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Artificial Intelligence
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AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
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Proceedings of the 2nd ACM international workshop on Data warehousing and OLAP
Proceedings of the 2nd ACM international workshop on Data warehousing and OLAP
ACM Computing Surveys (CSUR)
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EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
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EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
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ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Multidimensional databases
A user-driven data warehouse evolution approach for concurrent personalized analysis needs
Integrated Computer-Aided Engineering
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Proceedings of the 2006 conference on Leading the Web in Concurrent Engineering: Next Generation Concurrent Engineering
Context-aware generalization for cube measures
DOLAP '10 Proceedings of the ACM 13th international workshop on Data warehousing and OLAP
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BNCOD'06 Proceedings of the 23rd British National Conference on Databases, conference on Flexible and Efficient Information Handling
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Enhancing multidimensional database models with aggregation hierarchies allows viewing data at different levels of aggregation. Usually, hierarchy instances are represented by means of so-called rollup functions. Rollup between adjacent levels in the hierarchy are given extensionally, while rollups between connected non-adjacent levels are obtained by means of function composition. In many real-life cases, this model cannot capture accurately the meaning of common situations, particularly when exceptions arise. Exceptions may appear due to corporate policies, unreliable data or uncertainty, and their presence may turn the notion of rollup composition unsuitable for representing real relationships in the aggregation hierarchies. In this paper we present a language allowing augmenting traditional extensional rollup functions with intensional knowledge. We denote this language IRAH (Intensional Redefinition for Aggregation Hierarchies). Programs in IRAH consist of intensional rules, which can be regarded as patterns for: (a) overriding natural composition between rollup functions on adjacent levels in the concept hierarchy, (b) canceling the effect of rollup functions for specific values. Our proposal is presented as a stratified default theory. We show that a unique model for the underlying theory always exists, and can be computed in a bottom-up fashion. Finally, we present an algorithm that computes the revised dimension in polynomial time, although under more realistic assumptions, complexity becomes linear on the number of paths in the hierarchy of the dimension instance.