An Architecture Framework for Complex Data Warehouses

TL;DR

This paper proposes a comprehensive architecture framework for managing complex data in data warehouses, emphasizing metadata and XML to handle diverse data types and facilitate integration and analysis.

Contribution

It introduces a novel architecture framework specifically designed for complex data warehousing, leveraging XML and domain knowledge to address structural and processing challenges.

Findings

Framework effectively manages diverse complex data types.

Metadata and XML facilitate data integration and communication.

Supports decision support applications with complex data needs.

Abstract

Nowadays, many decision support applications need to exploit data that are not only numerical or symbolic, but also multimedia, multistructure, multisource, multimodal, and/or multiversion. We term such data complex data. Managing and analyzing complex data involves a lot of different issues regarding their structure, storage and processing, and metadata are a key element in all these processes. Such problems have been addressed by classical data warehousing (i.e., applied to "simple" data). However, data warehousing approaches need to be adapted for complex data. In this paper, we first propose a precise, though open, definition of complex data. Then we present a general architecture framework for warehousing complex data. This architecture heavily relies on metadata and domain-related knowledge, and rests on the XML language, which helps storing data, metadata and domain-specific knowledge altogether, and facilitates communication between the various warehousing processes.