Support Vector Machine Based Data Classification to Avoid Data Redundancy Removal before Persist the Data in a DBMS

Abstract

Data Base Management System is one of the growing fields in computing world. Grid computing, internet sharing, distributed computing, parallel processing and cloud are the areas store their huge amount of data in a DBMS to maintain the structure of the data. Memory management is one of the major portions in DBMS due to edit, delete, recover and commit operations used on the records. To improve the memory utilization efficiently, the redundant data should be eliminated accurately. In this paper, the redundant data is fetched by the Quick Search Bad Character (QSBC) function and intimate to the DB admin to remove the redundancy. QSBC function compares the entire data with patterns taken from index table created for all the data persisted in the DBMS to easy comparison of redundant (duplicate) data in the database. This experiment in examined in SQL server software on a university student database and performance is evaluated in terms of time and accuracy. The database is having 15000 students data involved in various activities.