Efficient Partitioning of Large Databases without Query Statistics

An efficient way of improving the performance of a database management system is distributed processing. Distribution of data involves fragmentation or partitioning, replication, and allocation process. Previous research works provided partitioning based on empirical data about the type and frequency of the queries. These solutions are not suitable at the initial stage of a distributed database as query statistics are not available then. In this paper, I have presented a fragmentation technique, Matrix based Fragmentation (MMF), which can be applied at the initial stage as well as at later stages of distributed databases. Instead of using empirical data, I have developed a matrix, Modified Create, Read, Update and Delete (MCRUD), to partition a large database properly. Allocation of fragments is done simultaneously in my proposed technique. So using MMF, no additional complexity is added for allocating the fragments to the sites of a distributed database as fragmentation is synchronized with allocation. The performance of a DDBMS can be improved significantly by avoiding frequent remote access and high data transfer among the sites. Results show that proposed technique can solve the initial partitioning problem of large distributed databases.