ABSTRACT * The advent of the internet has revolutionized the field of advertising by providing a whole new path for reaching potential customers. Studies show that online advertising is, on the whole, extremely effective and that consumer acceptance of online advertising is comparable to traditional media[7][8]. One of the reasons for the high effectiveness of online advertising is that users interact with the web at a far more personal and intimate level than they interact with other advertising media like the radio or television. Pervasive computing environments deal with users at an even more intimate level; hence such environments are even better advertising platforms than the web. Pervasive environments allow the delivery of relevant advertising in suitable ways to selected consumers. In this paper, we examine some of the possibilities of pervasive advertising as well as some of the issues involved.

Abstract—Grid computing offers significant enhancements to our capabilities for computation, information processing and collaboration, and has exciting ambitions in many fields of endeavour. In this paper we argue that the full richness of the Grid vision, with its application in e-Science, e-Research or e-Business, requires the ‘Semantic Grid’. The Semantic Grid is an extension of the current Grid in which information and services are given well-defined meaning, better enabling computers and people to work in cooperation. To this end, we outline the requirements of the Semantic Grid, discuss the state of the art in achieving them, and identify the key research challenges in realising this vision.

Web services and Grid computing are technologies with a great impact these days. Both technologies are taking a syntactical based approach to describe services, and therefore, limited support is provided for service related tasks like: discovery, composition, etc. Semantic Web services try to overcome the limitations of syntactical based approaches by adding semantics to services descriptions. One of the most salient initiatives in Semantic Web services area is the Web Service Modeling Ontology(WSMO). WSMO provides a conceptual model and languages to express semantic descriptions of services. We believe that WSMO, the conceptual model, and WSML, the associated family of languages, with some extensions and/or restrictions could be the solution for semantically describe services in Grid environment. In order support this statement, we provide a modeling example of a Grid service using WSMO/WSML. 1

There are many simulation environments around that can be used to simulate components of Grid Computing. There are also a few simulation environments that allow for data intensive jobs to be simulated. GridSim, with the Data.Grid extension, combines both of these simulation environments to allow simulation of data intensive jobs using Grid resources. GridSim is explored in this paper with an aim to simulate the Distributed Ontology Framework (DOF) in the Semantic Grid Environment. The DOF requires many data resources to be located as well as large scale processing to take place on the collected data. Several simulation environments are discussed and the similarities between the DOF and the GridSim environment are explored. Several simulations are carried out to show whether or not GridSIM is a suitable simulation environment for simulating the Distributed Ontology Framework. 1.

While advances in database and parallel processing technology over the past decade have improved the celerity with which bioinformatics computations can be performed, many unnatural barriers persist between bioinformaticians and their data, which is often scattered over dozens of machines in incompatible data stores in a myriad of formats. Users of biological data should be allowed to concentrate on their research and not the job of interfacing disparate systems and data sets. In this paper we present an approach to unifying diverse bioinformatics data sources, such as GenBank, PDB, and literature databases, using Semantic Web techniques. We describe a unified naming convention called LSID, which permits uniform, Internet-based retrieval of biological data, and schemas based on RDF that enable us to integrate protein and genetic sequence data, literature references, Web Service definitions, and annotations into a common framework. Finally, we demonstrate a user interface that takes advantage of the increased uniformity and expressive power of this data model and allows users to collect pieces of data together according to purpose or research objective rather than data format. 1

Scientific applications executed with modern distributed technologies tend to be computed on various, dispersed resources, and workflows become a natural method of describing them. This method of the functional application decomposition allows for fully manual design only to a certain level of complexity. However, the scientific simulations usually expose non-trivial processing logic including multiple distinct processing elements connected with complicated control patterns. Thus an environment for semi-automated and assisted composition and orchestration of such application workflows is desired. This report describes the concept and design of such a platform that analyzes user requirements regarding application results and leads the user in the process of possible solution construction, dynamic refinement and execution. The tools forming the presented system use domain-specific knowledge and employ several levels of workflow abstractness in order to deliver the functionality in a more natural way for the human user. This work presents a real-life case study from the city traffic simulation domain, in order to introduce subsequent steps of workflow orchestration. It also supplies a discussion on existing similar systems for workflow orchestration

Abstract not found

In this paper we presented a novel approach to semantic resource discovery in the Grid, Where different organizations may use different ontologies to describe the resource and application requirements. We do not require a central ontology for resource description and matching. The relation matrix is defined to describe relations between concepts in any two ontologies. Then, a method of rewriting resource queries based on the relation matrix is presented to solve the ontology heterogeneity problem. It rewrites the resource queries in one ontology to approximate queries in another ontology based on the relations between concepts. Machines can process this procedure automatically. 1.

Abstract: Triple Space Computing is a new communication and coordination paradigm for Semantic Web Services. It has been achieved by extending Tuple Space Computing to support RDF as Triple Space Computing and then use it for communication in Semantic Web Services. Similarly we want to use the emerging Triple Space Computing to enhance the communication of computation and information services of the Semantic Grid. This paper briefly describes how Triple Space Computing can be used for Semantic Web

Abstract. The Grid has emerged as a new distributed computing infrastructure for advanced science and engineering. It aims at enabling resource sharing as means to facilitate a problem-solving approach for dynamic environments that is based on the integration of available resources. Wide agreement that a flexible Grid is not possible without the support of semantic technologies, has lead to the term “Semantic Grid“. Transparent service composition has a great potential to facilitate the integration of Web Services deployed in the Grid. However, discovering, dynamically composing and optimizing large-scale Web Services (e.g., in the rank of 1,000 to 100,000 services) is a challenging problem. We approached the problem of Web Service compositions in the Grid as an optimization problem, in which for a given request a good plan has to be generated. In this work, we propose the definition of several optimization strategies to identify good orderings of Web Service compositions. 1 Research Problem