The Iterative Data Cube

Data cubes provide aggregate information to support the analysis of the contents of data warehouses and databases. An important tool to analyze data in data cubes is the range query. For range queries that summarize large regions of massive data cubes, computing the query result on-they can result in non-interactive response times (e.g. in the order of minutes). To speed up range queries, values that summarize regions of the data cube are pre-computed and stored. This faster response time results in more expensive updates and/or space overhead. In this paper a technique is presented that generates a new class of schemes that support range queries on data cubes { Iterative Data Cubes. The main idea is that techniques that provide a certain tradeoo of query and update costs for a one-dimensional data cube are applied iteratively along the dimensions of data cubes of higher dimensionality. The diierent one-dimensional techniques can be combined, resulting in a great variety of Iterative Data Cubes. We show that Iterative Data Cubes are easy to generate, analyze and implement. They generalize previous seemingly unrelated approaches. Our technique is applicable to aggregate operators and attributes if the attribute's domain forms an abelian group under the aggregate operator (e.g., integers under addition). In this paper we focus on techniques that do not cause any space overhead for dense data cubes.