A New Scale for Attribute Dependency in Large Database Systems
This work addresses database administrators by providing a tool for query optimization, but it appears incremental as it builds on existing probabilistic analysis methods.
The paper tackles the problem of predicting future query patterns in large relational databases by proposing a numeric scale to measure attribute dependencies, aiming to enhance query execution speed through better prediction models.
Large, data centric applications are characterized by its different attributes. In modern day, a huge majority of the large data centric applications are based on relational model. The databases are collection of tables and every table consists of numbers of attributes. The data is accessed typically through SQL queries. The queries that are being executed could be analyzed for different types of optimizations. Analysis based on different attributes used in a set of query would guide the database administrators to enhance the speed of query execution. A better model in this context would help in predicting the nature of upcoming query set. An effective prediction model would guide in different applications of database, data warehouse, data mining etc. In this paper, a numeric scale has been proposed to enumerate the strength of associations between independent data attributes. The proposed scale is built based on some probabilistic analysis of the usage of the attributes in different queries. Thus this methodology aims to predict future usage of attributes based on the current usage.