Pruning Attribute Values From Data Cubes with Diamond Dicing

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dc.creatorWebb, Hazel
dc.creatorKaser, Owen
dc.creatorLemire, Daniel
dc.date2008-05-06
dc.date.accessioned2026-07-07T09:37:18Z
dc.date.available2026-07-07T09:37:18Z
dc.descriptionData 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.
dc.identifierhttps://arxiv.org/abs/0805.0747
dc.identifierhttp://arxiv.org/abs/0805.0747
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/160413
dc.subjectDatabases
dc.subjectData Structures and Algorithms
dc.titlePruning Attribute Values From Data Cubes with Diamond Dicing
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