Trends in Improving Performances in Distributed Database Management Systems

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Author(s): Ismail Omar Hababeh (United Arab Emirates University, UAE)and Muthu Ramachandran (Leeds Metropolitan University, UK)
Copyright: 2010
Pages: 27
Source title: Handbook of Research on Software Engineering and Productivity Technologies: Implications of Globalization
Source Author(s)/Editor(s): Muthu Ramachandran (Leeds Metropolitan University, UK)and Rogério Atem de Carvalho (Instituto Federal Fluminense, Brazil)
DOI: 10.4018/978-1-60566-731-7.ch026

Keywords: Computer Science & IT / Information Science Reference / Systems & Software Design / Systems and Software Engineering

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Abstract

Database technology has been a significant field to work in for developing real life applications in network information systems. An enterprise’s reliance on its network and database applications in Distributed Database Management systems (DDBMS) environment is likely to continue growing exponentially. In such a system the estimation and prediction of Quality of Service (QoS) performance improvements are crucial since it increases understanding the issues that affect the distributed database networking system behaviour; like database fragmentation, clustering database network sites, and data allocation and replication that would reduce the amount of irrelevant data and speed up the transactions response time. This chapter introduces the trends of database management systems DBMS and presents an integrated method for designing Distributed Relational networking Database Management System DRDBMS that efficiently and effectively achieves the objectives of database fragmentation, clustering database network sites, and fragments allocation and replication. It is based on high speed partitioning, clustering, and data allocation techniques that minimize the data fragments accessed and data transferred through the network sites, maximize the overall system throughput by increasing the degree of concurrent transactions processing of multiple fragments located in different sites, and result in better QoS design and decision support.

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