Abstract. In this paper we describe IRS-II (Internet Reasoning Service) a framework and implemented infrastructure, whose main goal is to support the publication, location, composition and execution of heterogeneous web services, augmented with semantic descriptions of their functionalities. IRS-II has three main classes of features which distinguish it from other work on semantic web services. Firstly, it supports one-click publishing of standalone software: IRS-II automatically creates the appropriate wrappers, given pointers to the standalone code. Secondly, it explicitly distinguishes between tasks (what to do) and methods (how to achieve tasks) and as a result supports capabilitydriven service invocation; flexible mappings between services and problem specifications; and dynamic, knowledge-based service selection. Finally, IRS-II services are web service compatible -standard web services can be trivially published through the IRS-II and any IRS-II service automatically appears as a standard web service to other web service infrastructures. In the paper we illustrate the main functionalities of IRS-II through a scenario involving a distributed application in the healthcare domain.
Adaptive service selection is acknowledged to provide a certain number of advantages to optimize the service provisioning process or to cater for advanced service brokering. Semantic Web Services, that is services that have been enriched with semantic annotations have often been used for providing adaptive service selection by deferring the binding of services until runtime. Thus far, however, research on Semantic Web Services has mainly been dominated by rich conceptual frameworks such as WSMO and OWL-S which require a significant effort towards the annotation of services and rely on complex reasoning for which there are no efficient solutions that can scale to the Web yet. In this chapter, inline with current trends on the Semantic Web that sacrifice expressivity in favour of performance, we present a novel approach to providing adaptive service selection that relies on simple conceptual models for services and less expressive formalisms for which there currently exist mature and performant implementations. In particular, we present a set of concep-
Abstract. The FuturICT project seeks to use the power of big data, analytic models grounded in complexity science, and the collective intelligence they yield for societal benefit. Accordingly, this paper argues that these new tools should not remain the preserve of restricted
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