Discovery of Multi-perspective Declarative Process Models

Stefan Schönig, Claudio Di Ciccio, Fabrizio Maria Maggi, Jan Mendling

Publikation: Beitrag in Buch/KonferenzbandBeitrag in Konferenzband


Process discovery is one of the main branches of process mining that allows the user to build a process model representing the process behavior as recorded in the logs. Standard process discovery techniques produce as output a procedural process model (e.g., a Petri net). Recently, several approaches have been developed to derive declarative process models from logs and have been proven to be more suitable to analyze processes working in environments that are less stable and predictable. However, a large part of these techniques are focused on the analysis of the control flow perspective of a business process. Therefore, one of the challenges still open in this field is the development of techniques for the analysis of business processes also from other perspectives, like data, time, and resources. In this paper, we present a full-fledged approach for the discovery of multi-perspective declarative process models from event logs that allows the user to discover declarative models taking into consideration all the information an event log can provide. The approach has been implemented and experimented in real-life case studies.
Titel des SammelwerksService-Oriented Computing - 14th International Conference, ICSOC 2016, Banff, AB, Canada, October 10-13, 2016, Proceedings
Herausgeber*innen Quan Z. Sheng, Eleni Stroulia, Samir Tata, Sami Bhiri
ErscheinungsortBanff, Canada
VerlagSpringer Lecture Notes in Computer Science (LNCS)
Seiten87 - 103
ISBN (Print)978-3-319-46294-3
PublikationsstatusVeröffentlicht - 2016

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