A framework for discovering and classifying ubiquitous services in digital health ecosystems
Journal of Computer and System Sciences
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Digital Ecosystem is defined as “an open, loosely coupled, domain clustered, demand-driven, self-organizing and agent-based environment, in which each species is proactive and responsive for its own benefit and profit” [1]. Species in the Digital Ecosystem can play dual roles, which are service requester (client) service provider (server). A service provider enters the Digital Ecosystem by publishing a service metadata in the service factory, in which the service metadata can be clustered by domain-specific ontologies provided by the Digital Ecosystem. Two issues emerge here. First of all, vast and heterogeneous service metadata are ubiquitous before the Digital Ecosystem technology emerges. It is a challenge for the Digital Ecosystem to organize these metadata. In order to solve this issue, an automatic service metadata clustering approach could be desired. However, this could educe the second issue – the automatic association between service concepts and service metadata could not agree with service providers’ perceptions, as a result of the differences among individual understandings. To solve the two issues, in this paper, we present a hybrid ontology-based metadata clustering methodology comprising an extended case-based reasoning algorithm-based automatic concept-metadata association approach and a service provider-oriented concept-metadata association approach.