Automatic Selection of Bitmap Join Indexes in Data Warehouses

TL;DR

This paper presents an automatic method for selecting optimal bitmap join indexes in data warehouses by using data mining and cost models to improve query performance and reduce administrative complexity.

Contribution

It introduces a novel auto-administration approach that leverages frequent itemset mining and cost models to automatically select the most beneficial indexes.

Findings

The proposed method effectively identifies high-profit indexes.

Cost models accurately evaluate index benefits and maintenance costs.

Automatic index selection improves query response times.

Abstract

The queries defined on data warehouses are complex and use several join operations that induce an expensive computational cost. This cost becomes even more prohibitive when queries access very large volumes of data. To improve response time, data warehouse administrators generally use indexing techniques such as star join indexes or bitmap join indexes. This task is nevertheless complex and fastidious. Our solution lies in the field of data warehouse auto-administration. In this framework, we propose an automatic index selection strategy. We exploit a data mining technique ; more precisely frequent itemset mining, in order to determine a set of candidate indexes from a given workload. Then, we propose several cost models allowing to create an index configuration composed by the indexes providing the best profit. These models evaluate the cost of accessing data using bitmap join indexes, and the cost of updating and storing these indexes.