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A CACHING TECHNIQUE FOR OPTIMIZING AUTOMATED SERVICE DISCOVERY

    The development of sophisticated technologies for service-oriented architectures (SOA) is a grand challenge. A promising approach is the employment of semantic technologies to better support the service usage cycle. Most existing solutions show significant deficits in the computational performance, which hampers the applicability in large-scale SOA systems. We present an optimization technique for automated service discovery — one of the central operations in semantically enabled SOA environments — that can ensure a sophisticated performance while maintaining a high retrieval accuracy. The approach is based on goals that formally describe client objectives, and it employs a caching mechanism for enhancing the computational performance of a two-phased discovery framework. At design time, the suitable services for generic and reusable goal descriptions are determined by semantic matchmaking. The result is captured in a continuously updated graph structure that organizes goals and services with respect to the requested and provided functionalities. This is exploited at runtime in order to detect the suitable services for concrete client requests with minimal effort. We formalize the approach within a first-order logic framework, and define the graph structure along with the associated storage and retrieval algorithms. An empirical evaluation shows that significant performance improvements can be achieved.

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