Towards a Semantic Grid Architecture

The Semantic Grid is an extension of the current Grid in which information and services are given well defined and explicitly represented meaning, better enabling computers and people to work in cooperation. In the last few years, several projects have embraced this vision and there are already successful pioneering applications that combine the strengths of the Grid and of semantic technologies. However, the Semantic Grid currently lacks a reference architecture, or a systematic approach for designing Semantic Grid components or applications. We need a Reference Semantic Grid Architecture that extends the Open Grid Services Architecture by explicitly defining the mechanisms that will allow for the explicit use of semantics and the associated knowledge to support a spectrum of service capabilities. An architecture would have (at least) three major components which are depicted in the extended abstract

Modeling Services for the Semantic Grid

The Grid has emerged as a new distributed computing infrastructure for ad- vanced science and engineering aiming at enabling sharing of resources and infor- mation towards coordinated problem solving in dynamic environments. Research in Grid Computing and Web Services has recently converged in what is known as the Web Service Resource Framework. While Web Service technologies and standards such as SOAP and WSDL provide the syntactical basis for communi- cation in this framework, a service oriented grid architecture for communication has been defined in the Open Grid Service architecture. Wide agreement that a flexible service Grid is not possible without support by Semantic technologies has lead to the term "Semantic Grid" which is at the moment only vaguely defined. In our ongoing work on the Web Service Modeling Ontology (WSMO) we so far concentrated on the semantic description of Web services with respect to applications in Enterprise Application Integration and B2B integration sce- narios. Although the typical application areas of Semantic Web services have slightly different requirements than the typical application scenarios in the Grid a big overlap justifies the assumption that most research results in the Semantic Web Services area can be similarly applied in the Semantic Grid. The present abstract summarizes the authors view on how to fruitfully in- tegrate Semantic Web service technologies around WSMO/WSML and WSMX and Grid technologies in a Semantic Service Grid and gives an outlook on further possible directions and research. The reminder of this abstract is structured as follows. After giving a short overview of the current Grid Service architecture and its particular requirements, we shortly review the basic usage tasks for Semantic Web services. We then point out how these crucial tasks of Semantic Web services are to be addressed by WSMO. In turn, we try to analyze which special requirements for Semantic Web Services arise with respect to the Grid. We conclude by giving an outlook on the limitations of current Semantic Web services technologies and how we plan to address these in the future in a common Framework for Semantic Grid services

Grid-based semantic integration of heterogeneous data resources: Implementation on a HealthGrid

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.The semantic integration of geographically distributed and heterogeneous data resources still remains a key challenge in Grid infrastructures. Today's mainstream Grid technologies hold the promise to meet this challenge in a systematic manner, making data applications more scalable and manageable. The thesis conducts a thorough investigation of the problem, the state of the art, and the related technologies, and proposes an Architecture for Semantic Integration of Data Sources (ASIDS) addressing the semantic heterogeneity issue. It defines a simple mechanism for the interoperability of heterogeneous data sources in order to extract or discover information regardless of their different semantics. The constituent technologies of this architecture include Globus Toolkit (GT4) and OGSA-DAI (Open Grid Service Architecture Data Integration and Access) alongside other web services technologies such as XML (Extensive Markup Language). To show this, the ASIDS architecture was implemented and tested in a realistic setting by building an exemplar application prototype on a HealthGrid (pilot implementation). The study followed an empirical research methodology and was informed by extensive literature surveys and a critical analysis of the relevant technologies and their synergies. The two literature reviews, together with the analysis of the technology background, have provided a good overview of the current Grid and HealthGrid landscape, produced some valuable taxonomies, explored new paths by integrating technologies, and more importantly illuminated the problem and guided the research process towards a promising solution. Yet the primary contribution of this research is an approach that uses contemporary Grid technologies for integrating heterogeneous data resources that have semantically different. data fields (attributes). It has been practically demonstrated (using a prototype HealthGrid) that discovery in semantically integrated distributed data sources can be feasible by using mainstream Grid technologies, which have been shown to have some Significant advantages over non-Grid based approaches

A Semantic Grid Oriented to E-Tourism

With increasing complexity of tourism business models and tasks, there is a clear need of the next generation e-Tourism infrastructure to support flexible automation, integration, computation, storage, and collaboration. Currently several enabling technologies such as semantic Web, Web service, agent and grid computing have been applied in the different e-Tourism applications, however there is no a unified framework to be able to integrate all of them. So this paper presents a promising e-Tourism framework based on emerging semantic grid, in which a number of key design issues are discussed including architecture, ontologies structure, semantic reconciliation, service and resource discovery, role based authorization and intelligent agent. The paper finally provides the implementation of the framework.Comment: 12 PAGES, 7 Figure

Towards a service-oriented e-infrastructure for multidisciplinary environmental research

Research e-infrastructures are considered to have generic and thematic parts. The generic part provids high-speed networks, grid (large-scale distributed computing) and database systems (digital repositories and data transfer systems) applicable to all research commnities irrespective of discipline. Thematic parts are specific deployments of e-infrastructures to support diverse virtual research communities. The needs of a virtual community of multidisciplinary envronmental researchers are yet to be investigated. We envisage and argue for an e-infrastructure that will enable environmental researchers to develop environmental models and software entirely out of existing components through loose coupling of diverse digital resources based on the service-oriented achitecture. We discuss four specific aspects for consideration for a future e-infrastructure: 1) provision of digital resources (data, models & tools) as web services, 2) dealing with stateless and non-transactional nature of web services using workflow management systems, 3) enabling web servce discovery, composition and orchestration through semantic registries, and 4) creating synergy with existing grid infrastructures

Reasoning Services for the Semantic Grid

The Grid aims to support secure, flexible and coordinated resource sharing through providing a middleware platform for advanced distributing computing. Consequently, the Grid’s infrastructural machinery aims to allow collections of any kind of resources—computing, storage, data sets, digital libraries, scientific instruments, people, etc—to easily form Virtual Organisations (VOs) that cross organisational boundaries in order to work together to solve a problem. A Grid depends on understanding the available resources, their capabilities, how to assemble them and how to best exploit them. Thus Grid middleware and the Grid applications they support thrive on the metadata that describes resources in all their forms, the VOs, the policies that drive then and so on, together with the knowledge to apply that metadata intelligently