Dynamic View Management System for Query Prediction to View Materialization

摘要

On-Line Analytical Processing (OLAP) systems based on data warehouses are the main systems for managerial decision making and must have a quick response time. Several algorithms have been presented to select the proper set of data and elicit suitable structured environments to handle the queries submitted to OLAP systems, which are called view selection algorithms to materialize. As users 'requirements may change during run time, materialization must be viewed dynamically. In this work, the authors propose and operate a dynamic view management system to select and materialize views with new and improved architecture, which predicts incoming queries through association rule mining and three probabilistic reasoning approaches: Conditional probability, Bayes 'rule, and Naive Bayes 'rule. The proposed system is compared with DynaMat system and Hybrid system through two standard measures. Experimental results show that the proposed dynamic view selection system improves these measures. This system outperforms DynaMat and Hybrid for each type of query and each sequence of incoming queries.