Semantic modeling and analysis of complex data-aware processes and their executions

Abstract

Enriching business processes with the semantic knowledge of an ontology is recognized as a fundamental need in business process management in order to obtain better models and perform more expressive execution analyses. In our work, we model semantically annotated business processes in an OWL knowledge base formalizing the structure of business processes together with the associated business domain. We extend previous work in three substantial manners: first, we show how to exploit the OWL knowledge base to represent the complex data artifacts manipulated by a business process (together with the process itself); second, we show how execution traces can be added into the OWL knowledge base to perform a process execution analysis involving knowledge about both the structure and the domain of a business process; third, we show how to support the semantic modeling and analysis of complex data-aware business processes and their executions. In detail, we illustrate how we extended the MoKi tool for enabling the collaborative modeling of semantically annotated complex data-aware processes, and for supporting their execution analysis via semantic-based reasoning techniques capable of scaling to non-trivial amounts of data while maintaining their advantages. The feasibility and usefulness of the proposed conceptual framework and tool support is shown through a real use case and an experimental evaluation.