Banage T. G. S. Kumara

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Clustering Web services into functionally similar clusters is a very efficient approach to service discovery. A principal issue for clustering is computing the semantic similarity between services. Current approaches use similarity-distance measurement methods such as keyword, information-retrieval or ontology based methods. These approaches have problems(More)
Organizing Web services into functionally similar clusters, is an efficient approach to discovering Web services efficiently. An important aspect of the clustering process is calculating the semantic similarity of Web services. Most current clustering approaches are based on similarity-distance measurement, including keyword, ontology and(More)
Big Data analytics provide support for decision making by discovering patterns and other useful information from large set of data. Organizations utilizing advanced analytics techniques to gain real value from Big Data will grow faster than their competitors and seize new opportunities. Cross-Industry Standard Process for Data Mining (CRISP-DM) is an(More)
Web service clustering is one of a very efficient approach to discover Web services efficiently. Current clustering approaches use traditional clustering algorithms such as agglomerative as the clustering algorithm. The algorithms have not provided visualization of service clusters that gives inspiration for a specific domain from visual feedback and failed(More)
Web service filtering is an efficient approach to address some big challenges in service computing, such as discovery, clustering and recommendation. The key operation of the filtering process is measuring the similarity of services. Several methods are used in current similarity calculation approaches such as string-based, corpus-based, knowledge-based and(More)
Big Data contains massive information, which are generating from heterogeneous, autonomous sources with distributed and anonymous platforms. Since, it raises extreme challenge to organizations to store and process these data. Conventional pathway of store and process is happening as collection of manual steps and it is consuming various resources. An(More)
Big Data contains massive information, which are generating from heterogeneous, autonomous sources with distributed and anonymous platforms. Since, it raises extreme challenge to organizations to store and process these data. Conventional pathway of store and process is happening as collection of manual steps and it is consuming various resources. An(More)
The Web is a popular, easy and common way to propagate information today and according to the growth of the Web, Web service discovery has become a challenging task. Clustering Web services into similar clusters through calculating the semantic similarity of Web services is one way for overcome this issue. Several methods are used for current similarity(More)
With increasing presence and adoption of Web Services on the World Wide Web, to recommend suitable services to users has become an important issue. However, existing personalization approaches, such as collaborative filtering or content based recommendations, are ignoring services' sociability because of the isolation of services without social(More)
Web service clustering is one of a very efficient approach to discover Web services efficiently. Current approaches use similarity-distance measurement methods such as string-based, corpus-based, knowledge-based and hybrid methods. These approaches have problems that include discovering semantic characteristics, loss of semantic information, shortage of(More)