A foundation for capturing and querying complex multidimensional data
Information Systems - Data warehousing
Extending Practical Pre-Aggregation in On-Line Analytical Processing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
STORM: A Statistical Object Representation Model
Proceedings of the 5th International Conference SSDBM on Statistical and Scientific Database Management
Summarizability in OLAP and Statistical Data Bases
SSDBM '97 Proceedings of the Ninth International Conference on Scientific and Statistical Database Management
Normal Forms for Multidimensional Databases
SSDBM '98 Proceedings of the 10th International Conference on Scientific and Statistical Database Management
Should Optional Properties Be Used in Conceptual Modelling? A Theory and Three Empirical Tests
Information Systems Research
Multidimensional normal forms for data warehouse design
Information Systems
Research in data warehouse modeling and design: dead or alive?
DOLAP '06 Proceedings of the 9th ACM international workshop on Data warehousing and OLAP
Hierarchies in a multidimensional model: from conceptual modeling to logical representation
Data & Knowledge Engineering - Special issue: WIDM 2004
A UML profile for multidimensional modeling in data warehouses
Data & Knowledge Engineering - Special issue: ER 2003
Advanced Data Warehouse Design: From Conventional to Spatial and Temporal Applications (Data-Centric Systems and Applications)
Solving summarizability problems in fact-dimension relationships for multidimensional models
Proceedings of the ACM 11th international workshop on Data warehousing and OLAP
A survey on summarizability issues in multidimensional modeling
Data & Knowledge Engineering
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The development of a data warehouse system is based on a conceptual multidimensional model, which provides a high level of abstraction in the accurate and expressive description of real-world situations. Once this model has been designed, the corresponding logical representation must be obtained as the basis of the implementation of the data warehouse according to one specific technology. However, there is a semantic gap between the dimension hierarchies modeled in a conceptual multidimensional model and its implementation. This gap particularly complicates a suitable treatment of summarizability issues, which may in turn lead to erroneous results from business intelligence tools. Therefore, it is crucial not only to capture adequate dimension hierarchies in the conceptual multidimensional model of the data warehouse, but also to correctly transform these multidimensional structures in a summarizability-compliant representation. A model-driven normalization process is therefore defined in this paper to address this summarizability-aware transformation of the dimension hierarchies in rich conceptual models.