Dynamic Multidimensional Data Cubes

Abstract

Data cubes are ubiquitous tools in data warehousing, online analytical processing, and decision support applications. Based on a selection of pre-computed and materialized aggregate values, they can dramatically speed up aggregation and summarization over large data collections. Traditionally, the emphasis has been on lowering query costs with little regard to maintenance, i.e., update cost issues. We argue that current trends require data cubes to be not only query-efficient, but also dynamic at the same time, and we also show how this can be achieved. Several array-based techniques with different tradeoffs between query and update cost are discussed in detail. We also survey selected approaches for sparse data and the popular data cube operator, CUBE. Moreover, this work includes an overview of future trends and their impact on data cubes.