Efficient Retrieval of Web Services Using Prioritization and Clustering

WEB services are software entities that have a well defined interface and perform a specific task. Typical examples include services returning information to the user, such as news or weather forecast services. A web service is formally described in a standardized language (WSDL). The service description may include the parameters associated with web services like input , output and quality of service. As web services and service providers proliferate, there will be a large number of candidate, and likely competing, services for fulfilling a desired task. Hence, effective service discovery mechanisms are required for identifying and retrieving the most appropriate services. The main contributions of our paper are summarized as follows; we propose and implement a method for determining dominance relationships among service advertisements that simultaneously takes into consideration multiple PDM criteria. We introduce a method for prioritization and clustering web services based on similarity measures using efficient algorithms Keywords : Web Service , PDM , dominance score ,TKDD, clustering

Multidimensional Information-Based Web Service Representation Method

With the development of service-oriented architecture (SOA) technology, the amount of Web service is increasing. Clustering or classifying Web services correctly are an effective way to improve the quality of Web service discovery and the efficiency of Web service composition. However, the existing Web service modeling methods (such as latent Dirichlet allocation topic model) are difficult to obtain accurate and effective Web service representation from a sparse Web service dataset for Web service clustering. To solve this problem, this paper proposes a multi-dimensional information-based Web service representation method (MISR). First, it generates word vectors which contain topic and semantic information implicit in Web service description with Gaussian mixture model and Word2Vec. Then, the MISR algorithm combines tag-word relationship, popularity, and co-occurrence information together for generating multi-dimensional information Web service representation. Web service clustering and Web service classification are used for evaluating the effectiveness of MISR. Based on a real-world dataset of API services, the experiment results show that compared with LDA, Word2Vec, Doc2Vec, WT-LDA, HDP-SOM, GWSC, the proposed method has 38.8%, 54.5%, 15.3%, 33.3%, 44.7%, 9.7% improvement in Micro-F1 value

Linked education: interlinking educational resources and the web of data

Research on interoperability of technology-enhanced learning (TEL) repositories throughout the last decade has led to a fragmented landscape of competing approaches, such as metadata schemas and interface mechanisms. However, so far Web-scale integration of resources is not facilitated, mainly due to the lack of take-up of shared principles, datasets and schemas. On the other hand, the Linked Data approach has emerged as the de-facto standard for sharing data on the Web and offers a large potential to solve interoperability issues in the field of TEL. In this paper, we describe a general approach to exploit the wealth of already existing TEL data on the Web by allowing its exposure as Linked Data and by taking into account automated enrichment and interlinking techniques to provide rich and well-interlinked data for the educational domain. This approach has been implemented in the context of the mEducator project where data from a number of open TEL data repositories has been integrated, exposed and enriched by following Linked Data principles

Datamining for Web-Enabled Electronic Business Applications

Web-Enabled Electronic Business is generating massive amount of data on customer purchases, browsing patterns, usage times and preferences at an increasing rate. Data mining techniques can be applied to all the data being collected for obtaining useful information. This chapter attempts to present issues associated with data mining for web-enabled electronic-business

Viewpoint Discovery and Understanding in Social Networks

The Web has evolved to a dominant platform where everyone has the opportunity to express their opinions, to interact with other users, and to debate on emerging events happening around the world. On the one hand, this has enabled the presence of different viewpoints and opinions about a - usually controversial - topic (like Brexit), but at the same time, it has led to phenomena like media bias, echo chambers and filter bubbles, where users are exposed to only one point of view on the same topic. Therefore, there is the need for methods that are able to detect and explain the different viewpoints. In this paper, we propose a graph partitioning method that exploits social interactions to enable the discovery of different communities (representing different viewpoints) discussing about a controversial topic in a social network like Twitter. To explain the discovered viewpoints, we describe a method, called Iterative Rank Difference (IRD), which allows detecting descriptive terms that characterize the different viewpoints as well as understanding how a specific term is related to a viewpoint (by detecting other related descriptive terms). The results of an experimental evaluation showed that our approach outperforms state-of-the-art methods on viewpoint discovery, while a qualitative analysis of the proposed IRD method on three different controversial topics showed that IRD provides comprehensive and deep representations of the different viewpoints

Search and Discovery Tools for Astronomical On-line Resources and Services

A growing number of astronomical resources and data or information services are made available through the Internet. However valuable information is frequently hidden in a deluge of non-pertinent or non up-to-date documents. At a first level, compilations of astronomical resources provide help for selecting relevant sites. Combining yellow-page services and meta-databases of active pointers may be an efficient solution to the data retrieval problem. Responses generated by submission of queries to a set of heterogeneous resources are difficult to merge or cross-match, because different data providers generally use different data formats: new endeavors are under way to tackle this problem. We review the technical challenges involved in trying to provide general search and discovery tools, and to integrate them through upper level interfaces.Comment: 7 pages, 2 Postscript figures; to be published in A&A

Small-world networks, distributed hash tables and the e-resource discovery problem

Resource discovery is one of the most important underpinning problems behind producing a scalable, robust and efficient global infrastructure for e-Science. A number of approaches to the resource discovery and management problem have been made in various computational grid environments and prototypes over the last decade. Computational resources and services in modern grid and cloud environments can be modelled as an overlay network superposed on the physical network structure of the Internet and World Wide Web. We discuss some of the main approaches to resource discovery in the context of the general properties of such an overlay network. We present some performance data and predicted properties based on algorithmic approaches such as distributed hash table resource discovery and management. We describe a prototype system and use its model to explore some of the known key graph aspects of the global resource overlay network - including small-world and scale-free properties