Modeling Native Intelligence Semantics for Indigenous Selection of Web Services Using Slaky Composer

Abstract

Automatic web service composition is defined as the const ruction of virtual enterprises as complex services on demand. In the current research scenario choosing business service partners for composition on the fly is usually determined with user-centric metrics and deficient of provider’s business specific metrics. Automatic web service composition in today’s real world business circumstances is more of a toy model due to the lack of service provider collaboration metrics [1]. SLAKY System is a new model for selection of business service partners by adding servic e provider metrics to user centric metrics enabling strategic and realistic selection of services for composition. SLAKY System selects services automatically considering the vision, time planning, environmental context, user adoption, usage policies, trus t management, risk management, market scenario, native intelligence, and competitive profit management as service provider collaboration metrics in addition to functionality satisfaction metrics for client’s requirements. The time planning metric was designed using opus deviser algorithm [2] and profit management using SLAKY BWG algorithm [3]. In this paper we focus on strategic selection of semantic services based on the metric of native intelligence. Howard Gardner, psychology professor, Harvard Universit y, defines intelligence as an ability to solve problems, or to create products, that are valued within one or more cultural settings. Richard Heck, Professor of Natural Theology, Brown University, in his work states that native intelligence is primarily influenced by racial and cultural differences. User preferences in any sector intend to have native influence. For example the affinity towards entertainment services for Indian Premier League or low cost car booking service for TATA Nano is more in India th an America. Moreover decision made by a decision maker is motivated by native intelligence in addition to Intelligence Quotient and is valued highly in native grounds. For example the selection of a matrimonial service totally relies on native aspects. Native Intelligence is also a significant metric to improve time -to-market and quality in the launch of any product. Considering the significance of native intelligence in strategic selection we layer native intelligence descriptions for realistic selection o f semantic web services on the fly. OWL -S upper ontology that describes semantics of services for automation of service discovery, selection, composition, orchestration and invocation lacks entities for semantic modeling of native intelligence. In this pap er we propose an extended OWL -S ontology which augments where, why and who native status descriptions of a service apart from the existing what (ServiceProfile sub -ontology) and how (ServiceModel, ServiceGrounding sub-ontology) semantics. The why, whereand who semantics are newly augmented as ServiceParameters of Profile sub -ontology using sParameter property. We also propose Naavi algorithm that reasons with common sense knowledge for selection of the web service that is highly valuable in a native context using native description in extended OWL -S.