A Comparison of Five Probabilistic View-Size Estimation Techniques in OLAP [r-libre/200]

Aouiche, Kamel et Lemire, Daniel (2007). A Comparison of Five Probabilistic View-Size Estimation Techniques in OLAP. Dans Proceedings of the 10th International Workshop on Data Warehousing and OLAP. ACM.

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Catégorie de document :	Communications dans des actes de congrès/colloques
Évaluation par un comité de lecture :	Oui
Étape de publication :	Publié
Résumé :	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.
Déposant:	Lemire, Daniel
Responsable :	Daniel Lemire
Dépôt :	27 août 2007
Dernière modification :	16 juill. 2015 00:47

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