Pruning attribute values from data cubes with diamond dicing

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. terms Theory, Algorithms, Experimentation keywords Diamond cube, data warehouses, information retrieval, OLAP

• Type: Conference
• Year: 2008
• Where: IDEAS
• Authors: Hazel Webb, Owen Kaser, Daniel Lemire
• Keywords: Data Warehouses, Database, Diamond Cube, IDEAS 2008, Multidimensional Queries