Sharing of Semantically Enhanced Information for the Adaptive Execution of Business Processes
This work addresses the need for more flexible and data-driven business process adaptation, though it appears incremental as it builds on existing semantic and tuple space concepts.
The paper tackles the problem of enabling adaptable business processes by proposing the Semantic Context Space (SCS) Engine, which stores semantically annotated data for information exchange, and its applicability is validated through experimental evaluation of operations like write, read, and take.
Motivated from the Context Aware Computing, and more particularly from the Data-Driven Process Adaptation approach, we propose the Semantic Context Space (SCS) Engine which aims to facilitate the provision of adaptable business processes. The SCS Engine provides a space which stores semantically annotated data and it is open to other processes, systems, and external sources for information exchange. The specified implementation is inspired from the Semantic TupleSpace and uses the JavaSpace Service of the Jini Framework (changed to Apache River lately) as an underlying basis. The SCS Engine supplies an interface where a client can execute the following operations: (i) write: which inserts in the space available information along with its respective meta-information, (ii) read: which retrieves from the space information which meets specific meta-information constrains, and (iii) take: which retrieves and simultaneously deletes from the space information which meets specific meta-information constrains. In terms of this thesis the available types of meta-information are based on ontologies described in RDFS or WSML. The applicability of the SCS Engine implementation in the context of data-driven process adaptation has been ensured by an experimental evaluation of the provided operations. Ultimately, we refer to open issues which could be addressed to enrich the proposed Engine with additional features.