Index and Materialized View Selection in Data Warehouses

TL;DR

This paper reviews current index and materialized view selection methods in data warehouses, emphasizing data mining heuristics to improve performance optimization and reduce selection complexity.

Contribution

It introduces data mining-based heuristics for selecting indexes and views, addressing complexity and enhancing data warehouse performance optimization.

Findings

Heuristics effectively reduce selection problem complexity.

Data mining techniques identify the most relevant indexes and views.

Discussion of future trends in data warehouse optimization.

Abstract

The aim of this article is to present an overview of the major families of state-of-the-art index and materialized view selection methods, and to discuss the issues and future trends in data warehouse performance optimization. We particularly focus on data mining-based heuristics we developed to reduce the selection problem complexity and target the most pertinent candidate indexes and materialized views.