Optimizing and enhancing performance of database engine using data clustering technique

Abstract

The sizes of databases are increasing every day. Hence, now days, accessing data in an acceptable time is one of the biggest challenges in centralized database. In centralized databases, the records can be categorized according to the access frequencies; least accessed records (cold data) and most accessed records (hot data). In a study it shows that more than 90% cases query are requested for hot data, and in case of insertion operation, 99% are done on hot data. Thus categorizing of the data set may improve data accessibility. In this paper, we are proposing a data clustering mechanism based on data access frequency. We have considered only the hot data and the cold data. Here we divided the whole database into two separate files. The first file contains only hot data and the second file contains only the cold data. The time period of hot and cold data will vary for different application domains. The database engine will have direct access on the first database file and in case of unavailability of data; the database engine will look for the second database file. Finally, the experiment result shows how and why data accessibility time should outperform than other available data clustering techniques.