5 - Data Cube Technology

Abstract

This chapter focuses on data cube technology. Data warehouse systems provide online analytical processing (OLAP) tools for interactive analysis of multidimensional data at varied granularity levels. OLAP tools typically use the data cube and a multidimensional data model to provide flexible access to summarized data. A data cube can interactively explore the data in a multidimensional way through OLAP operations like drill-down (to see more specialized data such as total sales per city) or roll-up (to see the data at a more generalized level such as total sales per country). Although the data cube concept was originally intended for OLAP, it is also useful for data mining. Multidimensional data mining is an approach to data mining that integrates OLAP-based data analysis with knowledge discovery techniques. It is also known as exploratory multidimensional data mining and online analytical mining (OLAM). It searches for interesting patterns by exploring the data in multidimensional space. Users can interactively drill down or roll up to varying abstraction levels to find classification models, clusters, predictive rules, and outliers. Methods for data cube computation and methods for multidimensional data analysis are focused on. Precomputing a data cube (or parts of a data cube) allows for fast accessing of summarized data. Given the high dimensionality of most data, multidimensional analysis can run into performance bottlenecks. Therefore, it is important to study data cube computation techniques. Data cube technology provides many effective and scalable methods for cube computation. Studying these methods also help in the understanding and further development of scalable methods for other data mining tasks such as the discovery of frequent patterns.