Building Smart Cubes for Reliable and Faster Access to Data

Abstract

In data warehousing, selecting a subset of views for materialization has been widely employed as a way to reduce the query evaluation time for real-time OLAP queries. However, materialization of a large number of views may be counterproductive and may exceed storage thresholds, especially when considering very large data warehouses. Thus, an important concern is to find the best set of views to materialize, in order to guarantee acceptable query response times. It further follows that the best set of views may change, as the query histories evolve. To address these issues, we introduce the Smart Cube algorithm that combines vertical partitioning, partial materialization and dynamic computation. In our approach, we partition the search space into fragments and proceed to select the optimal subset of fragments to materialize. We dynamically adapt the set of materialized views that we store, as based on query histories. The experimental evaluation of our Smart Cube algorithm shows that our work compare favorably with the state-of-the-art. The results indicate that our algorithm materializes a smaller number of views than other techniques, while yielding fast query response times.