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• W1413304420 startingPage "144" @default.
• W1413304420 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." @default.
• W1413304420 created "2016-06-24" @default.
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• W1413304420 creator A5047795784 @default.
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• W1413304420 date "2015-01-01" @default.
• W1413304420 modified "2023-09-27" @default.
• W1413304420 title "Ensuring Model Consistency in Declarative Process Discovery" @default.
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• W1413304420 doi "https://doi.org/10.1007/978-3-319-23063-4_9" @default.
• W1413304420 hasPublicationYear "2015" @default.
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