advantage of using a declarative description is twofold. First, the process is represented in an intuitive and easily readable way; second, a family of proof procedures associated to the chosen language can be used to support the monitoring and management of processes (conformance testing, properties verification and interoperability checking, in particular).

The approach consists in first learning integrity constraints expressed as logical formulas and then translating them into a declarative graphical language named DecSerFlow.

We demonstrate the viability of the approach by applying it to a real dataset from a health case process and to an artificial dataset from an e-commerce protocol.


Process mining Process verification and validation Logic Programming DecSerFlow Careflow 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Evelina Lamma
    • 1
  • Paola Mello
    • 2
  • Marco Montali
    • 2
  • Fabrizio Riguzzi
    • 1
  • Sergio Storari
    • 1
  1. 1.ENDIF – Università di Ferrara, Via Saragat, 1 – 44100 – FerraraItaly
  2. 2.DEIS – Università di Bologna, viale Risorgimento, 2 – 40136 – BolognaItaly

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