A static analysis technique to detect unsatisfiable conditions in ontology-based workflows
Static analysis techniques for consistency checking of workflows allow to avoid runtime errors. This is in particular crucial for long running workflows where errors, detected late, can cause high costs. In many classes of workflows, the data perspective is rather simple, and the control flow perspective is the focus of consistency checking. In our setting, however, workflows are used to collect and integrate complex data based on a given domain ontology. In such scenarios, the data perspective becomes central and data consistency checking crucial. In this paper, we focus on detecting unsatisfiable conditions, a data inconsistency which can lead to non-reachable tasks in workflows. We describe an algorithm to detect such inconsistencies in workflows with an ontology-based data perspective. The algorithm utilizes semantic web reasoning. We discuss soundness and completeness of the technique.
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