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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.
Fichier(s) associé(s) à ce document :| PDF - 0805.0747v1.pdf |
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| Catégorie de document : | Communications dans des actes de congrès/colloques |
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| 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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