Paper 3
Dynamic Materialization for Building Personalized Smart CubesAuthors: Daniel K. Antwi and Herna L. Viktor |
AbstractSelecting the optimal subset of views for materialization provides an e ective way to reduce the query evaluation time for real-time Online Analytic Processing (OLAP) queries posed against a data warehouse. However, materializing a large number of views may be counter-productive and may exceed storage thresholds, especially when consider- ing very large data warehouses. Thus, an important concern is to nd the best set of views to materialize, in order to guarantee acceptable query response times. It further follows that this set of views may di er, from user to user, based on personal preferences. In addition, the set of queries that a speci c user poses also changes over time, which further impacts the view selection process. In this paper, we introduce the personalized Smart Cube algorithm that combines vertical partitioning, partial ma- terialization and dynamic computation to address these issues. 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 his- tories and user interests. The experimental evaluation of our personalized 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. |