Grid service discovery with rough sets

Copyright [2008] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.The computational grid is evolving as a service-oriented computing infrastructure that facilitates resource sharing and large-scale problem solving over the Internet. Service discovery becomes an issue of vital importance in utilising grid facilities. This paper presents ROSSE, a Rough sets based search engine for grid service discovery. Building on Rough sets theory, ROSSE is novel in its capability to deal with uncertainty of properties when matching services. In this way, ROSSE can discover the services that are most relevant to a service query from a functional point of view. Since functionally matched services may have distinct non-functional properties related to Quality of Service (QoS), ROSSE introduces a QoS model to further filter matched services with their QoS values to maximise user satisfaction in service discovery. ROSSE is evaluated in terms of its accuracy and efficiency in discovery of computing services

Improving the performance of web service recommenders using semantic similarity

This paper addresses issues related to recommending Semantic Web Services (SWS) using collaborative filtering (CF). The focus is on reducing the problems arising from data sparsity, one of the main difficulties for CF algorithms. Two CF algorithms are presented and discussed: a memory-based algorithm, using the k-NN method, and a model-based algorithm, using the k-means method. In both algorithms, similarity between users is computed using the Pearson Correlation Coefficient (PCC). One of the limitations of using the PCC in this context is that in those instances where users have not rated items in common it is not possible to compute their similarity. In addition, when the number of common items that were rated is low, the reliability of the computed similarity degree may also be low. To overcome these limitations, the presented algorithms compute the similarity between two users taking into account services that both users accessed and also semantically similar services. Likewise, to predict the rating for a not yet accessed target service, the algorithms consider the ratings that neighbor users assigned to the target service, as is normally the case, while also considering the ratings assigned to services that are semantically similar to the target service. The experiments described in the paper show that this approach has a significantly positive impact on prediction accuracy, particularly when the user-item matrix is sparse.Facultad de Informátic

Improving the performance of web service recommenders using semantic similarity

This paper addresses issues related to recommending Semantic Web Services (SWS) using collaborative filtering (CF). The focus is on reducing the problems arising from data sparsity, one of the main difficulties for CF algorithms. Two CF algorithms are presented and discussed: a memory-based algorithm, using the k-NN method, and a model-based algorithm, using the k-means method. In both algorithms, similarity between users is computed using the Pearson Correlation Coefficient (PCC). One of the limitations of using the PCC in this context is that in those instances where users have not rated items in common it is not possible to compute their similarity. In addition, when the number of common items that were rated is low, the reliability of the computed similarity degree may also be low. To overcome these limitations, the presented algorithms compute the similarity between two users taking into account services that both users accessed and also semantically similar services. Likewise, to predict the rating for a not yet accessed target service, the algorithms consider the ratings that neighbor users assigned to the target service, as is normally the case, while also considering the ratings assigned to services that are semantically similar to the target service. The experiments described in the paper show that this approach has a significantly positive impact on prediction accuracy, particularly when the user-item matrix is sparse.Facultad de Informátic

Improving the performance of web service recommenders using semantic similarity

This paper addresses issues related to recommending Semantic Web Services (SWS) using collaborative filtering (CF). The focus is on reducing the problems arising from data sparsity, one of the main difficulties for CF algorithms. Two CF algorithms are presented and discussed: a memory-based algorithm, using the k-NN method, and a model-based algorithm, using the k-means method. In both algorithms, similarity between users is computed using the Pearson Correlation Coefficient (PCC). One of the limitations of using the PCC in this context is that in those instances where users have not rated items in common it is not possible to compute their similarity. In addition, when the number of common items that were rated is low, the reliability of the computed similarity degree may also be low. To overcome these limitations, the presented algorithms compute the similarity between two users taking into account services that both users accessed and also semantically similar services. Likewise, to predict the rating for a not yet accessed target service, the algorithms consider the ratings that neighbor users assigned to the target service, as is normally the case, while also considering the ratings assigned to services that are semantically similar to the target service. The experiments described in the paper show that this approach has a significantly positive impact on prediction accuracy, particularly when the user-item matrix is sparse.Facultad de Informátic

Semantic description and matching of services for pervasive environments

With the evolution of the World Wide Web and the advancement of the electronic world, the diversity of available services is increasing rapidly.This raises new demands for the efficient discovery and location of heterogeneous services and resources in dynamically changing environments. The traditional approaches for service discovery such as UDDI, Salutation, SLP etc. characterise the services by using predefined service categories and fixed attribute value pairs and the matching techniques in these approaches are limited to syntactic comparisons based on attributes or interfaces. More recently with the popularity of Semantic Web technologies, there has been an increased interest in the application of reasoning mechanisms to support discovery and matching. These approaches provide important directions in overcoming the limitations present in the traditional approaches to service discovery. However, these still have limitations and have overlooked issues that need to be addressed; particularly these approaches do not have an effective ranking criterion to facilitate the ordering of the potential matches, according to their suitability to satisfy the request under concern. This thesis presents a semantic matching framework to facilitate effective discovery of device based services in pervasive environments. This offers a ranking mechanism that will order the available services in the order of their suitability and also considers priorities placed on individual requirements in a request during the matching process. The proposed approach has been implemented in a pervasive scenario for matching device-based services. The Device Ontology which has been developed as part of this research, has been used to describe the devices and their services. The retrieval effectiveness of this semantic matching approach has been formally investigated through the use of human participant studies and the experimental results have indicated that the results correlate well with human perception. The performance of the solution has also been evaluated, to explore the effects of employing reasoning mechanisms on the efficiency of the matching process. Specifically the scalability of the solution has been investigated with respect to the request size and the number of advertisements involved in matching.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

An evaluation methodology and framework for semantic web services technology

Software engineering has been driven over decades by the trend towards component based development and loose coupling. Service oriented architectures and Web Services in particular are the latest product of this long-reaching development. Semantic Web Services (SWS) apply the paradigms of the Semantic Web to Web Services to allow more flexible and dynamic service usages. Numerous frameworks to realize SWS have been put forward in recent years but their relative advantages and general maturity are not easy to assess. This dissertation presents a solution to this issue. It defines a general methodology and framework for SWS technology evaluation as well as concrete benchmarks to assess the functional scope and performance of various approaches. The presented benchmarks have been executed within international evaluation campaign. The thesis thus comprehensively covers theoretical, methodological as well as practical results regarding the evaluation and assessment of SWS technologies