New Patterns Discovery for Web Services Compositionfrom Mining Execution Logs

Ritcharoenwattu, Tossapol; Rungworawut, Wararat

doi:10.7763/ijcee.2013.v5.670

“…In the proposed system, the mathematical calculation is prove that the selection of correct ontology for converting WSDL to OWL. Ritcharoenwattu and Rungworawut (2013) Provides a frame for extract new pattern using mining association rules. For a WSDL file, type elements are extracted and respective concepts are recognized.…”

Section: Resultsmentioning

confidence: 99%

Selection of Ontology for Web Service Description Language to Ontology Web Language Conversion

Mannan¹

2014

Journal of Computer Science

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Semantic web is to extend the current human readable web to encoding some of the semantic of resources in a machine processing form. As a Semantic web component, Semantic Web Services (SWS) uses a mark-up that makes the data into detailed and sophisticated machine readable way. One such language is Ontology Web Language (OWL). Existing conventional web service annotation can be changed to semantic web service by mapping Web Service Description Language (WSDL) with the semantic annotation of OWL-S. In this conversion of WSDL to OWL process, the ontology plays a vital role. Ontology can be stored and retrieved from local repository and selecting the appropriate ontology is a complicated process and this can be achieved by Ontology Searching and Property Matching (OSPM) engine. Ontology is stored in the local repository as ontology document and exact matching of ontology for the requested query can be searched using semantic similarity ranking method. High ranked classes of ontology will undergo property matching; here requested concept will be matched with the resulting property. OSPM engine act as the backbone for selecting an exact ontology and reduce the conflict that occurs while selecting the ontology for annotation purpose.

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“…Ritcharoenwattu and Rungworawut (2013) Provides a frame for extract new pattern using mining association rules. For a WSDL file, type elements are extracted and respective concepts are recognized.…”

Section: Resultsmentioning

confidence: 99%

Selection of Ontology for Web Service Description Language to Ontology Web Language Conversion

Mannan¹,

Sundarambal²,

Raghul³

2014

Journal of Computer Science

2

0

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Semantic web is to extend the current human readable web to encoding some of the semantic of resources in a machine processing form. As a Semantic web component, Semantic Web Services (SWS) uses a mark-up that makes the data into detailed and sophisticated machine readable way. One such language is Ontology Web Language (OWL). Existing conventional web service annotation can be changed to semantic web service by mapping Web Service Description Language (WSDL) with the semantic annotation of OWL-S. In this conversion of WSDL to OWL process, the ontology plays a vital role. Ontology can be stored and retrieved from local repository and selecting the appropriate ontology is a complicated process and this can be achieved by Ontology Searching and Property Matching (OSPM) engine. Ontology is stored in the local repository as ontology document and exact matching of ontology for the requested query can be searched using semantic similarity ranking method. High ranked classes of ontology will undergo property matching; here requested concept will be matched with the resulting property. OSPM engine act as the backbone for selecting an exact ontology and reduce the conflict that occurs while selecting the ontology for annotation purpose.

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Implementation of Mining Techniques to Enhance Discovery in Service-Oriented Computing

Surianarayanan

¹

,

Ganapathy

²

2014

Data Mining and Analysis in the Engineering Field

0

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Web services have become the de facto platform for developing enterprise applications using existing interoperable and reusable services that are accessible over networks. Development of any service-based application involves the process of discovering and combining one or more required services (i.e. service discovery) from the available services, which are quite large in number. With the availability of several services, manually discovering required services becomes impractical and time consuming. In applications having composition or dynamic needs, manual discovery even prohibits the usage of services itself. Therefore, effective techniques which extract relevant services from huge service repositories in relatively short intervals of time are crucial. Discovery of service usage patterns and associations/relationships among atomic services would facilitate efficient service composition. Further, with availability of several services, it is more likely to find many matched services for a given query, and hence, efficient methods are required to present the results in useful form to enable the client to choose the best one. Data mining provides well known exploratory techniques to extract relevant and useful information from huge data repositories. In this chapter, an overview of various issues of service discovery and composition and how they can be resolved using data mining methods are presented. Various research works that employ data mining methods for discovery and composition are reviewed and classified. A case study is presented that serves as a proof of concept for how data mining techniques can enhance semantic service discovery.

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New Patterns Discovery for Web Services Compositionfrom Mining Execution Logs

Cited by 4 publications

References 4 publications

Selection of Ontology for Web Service Description Language to Ontology Web Language Conversion

Selection of Ontology for Web Service Description Language to Ontology Web Language Conversion

Selection of Ontology for Web Service Description Language to Ontology Web Language Conversion

Implementation of Mining Techniques to Enhance Discovery in Service-Oriented Computing

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