Specifying, Monitoring, and Executing Workflows in Linked Data Environments
This work addresses the integration of workflow modeling with Linked Data for non-expert users in domains like IoT and enterprise systems, but it is incremental as it builds on existing workflow languages and Linked Data interfaces.
The authors tackled the problem of combining workflow languages with Linked Data interfaces to enable high-level specification of application behavior by non-expert users in decentralized environments, resulting in an ontology that covers basic workflow patterns and a prototype system that scales linearly with the number of IoT devices in a benchmark.
We present an ontology for representing workflows over components with Read-Write Linked Data interfaces and give an operational semantics to the ontology via a rule language. Workflow languages have been successfully applied for modelling behaviour in enterprise information systems, in which the data is often managed in a relational database. Linked Data interfaces have been widely deployed on the web to support data integration in very diverse domains, increasingly also in scenarios involving the Internet of Things, in which application behaviour is often specified using imperative programming languages. With our work we aim to combine workflow languages, which allow for the high-level specification of application behaviour by non-expert users, with Linked Data, which allows for decentralised data publication and integrated data access. We show that our ontology is expressive enough to cover the basic workflow patterns and demonstrate the applicability of our approach with a prototype system that observes pilots carrying out tasks in a mixed-reality aircraft cockpit. On a synthetic benchmark from the building automation domain, the runtime scales linearly with the size of the number of Internet of Things devices.