The data warehouse toolkit: practical techniques for building dimensional data warehouses
The data warehouse toolkit: practical techniques for building dimensional data warehouses
Proceedings of the 2nd ACM international workshop on Data warehousing and OLAP
Designing data marts for data warehouses
ACM Transactions on Software Engineering and Methodology (TOSEM)
The Data Warehouse Lifecycle Toolkit: Expert Methods for Designing, Developing and Deploying Data Warehouses with CD Rom
A Logical Approach to Multidimensional Databases
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
A Method for Demand-Driven Information Requirements Analysis in Data Warehousing Projects
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 8 - Volume 8
Goal-oriented requirement analysis for data warehouse design
Proceedings of the 8th ACM international workshop on Data warehousing and OLAP
A UML-based data warehouse design method
Decision Support Systems
Reconciling requirement-driven data warehouses with data sources via multidimensional normal forms
Data & Knowledge Engineering
Proceedings of the ACM tenth international workshop on Data warehousing and OLAP
Automatic validation of requirements to support multidimensional design
Data & Knowledge Engineering
A framework for multidimensional design of data warehouses from ontologies
Data & Knowledge Engineering
Towards multidimensional requirement design
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Multidimensional integrated ontologies: a framework for designing semantic data warehouses
Journal on Data Semantics XIII
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In this paper we discuss what current multidimensional design approaches provide and which are their major flaws. Our contribution lays in a comprehensive framework that does not focus on how these approaches work but what they do provide for usage in real data warehouse projects. So that, we do not aim at comparing current approaches but set up a framework (based on four criteria: the role played by enduser requirements and data sources, the degree of automation achieved and the quality of the output produced) highlighting their drawbacks, and the need for further research on this area.