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Survey Paper | Computer Science & Engineering | India | Volume 5 Issue 11, November 2016
A Survey on Optimal Web Service Selection
Dipti Gumfawar | Manjushree Mahajan
Abstract: The last decade has witnessed a tremendous growth of web services as a major technology for sharing data, computing resources, and programs on the web. With the increasing adoption and presence of web services, design of novel approaches for effective web service recommendation to satisfy users potential requirements has become of paramount importance. Service-oriented computing and Web services are becoming more and more popular, enabling organizations to use the Web as a market for selling their own Web services and consuming existing Web services from others. Nevertheless, with the increasing adoption and presence of Web services, it becomes more difficult to find the most appropriate Web service that satisfies both users functional and nonfunctional requirements. QoS values are important criteria for service selection or recommendation. Most of the former works in web service selection and recommendation treat the QoS values as constants. However, QoS values of a service as perceived by a given user are intrinsically random variables because QoS value prediction can never be precise and there are always some unobserved random effects. Web services are not always able to fulfill customer requirements on their own, so in those cases it can choose to make a composition of web services. However, this is a complex problem since it must take into account the large number of available services, performance requirements, and other factors related to quality of service (QoS). With the fast development of Web services in service oriented systems, the requirement of efficient Quality of Service (QoS) evaluation methods becomes strong. However, many QoS values are unknown in reality. Therefore, it is necessary to predict the unknown QoS values of Web services based on the obtainable QoS values.
Keywords: Web service, sevice oriented computing, QoS, collaborative filtering, hidden markov models
Edition: Volume 5 Issue 11, November 2016,
Pages: 1744 - 1747