[Paper Review] An Architecture Framework for Complex Data Warehouses

Jérôme Darmont, Omar Boussaïd+2 more|ArXiv.org|Jul 10, 2007

Advanced Database Systems and Queries7 references4 citations

TL;DR

This paper proposes a metadata- and XML-centric architecture framework for managing complex data warehouses, integrating multiformat, multisource, and multimodal data through a layered design. The framework supports both virtual and centralized warehousing, emphasizing metadata and domain-specific knowledge for ETL, integration, analysis, and performance optimization in complex data environments.

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.

Motivation & Objective

• To define and formalize the concept of complex data, including multiformat, multisource, multistructure, multimodal, and multiversion characteristics.
• To address the limitations of classical data warehousing in handling non-numerical, heterogeneous, and semantically rich data.
• To design a general-purpose architecture framework that supports both virtual and centralized complex data warehousing.
• To emphasize the critical role of metadata and domain-specific knowledge in managing, integrating, and analyzing complex data.
• To identify key challenges in complex data warehousing, including data integration, multidimensional modeling, and performance optimization.

Proposed method

• Proposes a five-axis framework for defining complex data: multiformat, multistructure, multisource, multimodal, and multiversion.
• Introduces a layered architecture with a warehouse kernel, metadata and knowledge base layer, and three core processes: ETL/integration, administration/monitoring, and analysis/usage.
• Uses XML as a unifying data, metadata, and communication format across all layers and processes.
• Defines four data flows: external (ETL and analysis), internal (between kernel and metadata layer), metadata management, and reference flow (metadata as central control).
• Supports both virtual warehousing (on-the-fly data integration via mediators) and centralized XML-based warehousing (persistent XML document storage).
• Proposes using XML and RDF schemas for representing metadata and domain knowledge, with potential integration of CWM for standardization.

Experimental results

Research questions

• RQ1 How can complex data—defined by multiple dimensions of heterogeneity—be formally characterized and distinguished from classical data warehouse inputs?
• RQ2 What architectural components are necessary to support the integration, management, and analysis of complex data across diverse sources and formats?
• RQ3 How can metadata and domain-specific knowledge be systematically modeled and leveraged to improve data warehousing processes?
• RQ4 What are the key performance and scalability challenges in complex data warehousing, and how can they be addressed through architectural design?
• RQ5 To what extent can existing standards like CWM be extended or adapted to support complex data warehousing needs?

Key findings

• The proposed architecture framework provides a comprehensive, extensible model for complex data warehousing that supports both virtual and centralized approaches.
• Metadata and domain-specific knowledge are identified as central to managing complexity, enabling semantic understanding and process coordination.
• XML serves as a unifying format for data, metadata, and communication, facilitating interoperability across heterogeneous sources and processes.
• The framework identifies four distinct data flows, with the reference flow ensuring that all external operations (ETL, analysis) depend on metadata and knowledge for consistency and correctness.
• The architecture enables the reuse of analysis results as new data sources, supporting iterative and evolving data warehousing processes.
• The study identifies open challenges in metadata representation, particularly regarding the suitability of CWM for complex data workloads and the need for extended or new metamodels.