On Linear-Spline Based Histograms
WAIM '02 Proceedings of the Third International Conference on Advances in Web-Age Information Management
Binary-Tree Histograms with Tree Indices
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
A multi-dimensional histogram for selectivity estimation and fast approximate query answering
CASCON '03 Proceedings of the 2003 conference of the Centre for Advanced Studies on Collaborative research
Fast range query estimation by N-level tree histograms
Data & Knowledge Engineering
The history of histograms (abridged)
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Histograms based on the minimum description length principle
The VLDB Journal — The International Journal on Very Large Data Bases
Enhancing histograms by tree-like bucket indices
The VLDB Journal — The International Journal on Very Large Data Bases
Enabling OLAP in mobile environments via intelligent data cube compression techniques
Journal of Intelligent Information Systems
Entropy-based histograms for selectivity estimation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Histograms are used to summarize the contents of relations for the estimation of query result sizes into a number of buckets. Several techniques (e.g., MaxDiff and V-Optimal) have been proposed in the past for determining bucket boundaries which provide better estimations. This paper proposes to use a 32-bit information (4-level tree index) for each bucket for storing approximated cumulative frequencies at 7 internal intervals of a bucket. Both theoretical analysis and experimental results show that the 4-level tree index provides the best frequency estimation inside a bucket. The index is later added to two well-known techniques for constructing histograms, MaxDiff and V-Optimal, thus obtaining high improvements in the frequency estimation over inter-bucket ranges w.r.t. the original methods.