Optimizing Large Join Queries Using A Graph-Based Approach
IEEE Transactions on Knowledge and Data Engineering
Algebraic Foundation and Optimization for Object Based Query Languages
Proceedings of the Ninth International Conference on Data Engineering
Selection of Views to Materialize in a Data Warehouse
ICDT '97 Proceedings of the 6th International Conference on Database Theory
A Framework for Designing Materialized Views in Data Warehousing Environment
ICDCS '97 Proceedings of the 17th International Conference on Distributed Computing Systems (ICDCS '97)
Improved Bitmap Indexing Strategy for Data Warehouses
ICIT '06 Proceedings of the 9th International Conference on Information Technology
Multi-query Optimization for Distributed Similarity Query Processing
ICDCS '08 Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems
Data mining-based materialized view and index selection in data warehouses
Journal of Intelligent Information Systems
Optimization of Linear Recursive Queries in SQL
IEEE Transactions on Knowledge and Data Engineering
Materialized view management in peer to peer environment
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
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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.