05271 Abstracts Collection -- Semantic Grid: The Convergence of Technologies

From 03.07.05 to 08.07.05, the Dagstuhl Seminar 05271 ``Semantic Grid -- The Convergence of Technologies\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

S-OGSA as a Reference Architecture for OntoGrid and 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

Semantic Grid Roadmap

Conclusion: While the first level of information systems was built to assist humans in real world processes, the increasing complexity of the ITC infrastructures calls for a second level of information systems that will assist in making a better use of ITC. All business process roadmaps are foreseeing a strong role of ICT in the future. Semantic grid has a potential to be a highway in these roadmaps or a dead end into which substantial effort will be placed, but that will not address the needs of the users. We will not know, unless we try it out, looking carefully at the technology pull in some areas and applying the technology push if a breakthrough seems possible

A high-level semiotic trust agent scoring model for collaborative virtual organisations

In this paper, we describe how a semiotic ladder, together with a supportive trust agent, can be used to address “soft” trust issues in the context of collaborative Virtual Organisations (VO). The intention is to offer all parties better support for trust (as reputation) management including the reduction of risk and improved reliability of VO e-services. The semiotic ladder is intended to support the VO e-service lifecycle through the articulation of e-trust at various levels of system abstraction, including trust as measurable confidence. At the social level, reputation and reliability measures of e-trust are the relevant dimensions as regards choice of VO partner and are also relevant to the negotiation of service level agreements between the VO partners. By contrast, at the lower levels of the trust ladder, e-trust measures typically address the degree to which secure sign on and message level security conforms to various tangible technological security protocols. The novel trust agent provides the e-service consumer with an objective measure of the trustworthiness of the e-service at run-time, just prior to its actual consumption. Specifically, VO e-service consumer confidence level is informed, by leveraging third party objective evidence. This evidence comprises a set of Corporate Governance (CG) scores. These scores are used as a trust proxy for the "real" owner of the VO. There are also inherent limitations associated with the use of CG scores. These are duly acknowledged

The state-of-the-art in personalized recommender systems for social networking

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0

Ontological Framework for Semantic Web

The web in today’s era is an remarkable triumph due to the proliferating number of users and sources of information. However, the increasing complexity of the web is not reflected in the present-day state of Web technology. The load of gaining access to, extracting relevant information, interpretation and upholding is left to the human (user end).The Semantic Web should structure the content of web pages so that information is also structured in well-defined manner. Ontology structuring techniques is good choice to structure and model problem. It provides proper conceptualization of a domain that is communal to group of people. Ontology is basically representation of domain based theory or information in view of their semantics. Thus ontology is basically a structure or framework which underlies and enables machine understanding. The paper discusses ontology in context of semantic web

Research Proposal on Distinct Study and Significant of Search Techniques in Web Mining

The goal of this research is to provide a more current evaluation and update of web mining research and how machine learning techniques can be applied to web mining techniques available. Currenttrends in each of the three different types of web mining are reviewed in the categories of web content mining, web usage mining, and web structure mining.Unlike previous investigators, we divide web mining processes into the following five subtasks such as resource finding and retrieving, information selection and preprocessing, patterns analysis and recognition, validation and interpretation, and visualization.Major limitations of web mining research are lack of suitable test collections that can be reused by researchers and difficulty to collect web usage data across different web sites. Most web mining applications have been reviewed in this research. Although the activities are still in their early stages and should continue to develop as the Web evolves. This research shows that frequent pattern growth algorithm produces more efficient and accurate results to compare with K-Apriori algorithm. The proposed methods were successfully tested and results were observed and compared with existing methods on the web log files using machine learning techniques

Propiedades de Calidad de Servicio en el Descubrimiento de Recursos Grid

Uno de los problemas abiertos en el contexto de las Arquitecturas Orientadas a Servicios es del descubrimiento de recursos y/o servicios adecuados para llevar a cabo una tarea determinada. Los proveedores de información Grid básicamente ofrecen información funcional sobre los recursos Grid que monitorizan, por lo que los modelos de información Grid básicamente representan esta información sintáctica, y los consumidores de información Grid usan normalmente dichas propiedades funcionales para seleccionar recursos. En la práctica, muchos trabajos se reinician debido a fallos en los recursos, aunque existen iniciativas que tratan de usar técnicas aisladas para manejar algunas propiedades de calidad de servicio. En el presente artículo se propone un nuevo enfoque para modelar recursos Grid junto con propiedades de calidad de servicio. Por un lado, este modelo está basado en una ontología desarrollada para integrar los modelos existentes tanto a nivel de representación de información Grid como de calidad de servicio en general. Por otro lado, también propone la creación de un sistema de medida - actualmente en desarrollo - para algunas propiedades de calidad de servicio (disponibilidad, rendimiento y fiabilidad)

An overview of S-OGSA: A Reference Semantic Grid Architecture

The Grid's vision, of sharing diverse resources in a flexible, coordinated and secure manner through dynamic formation and disbanding of virtual communities, strongly depends on metadata. Currently, Grid metadata is generated and used in an ad hoc fashion, much of it buried in the Grid middleware's code libraries and database schemas. This ad hoc expression and use of metadata causes chronic dependency on human intervention during the operation of Grid machinery, leading to systems which are brittle when faced with frequent syntactic changes in resource coordination and sharing protocols. The Semantic Grid is an extension of the Grid in which rich resource metadata is exposed and handled explicitly, and shared and managed via Grid protocols. The layering of an explicit semantic infrastructure over the Grid Infrastructure potentially leads to increased interoperability and greater flexibility. In recent years, several projects have embraced the Semantic Grid vision. However, the Semantic Grid lacks a Reference Architecture or any kind of systematic framework for designing Semantic Grid components or applications. The Open Grid Service Architecture ( OGSA) aims to define a core set of capabilities and behaviours for Grid systems. We propose a Reference Architecture that extends OGSA to support the explicit handling of semantics, and defines the associated knowledge services to support a spectrum of service capabilities. Guided by a set of design principles, Semantic-OGSA ( S-OGSA) defines a model, the capabilities and the mechanisms for the Semantic Grid. We conclude by highlighting the commonalities and differences that the proposed architecture has with respect to other Grid frameworks. (c) 2006 Elsevier B. V. All rights reserved