A Comparison of Five Probabilistic View-Size Estimation Techniques in OLAP Export

@article{citeulike:4184916, abstract = {A data warehouse cannot materialize all possible views, hence we must estimate quickly, accurately, and reliably the size of views to determine the best candidates for materialization. Many available techniques for view-size estimation make particular statistical assumptions and their error can be large. Comparatively, unassuming probabilistic techniques are slower, but they estimate accurately and reliability very large view sizes using little memory. We compare five unassuming hashing-based view-size estimation techniques including Stochastic Probabilistic Counting and LogLog Probabilistic Counting. Our experiments show that only Generalized Counting, Gibbons-Tirthapura, and Adaptive Counting provide universally tight estimates irrespective of the size of the view; of those, only Adaptive Counting remains constantly fast as we increase the memory budget.}, archivePrefix = {arXiv}, author = {Aouiche, Kamel and Lemire, Daniel}, citeulike-article-id = {4184916}, citeulike-linkout-0 = {http://arxiv.org/abs/cs/0703058}, citeulike-linkout-1 = {http://arxiv.org/pdf/cs/0703058}, day = {8}, eprint = {cs/0703058}, keywords = {olap}, month = {Dec}, posted-at = {2009-03-17 01:19:13}, priority = {0}, title = {A Comparison of Five Probabilistic View-Size Estimation Techniques in OLAP}, url = {http://arxiv.org/abs/cs/0703058}, year = {2008} }

TY - JOUR ID - citeulike:4184916 L3 - citeulike-article-id:4184916 N2 - A data warehouse cannot materialize all possible views, hence we must estimate quickly, accurately, and reliably the size of views to determine the best candidates for materialization. Many available techniques for view-size estimation make particular statistical assumptions and their error can be large. Comparatively, unassuming probabilistic techniques are slower, but they estimate accurately and reliability very large view sizes using little memory. We compare five unassuming hashing-based view-size estimation techniques including Stochastic Probabilistic Counting and LogLog Probabilistic Counting. Our experiments show that only Generalized Counting, Gibbons-Tirthapura, and Adaptive Counting provide universally tight estimates irrespective of the size of the view; of those, only Adaptive Counting remains constantly fast as we increase the memory budget. TI - A Comparison of Five Probabilistic View-Size Estimation Techniques in OLAP KW - olap AU - Aouiche, Kamel AU - Lemire, Daniel PY - 2008/Dec/08/ UR - http://arxiv.org/abs/cs/0703058 ER -