A Recursive Paradigm for Aligning Observed Behavior of Large Structured Process Models

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9850)

Abstract

The alignment of observed and modeled behavior is a crucial problem in process mining, since it opens the door for conformance checking and enhancement of process models. The state of the art techniques for the computation of alignments rely on a full exploration of the combination of the model state space and the observed behavior (an event log), which hampers their applicability for large instances. This paper presents a fresh view to the alignment problem: the computation of alignments is casted as the resolution of Integer Linear Programming models, where the user can decide the granularity of the alignment steps. Moreover, a novel recursive strategy is used to split the problem into small pieces, exponentially reducing the complexity of the ILP models to be solved. The contributions of this paper represent a promising alternative to fight the inherent complexity of computing alignments for large instances.

Keywords

Integer Linear Program Optimal Alignment Integer Linear Program Model Firing Sequence Integer Linear Program Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported by the Spanish Ministry for Economy and Competitiveness (MINECO) and the European Union (FEDER funds) under grant COMMAS (ref. TIN2013-46181-C2-1-R).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Universitat Politècnica de CatalunyaBarcelonaSpain

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