Range queries in OLAP data cubes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Quasi-cubes: exploiting approximations in multidimensional databases
ACM SIGMOD Record
Wavelet-based histograms for selectivity estimation
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data cube approximation and histograms via wavelets
Proceedings of the seventh international conference on Information and knowledge management
Multi-dimensional selectivity estimation using compressed histogram information
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
EDBT '00 Proceedings of the 7th International Conference on Extending Database Technology: Advances in Database Technology
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Aqua: A Fast Decision Support Systems Using Approximate Query Answers
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Hierarchical Prefix Cubes for Range-Sum Queries
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Dynamic Maintenance of Wavelet-Based Histograms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Relative Prefix Sums: An Efficient Approach for Querying Dynamic OLAP Data Cubes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Condensed Cube: An Efficient Approach to Reducing Data Cube Size
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Semi-closed cube: an effective approach to trading off data cube size and query response time
Journal of Computer Science and Technology
Quotient cube: how to summarize the semantics of a data cube
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Hi-index | 0.00 |
In this research, we propose to use the discrete cosine transform to approximate the cumulative distributions of data cube cells' values. The cosine transform is known to have a good energy compaction property and thus can approximate data distribution functions easily with small number of coefficients. The derived estimator is accurate and easy to update. We perform experiments to compare its performance with a well-known technique - the (Haar) wavelet. The experimental results show that the cosine transform performs much better than the wavelet in estimation accuracy, speed, space efficiency, and update easiness.