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Unasssuming View-Size Estimation Techniques in OLAP [r-libre/201]

Aouiche, Kamel, & Lemire, Daniel (2007). Unasssuming View-Size Estimation Techniques in OLAP. In Proceedings of the 9th International Conference on Enterprise Information Systems. Portugal : INSTICC.

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Item Type: Papers in Conference Proceedings
Refereed: Yes
Status: Published
Abstract: Even if storage was infinite, a data warehouse could not materialize all possible views due to the running time and update requirements. Therefore, it is necessary to estimate quickly, accurately, and reliably the size of views. Many available techniques make particular statistical assumptions and their error can be quite large. Unassuming techniques exist, but typically assume we have independent hashing for which there is no known practical implementation. We adapt an unassuming estimator due to Gibbons and Tirthapura: its theoretical bounds do not make unpractical assumptions. We compare this technique experimentally with stochastic probabilistic counting, LogLog probabilistic counting, and multifractal statistical models. Our experiments show that we can reliably and accurately (within 10%, 19 times out 20) estimate view sizes over large data sets (1.5 GB) within minutes, using almost no memory. However, only Gibbons-Tirthapura provides universally tight estimates irrespective of the size of the view. For large views, probabilistic counting has a small edge in accuracy, whereas the competitive sampling-based method (multifractal) we tested is an order of magnitude faster but can sometimes provide poor estimates (relative error of 100%). In our tests, LogLog probabilistic counting is not competitive. Experimental validation on the US Census 1990 data set and on the Transaction Processing Performance (TPC H) data set is provided.
Depositor: Lemire, Daniel
Owner / Manager: Daniel Lemire
Deposited: 25 Sep 2008
Last Modified: 16 Jul 2015 00:47

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