Efficiency Analysis of Materialized views in DataWarehouse Using selfmaintenance

TL;DR

This paper investigates optimizing the maintenance of materialized views in data warehouses by proposing an algorithm that minimizes auxiliary views and space, enhancing self-maintainability and efficiency.

Contribution

It introduces a novel algorithm leveraging key and referential constraints to reduce auxiliary views and space in data warehouse maintenance.

Findings

Reduces the number of auxiliary views needed for maintenance

Uses information sharing to further minimize auxiliary views

Enhances self-maintainability of data warehouse views

Abstract

A data warehouse is a large data repository for the purpose of analysis and decision making in organizations. To improve the query performance and to get fast access to the data, data is stored as materialized views (MV) in the data warehouse. When data at source gets updated, the materialized views also need to be updated. In this paper, we focus on the problem of maintenance of these materialized views and address the issue of finding such auxiliary views (AV) that together with the materialized views make the data self-maintainable and take minimal space. We propose an algorithm that uses key and referential constraints which reduces the total number of tuples in auxiliary views and uses idea of information sharing between these auxiliary views to further reduce number of auxiliary views.