Md. Sharifur Rahman

Work place: Institute of Information Technology University of Dhaka, Dhaka, Bangladesh



Research Interests: Computational Engineering, Software Construction, Software Engineering


Md. Sharifur Rahman received his Post Graduate Diploma and Master in Information Technology from University of Dhaka, Bangladesh, in 2008 and 2009 respectively. He is Senior IT Consultant Government Republic of Bangladesh Finance Division, Ministry of Finance. His major work area includes Government Financial Management, Pension & Fund Management, Budget Management analytics, Service Oriented Architecture, Web Service Management and software engineering.

Author Articles
Subset Matching based Selection and Ranking (SMSR) of Web Services

By Md. Abdur Rahman Md. Belal Hossain Md. Sharifur Rahman Saeed Siddik

DOI:, Pub. Date: 8 Apr. 2019

Web service is a software application, which is accessible using platform independent and language neutral web protocols. However, selecting the most relevant services became one of the vital challenges. Quality of services plays very important role in web service selection, as it determines the quality and usability of a service, including its non-functional properties such as scalability, accessibility, integrity, efficiency, etc. When agent application send request with a set of quality attributes, it becomes challenging to find out the best service for satisfying maximum quality requirements. Among the existing approaches, the single value decomposition technique is popular one; however, it suffers for computational complexity. To overcome this limitation, this paper proposed a subset matching based web service selection and ranking by considering the quality of service attributes. This proposed method creates a quality-web matrix to store available web services and associated quality of service attributes. Then, matrix subsets are created using web service repository and requested quality attributes. Finally, web services are efficiently selected and ranked based on calculated weights of corresponding web services to reduce composition time. Experimental results showed that proposed method performs more efficient and scalable than existing several techniques such as single value decomposition.

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