The rapid growth of Internet technologies and availability of web tools created an opportunity to develop a robust and user-friendly web service model for medical care, and it demands urgent solutions as the uncertainty of disease spread threaten humanity. With changing Quality of Service principles, many existing web services need to offer specific medical services that suit practical needs. The provision of an effective service selection and recommendation features that best meet the user's requirements will be able to improve the quality of web service model. The Quality of Service metrics should be calculated and analyzed before optimizing a recommendation technique. Evaluation therefore forms an important part of the process for designing and implementing recommendation systems. Further, predicting Quality of Service indicators accurately from historical background dataset under complex scenarios and combination of conditions is useful. In this perspective, lots of web service methods are studied, and this paper presents our comprehensive analysis mainly focusing on the development of better web service based framework for medical applications.
Semantic Web service discovery provides high retrieval accuracy. However, it imposes an implicit constraint to service clients that the clients must express their queries with the same domain ontologies as used by the service providers. Fulfilling this criterion is very tedious. Hence, a WordNet (general ontology)-based similarity model is proposed for service discovery, and its accuracy is enhanced to a level comparable to the accuracy of computing similarity using service specific ontologies. This is done by optimizing similarity threshold, which refers to a minimum similarity that is required to decide whether a given pair of services is similar or not. The proposed model is implemented and results are presented. The approach warrants clients to express their queries without specifying any ontology and alleviates the problem of maintaining complex domain ontologies. Moreover, the computation time of WordNet-based model is very low when compared to specific ontology-based model.
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