A Survey on Data Warehouse Evolution

TL;DR

This survey reviews various approaches to managing the evolution of data warehouses, addressing challenges posed by changing data sources and schemas to ensure accurate decision support systems.

Contribution

It provides an overview and comparative analysis of existing methods for handling data warehouse evolution and change management.

Findings

Identifies key challenges in DW evolution

Classifies different approaches to change management

Highlights gaps and future research directions

Abstract

The data warehouse (DW) technology was developed to integrate heterogeneous information sources for analysis purposes. Information sources are more and more autonomous and they often change their content due to perpetual transactions (data changes) and may change their structure due to continual users' requirements evolving (schema changes). Handling properly all type of changes is a must. In fact, the DW which is considered as the core component of the modern decision support systems has to be update according to different type of evolution of information sources to reflect the real world subject to analysis. The goal of this paper is to propose an overview and a comparative study between different works related to the DW evolution problem.