Dynamic index selection in data warehouses

TL;DR

This paper introduces an automatic, dynamic index selection method for data warehouses that adapts to evolving workloads using incremental frequent itemset mining, improving query performance efficiently.

Contribution

It proposes a novel incremental approach for index selection that updates indexes dynamically based on workload changes, reducing manual effort and reindexing costs.

Findings

Improves query response times in data warehouses.

Reduces overhead compared to static index strategies.

Demonstrates effectiveness through preliminary experiments.

Abstract

Analytical queries defined on data warehouses are complex and use several join operations that are very costly, especially when run on very large data volumes. To improve response times, data warehouse administrators casually use indexing techniques. This task is nevertheless complex and fastidious. In this paper, we present an automatic, dynamic index selection method for data warehouses that is based on incremental frequent itemset mining from a given query workload. The main advantage of this approach is that it helps update the set of selected indexes when workload evolves instead of recreating it from scratch. Preliminary experimental results illustrate the efficiency of this approach, both in terms of performance enhancement and overhead.