Frequent itemsets mining for database auto-administration

TL;DR

This paper presents a method using frequent itemset mining to automatically generate index configurations, significantly improving database performance in data warehouses.

Contribution

It introduces a novel tool that extracts frequent itemsets from workload data to automate index optimization, enhancing performance without manual intervention.

Findings

Performance gains of 15% to 25% on test databases.

Effective automatic index configuration generation.

Potential for reducing manual database administration tasks.

Abstract

With the wide development of databases in general and data warehouses in particular, it is important to reduce the tasks that a database administrator must perform manually. The aim of auto-administrative systems is to administrate and adapt themselves automatically without loss (or even with a gain) in performance. The idea of using data mining techniques to extract useful knowledge for administration from the data themselves has existed for some years. However, little research has been achieved. This idea nevertheless remains a very promising approach, notably in the field of data warehousing, where queries are very heterogeneous and cannot be interpreted easily. The aim of this study is to search for a way of extracting useful knowledge from stored data themselves to automatically apply performance optimization techniques, and more particularly indexing techniques. We have designed a tool that extracts frequent itemsets from a given workload to compute an index configuration that helps optimizing data access time. The experiments we performed showed that the index configurations generated by our tool allowed performance gains of 15% to 25% on a test database and a test data warehouse.