Md. Belal Hossain

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



Research Interests: Information Security, Network Security, Data Structures and Algorithms, Analysis of Algorithms, Combinatorial Optimization


Md. Belal Hossain was born in Kushtia, Bangladesh, in 1982. He received the B.Sc. degree in Computer Science & Engineering from the Khulna University, Bangladesh, in 2008 and the Master in Information Technology from the University of Dhaka, Bangladesh, in 2010. He is now working at The Central Bank of Bangladesh as a software engineer. His major research interest includes cyber security, smart & secured application development, search engine optimization, data recovery algorithms. He has published a number of research papers in various international conferences.

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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