Parallel Algorithms for the Automated Discovery of Declarative Process Models

Fabrizio Maria Maggi, Claudio Di Ciccio, Chiara Di Francescomarino, Taavi Kala

Publication: Scientific journalJournal articlepeer-review


The aim of process discovery is to build a process model from an event log without prior information about the process. The discovery of declarative process models is useful when a process works in an unpredictable and unstable environment since several allowed paths can be represented as a compact set of rules. One of the tools available in the literature for discovering declarative models from logs is the Declare Miner, a plug-in of the process mining tool ProM. Using this plug-in, the discovered models are represented using Declare, a declarative process modeling language based on ltl for finite traces. However, the high execution times of the Declare Miner when processing large sets of data hampers the applicability of the tool to real-life settings. Therefore, in this paper, we propose a new approach for the discovery of Declare models based on the combination of an Apriori algorithm and a group of algorithms for Sequence Analysis to enhance the time performance of the plug-in. The approach has been developed in a way that it is easy to be parallelized using two different partitioning methods: the search space partitioning, in which different groups of candidate constraints are processed in parallel, and the database partitioning, in which different chunks of the log are processed at the same time. The approach has been implemented in ProM in its sequential version and in two multi-threading implementations leveraging these two partitioning methods. All the new variants of the plug-in have been evaluated using a large set of synthetic and real-life event logs.
Original languageEnglish
Pages (from-to)135 - 152
JournalInformation Systems (IS)
Issue numberPart2
Publication statusPublished - 2018

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 102022 Software development
  • 102
  • 502
  • 502050 Business informatics

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