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arXiv · 0805.0747

Pruning Attribute Values From Data Cubes with Diamond Dicing

Hazel Webb·Owen Kaser·Daniel Lemire
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Abstract

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.

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Hazel Webb, Owen Kaser, Daniel Lemire. 2008-05-06. Pruning Attribute Values From Data Cubes with Diamond Dicing. https://arxiv.org/abs/0805.0747

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