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Dynamic Materialization for Building Personalized Smart Cubes

  • Daniel K. Antwi
  • Herna L. ViktorEmail author
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9670)

Abstract

Selecting the optimal subset of views for materialization provides an effective 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 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 this set of views may differ, from user to user, based on personal preferences. In addition, the set of queries that a specific 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 materialization 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 histories 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.

Keywords

Smart data cubes Dynamic cube construction Real-time OLAP Partial materialization Personalization User interests 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada

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