A data model for supporting on-line analytical processing
CIKM '96 Proceedings of the fifth international conference on Information and knowledge management
Deriving orthogonality to optimize the search for summary data
Information Systems
The entity-relationship model—toward a unified view of data
ACM Transactions on Database Systems (TODS) - Special issue: papers from the international conference on very large data bases: September 22–24, 1975, Framingham, MA
A relational model of data for large shared data banks
Communications of the ACM
A Logical Approach to Multidimensional Databases
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Summarizability in OLAP and Statistical Data Bases
SSDBM '97 Proceedings of the Ninth International Conference on Scientific and Statistical Database Management
Extending the E/R Model for the Multidimensional Paradigm
ER '98 Proceedings of the Workshops on Data Warehousing and Data Mining: Advances in Database Technologies
Conceptual Design of Data Warehouses from E/R Schema
HICSS '98 Proceedings of the Thirty-First Annual Hawaii International Conference on System Sciences-Volume 7 - Volume 7
Multidimensional Data Modeling for Complex Data
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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We will study how relational dependency information can be applied to OLAP cube design. We use dependency information to control sparsity, since functional dependencies between dimensions clearly increase sparsity. Our method helps the user in finding dimensions and hierarchies, identifying sparsity risks, and finally changing the design in order to get a more suitable result. Sparse raw data, a large amount of pre-calculated aggregations, and many dimensions may expand the need of the storage space so rapidly that the problem cannot be solved by increasing the capacity of the system. We give two methods to construct suitable OLAP cubes. In the synthesis method, attributes are divided into equivalence classes according to dependencies in which they participate. Each equivalence class may form a dimension. The decomposition method is applied when candidates for dimensions exist. We decompose dimensions based on conflicts, and construct new cubes for removed dimensions until no conflicts between dimensions exist.