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 cured 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. Second, we introduce techniques based on the notion of automata-product monoid, which guarantees the consistency of the discovered models and, at the same time, keeps the most interesting constraints in the pruned set. The level of interestingness is dictated by user-specified prioritisation criteria. We evaluate the devised techniques on a set of real-world event logs.
Highlights
The automated discovery of processes is the branch of the process mining discipline that aims at constructing a process model on the basis of the information reported in event data
We show that our approach is capable of pruning the discovered models by detecting inconsistencies within the constraints discovered by two state-of-the-art declarative process discovery algorithms: MINERful and Declare Maps Miner
We addressed the problems of eliminating redundant and inconsistent constraint sets that are potentially generated by declarative process mining tools
Summary
The automated discovery of processes is the branch of the process mining discipline that aims at constructing a process model on the basis of the information reported in event data. The underlying assumption is that the recorded events indicate the sequential execution of the to-be-discovered process activities. The compact and correct representation of the behaviour observed in event data is one of the major concerns of process mining. Process discovery algorithms are classified according to the type of process model that they return, i.e., either procedural or declarative. Procedural process discovery techniques return models that explicitly describe all the possible executions allowed by the process from the beginning to the end. The output of declarative process discovery algorithms consists of a set of constraints, which exert conditions on the enactment of the process activities. The possible executions are implicitly established as all those ones that respect the given constraints. Mutual strengths and weaknesses of declarative and procedural models are discussed in [1, 2]
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