Pruning Attribute Values From Data Cubes with Diamond Dicing

TL;DR

This paper introduces the diamond dice operator for multidimensional data pruning in data warehouses, enabling efficient computation of complex, multi-attribute queries on large datasets.

Contribution

It proposes a novel operator, diamond dice, and demonstrates its effectiveness on large data sets with practical computation times.

Findings

Diamond dice can be computed over 100 million facts in less than 35 minutes.

Experiments validate the operator's efficiency on large-scale data.

Diamond dicing addresses the challenge of multidimensional data pruning.

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.