Pruning Attributes From Data Cubes with Diamond Dicing [r-libre/221]

Webb, Hazel, Kaser, Owen et Lemire, Daniel (2008). Pruning Attributes From Data Cubes with Diamond Dicing. Dans IDEAS '08 Proceedings of the 2008 international symposium on Database engineering & applications. ACM International Conference Proceeding Series.

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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 :	Data stored in a data warehouse are inherently multidimensional, but most data-pruning techniques (such as iceberg and top-k queries) are unidimensional. However, analysts need to issue multidimensional queries. For example, an analyst may need to select not just the most profitable stores or--separately--the most profitable products, but simultaneous sets of stores and products fulfilling some profitability constraints. To fill this need, we propose a new operator, the diamond dice. Because of the interaction between dimensions, the computation of diamonds is challenging. We present the first diamond-dicing experiments on large data sets. Experiments show that we can compute diamond cubes over fact tables containing 100 million facts in less than 35 minutes using a standard PC.
Déposant:	Lemire, Daniel
Responsable :	Daniel Lemire
Dépôt :	24 juill. 2014 18:15
Dernière modification :	16 juill. 2015 00:47

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