Data Mining-based Materialized View and Index Selection in Data Warehouses

TL;DR

This paper proposes a data mining-based approach to jointly select materialized views and indexes in data warehouses, optimizing storage and query performance by considering their interactions.

Contribution

It introduces a coupled selection method using data mining and cost models, improving over independent selection strategies.

Findings

Joint selection outperforms independent methods in experiments

Cost models effectively evaluate benefits of views and indexes

Strategy reduces storage overhead while enhancing query speed

Abstract

Materialized views and indexes are physical structures for accelerating data access that are casually used in data warehouses. However, these data structures generate some maintenance overhead. They also share the same storage space. Most existing studies about materialized view and index selection consider these structures separately. In this paper, we adopt the opposite stance and couple materialized view and index selection to take view-index interactions into account and achieve efficient storage space sharing. Candidate materialized views and indexes are selected through a data mining process. We also exploit cost models that evaluate the respective benefit of indexing and view materialization, and help select a relevant configuration of indexes and materialized views among the candidates. Experimental results show that our strategy performs better than an independent selection of materialized views and indexes.