Development of a framework to reduce overhead on database engine through data distribution

Abstract

Software driven solutions are limited to the amount of memory size and storage capacity, but the sizes of databases are increasing every day. Hence, now a day, handling data and accessing it in an acceptable time is one of the biggest challenges especially in a large database system. In a database, the records can be categorized according to the access frequencies; some records are very frequently accessed (hot data), some records are hardly accessed (cold data) and other records accessed occasionally (warm data). In a conventional database we keep all hot, warm and cold records in a single database. In case of record access (query, update etc.) a query might takes longer time even if a good data accessing algorithm (clustering/mining) incorporate with the database. Thus categorizing of the data set, i. e. clustering in terms of access frequency may improve data accessibility. In this paper, we are proposing a data clustering mechanism based on data access frequency. Finally, the expected result shows how and why data accessibility time should outperform other available data clustering techniques.