Shared-Table for Textual Data Clustering in Distributed Relational Databases

High-performance query processing is a significant requirement of database administrators that can be achieved by grouping data into continuous hard disk pages. Such performance can be achieved by using database partitioning techniques. Database partitioning techniques aid in splitting of the physical structure of database tables into small partitions. A distributed database management system is advantageous for many businesses because such a system aids in the achievement of high-performance processing. However, massive amount of data distributed over network nodes affect query processing when retrieving data from different nodes. This study proposes a novel technique based on a shared-table in a relational database under a distributed environment to achieve high-performance query processing by using data mining techniques. A shared-table is used as a guide to show where the data should be saved. Thus, the efficiency of query processing will improve when data is saved at the same location. The proposed method is suitable for news agencies and domains that rely on massive amount of textual data.