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2021 | OriginalPaper | Buchkapitel

Web Service Clustering Approaches to Enhance Service Discovery: A Review

verfasst von : Neha Agarwal, Geeta Sikka, Lalit Kumar Awasthi

Erschienen in: Recent Innovations in Computing

Verlag: Springer Singapore

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Abstract

Due to the emergence of Internet technologies and service-oriented computing, there is a rapid growth in the quantity and variety of services on the web. Discovering the web services as per the request is not an easy task because of the advancement of service-oriented computing which includes web services, cloud services, mobile services, etc. These services are dynamic and published according to the emerging standards in repositories. Web service clustering plays a crucial role in web service discovery. When services are grouped according to the similarity, then it reduces the search space and time, so that services can be discovered efficiently. Many eminent researchers have proposed approaches for efficient web service discovery by incorporating web service clustering. In this paper, we review different approaches that are proposed for web service clustering to enhance the discovery process. A comparison among existing approaches is carried out based on the vector representation approach, dimensionality reduction technique, method to capture semantic relationship among features, clustering technique, type of input dataset, number of web services in dataset and service repository. This review will help the researchers to understand the existing techniques to group services in similar clusters to improvise service discovery and the scope for improvement by future directions.

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Titel: Web Service Clustering Approaches to Enhance Service Discovery: A Review
verfasst von: Neha Agarwal
Geeta Sikka
Lalit Kumar Awasthi
Verlag: Springer Singapore
Buch: Recent Innovations in Computing
Print ISBN: 978-981-15-8296-7

Electronic ISBN: 978-981-15-8297-4

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-981-15-8297-4_3

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