Data Cube Technology - Chapter 5 - Data Cube Computation and Multidimensional Analysis

Sep 17, 2026

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Data Cube Technology introduces how data cube and multidimensional modeling support interactive OLAP analysis across varying granularities, including drill-down and roll-up operations on summarized measures. It also frames data cube ideas for exploratory multidimensional data mining and online analytical mining, guiding users to discover patterns in multidimensional space. The chapter focuses on data cube computation methods, contrasting full and partial materialization, and then extends to cube-based query processing and multidimensional analysis techniques such as sampling, ranking, prediction, multifeature, and exception-driven exploration.

LanguageEnglish

CategoryTechnology

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Pages64

Reading Time2 h 41 min

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Data Cube Technology

Data Cube Computation: Preliminary Concepts

Cubes, Materialization, and Computation Methods

Processing Advanced Kinds of Queries by Exploring Cube Technology

Multidimensional Data Analysis in Cube Space

Summary

Exercises

Bibliographic Notes

Contents 5 Data Cube Technology 3 5.1 Data Cube Computation: Preliminary Concepts ......... 4 5.1.1 Cube Materialization: Full Cube, Iceberg Cube, Closed Cube, and Cube Shell .................... 4 5.1.2 General Strategies for Data Cube Computation ...... 8 5.2 Data Cube Computation Methods ................. 10 5.2.1 Multiway Array Aggregation for Full Cube Computation 11 5.2.2 BUC: Computing Iceberg Cubes from the Apex Cuboid Downward ........................... 16 5.2.3 Star-Cubing: Computing Iceberg Cubes Using a Dynamic Star-tree Structure ............... 19 5.2.4 Precomputing Shell Fragments for Fast High-Dimensional OLAP ............................. 25 5.3 Processing Advanced Kinds of Queries by Exploring Cube Technology ................................. 33 5.3.1 Sampling Cubes: OLAP-based Mining on Sampling Data 33 5.3.2 Ranking Cubes: Eﬃcient Computation of Top-k Queries 40 5.4 Multidimensional Data Analysis in Cube Space .......... 42 5.4.1 Prediction Cubes: Prediction Mining in Cube Space ... 43 5.4.2 Multifeature Cubes: Complex Aggregation at Multiple Granularities ......................... 45 5.4.3 Exception-Based Discovery-Driven Exploration of Cube Space ............................. 46 5.5 Summary ............................... 50 5.6 Exercises ............................... 51 5.7 Bibliographic Notes .......................... 56 2 CONTENTS Chapter 5 Data Cube Technology Data warehouse systems provide OLAP tools for interactive analysis of multidimensional data at varied levels of granularity. OLAP tools typically use the data cube and a multidimensional data model to provide ﬂexible access to summarized data. For example, a data cube can store precomputed measures (like count and total sales) for multiple combinations of data dimensions (like item, region, and customer) . Users can pose OLAP queries on the data. They can also 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. This gives users the freedom to dynamically focus on any subset of interesting dimensions. Users can interactively drill down or roll up to varying levels of abstraction to ﬁnd classiﬁcation models, clusters, predictive rules, and outliers. This chapter focusses on data cube technology. In particular, we study methods for data cube computation and methods for multidimensional data analysis. 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. Luckily, data cube technology provides many eﬀective and scalable methods for cube computation. Studying such methods will also help in our understanding and the development of scalable methods for other data mining tasks, such as the discovery of frequent patterns (Chapters 6 and 7) . We begin in Section 5.1 with preliminary concepts for cube computation. These summarize the notion of the data cube as a lattice of cuboids, and describe 4 CHAPTER 5 . DATA CUBE TECHNOLOGY the basic forms of cube materialization. General strategies for cube computation are given. Section 5.2 follows with an in-depth look at speciﬁc methods for data cube computation. We study both full materialization (that is, where all of the cuboid

FAQs

What role do data cubes play in OLAP and multidimensional data analysis?

Data cubes store precomputed measures across combinations of dimensions, enabling fast interactive OLAP analysis. They allow users to drill down or roll up to view data at different abstraction levels.

How does the chapter distinguish full cube materialization from partial materialization?

Full materialization precomputes all cuboids needed for the cube, while partial materialization precomputes only selected “useful” parts to reduce performance bottlenecks from high dimensionality.

Which cube-based query processing and analysis techniques are covered?

The chapter covers sampling cubes for OLAP over sampling data, ranking cubes for efficient top-k queries, prediction cubes for predictive modeling, multifeature cubes for complex multi-granularity aggregates, and exception-based exploration to guide discovery of outliers.

Sep 17, 2026

Upload by Riley