Towards Online Discovery of Data-Aware Declarative Process Models from Event Streams

Nicolò Navarin, Matteo Cambiaso, Andrea Burattin, Fabrizio M. Maggi, Luca Oneto, Alessandro Sperduti

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In recent years, several techniques have been made available to automatically discover declarative process models from event logs. These techniques are useful to provide a comprehensible picture of the process as opposed to full specifications of process behavior provided by procedural modeling languages. Since many modern systems produce “big data” from business process executions, in previous work, a framework for the discovery of LTL-based declarative process models from streaming event data has been proposed. This framework can be used to process events online, as they occur, as a way to deal with large and complex collections of datasets that are impossible to store and process altogether. However, the proposed framework does not take into account data attributes associated with events in the log, which can otherwise provide valuable insights into the rules that govern the process. This paper makes the first proposal to close this gap by presenting a technique for discovering declarative process models from event streams that incorporates both control-flow dependencies and data conditions. Specifically, we use Hoeffding trees to incrementally discover data-aware declarative process models, which are representedas conjunctions of first-order temporal logic expressions. The proposed technique has been validated on a synthetic event log, and on a real-life log of a cancer treatment process.
Original languageEnglish
Title of host publicationProceedings of the 2020 International Joint Conference on Neural Networks
Number of pages8
Publication date2020
ISBN (Print)978-1-7281-6927-9
Publication statusPublished - 2020
Event2020 International Joint Conference on Neural Networks - Virtual event, Glasgow , United Kingdom
Duration: 19 Jul 202024 Jul 2020


Conference2020 International Joint Conference on Neural Networks
LocationVirtual event
Country/TerritoryUnited Kingdom


  • Online process discovery
  • Data-aware process model
  • Declarative process models


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