Characterization of hierarchies and some operators in OLAP environment
Proceedings of the 2nd ACM international workshop on Data warehousing and OLAP
Decision Support Systems - Special issue on WITS '97
A foundation for capturing and querying complex multidimensional data
Information Systems - Data warehousing
An Object Oriented Multidimensional Data Model for OLAP
WAIM '00 Proceedings of the First International Conference on Web-Age Information Management
Extending Practical Pre-Aggregation in On-Line Analytical Processing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
What can Hierarchies do for Data Warehouses?
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Normal Forms for Multidimensional Databases
SSDBM '98 Proceedings of the 10th International Conference on Scientific and Statistical Database Management
Normalising OLAP cubes for controlling sparsity
Data & Knowledge Engineering
Multidimensional data modeling for location-based services
The VLDB Journal — The International Journal on Very Large Data Bases
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OLAP (On-Line Analytical Processing) systems are used to support decision-making processes by providing agile analytical operations on large amounts of data. Usually, the operations require dimensions to be onto and covering; however, in real-world applications, many dimensions fail to meet the requirements, which can be non-covering, non-onto or self-into. In this paper, we will mainly concern the transforming of non-covering dimensions; we first define four different types of non-covering dimensions, and then devise several algorithms to transform them into covering ones respectively. The algorithms are of low computational complexity and can provide full support for transforming various non-covering dimensions into covering ones correctly.