2012/06/27 by Soumya Sen, Sen, Soumya, Anjan Dutta +5
Computer Science · #Advanced Database Systems and Queries #Artificial intelligence #Computer science #Context (archaeology) #Data Management and Algorithms #Data Mining Algorithms and Applications #Data mining #Data warehouse #Database #Database design #Databases (cs.DB) #Dependency (UML) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Information retrieval #Relational database #Relational model #SQL #Scale (ratio) #Set (abstract data type) #Table (database) #View #cs.DB #cs.IR
paper · pdf · doi:10.48550/arxiv.1206.6322
published in arXiv (Cornell University) (Cornell University) · 12 pages - paper accepted for presentation and publication in CISIM 2012 International Confrence
arxiv created 2012/06/27 · openalex publication_date 2012/06/27 · arxiv updated 2012/06/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.