A universal-scheme approach to statistical databases containing homogeneous summary tables
ACM Transactions on Database Systems (TODS)
Balancing histogram optimality and practicality for query result size estimation
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Improved histograms for selectivity estimation of range predicates
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Wavelet-based histograms for selectivity estimation
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Implications of certain assumptions in database performance evauation
ACM Transactions on Database Systems (TODS)
Access path selection in a relational database management system
SIGMOD '79 Proceedings of the 1979 ACM SIGMOD international conference on Management of data
Estimating Range Queries Using Aggregate Data with Integrity Constraints: A Probabilistic Approach
ICDT '01 Proceedings of the 8th International Conference on Database Theory
Recovering Information from Summary Data
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Improving Temporal Joins Using Histograms
DEXA '00 Proceedings of the 11th International Conference on Database and Expert Systems Applications
Improving Range Query Estimation on Histograms
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Fast range query estimation by N-level tree histograms
Data & Knowledge Engineering
H-IQTS: a semantics-aware histogram for compressing categorical OLAP data
IDEAS '08 Proceedings of the 2008 international symposium on Database engineering & applications
Enabling OLAP in mobile environments via intelligent data cube compression techniques
Journal of Intelligent Information Systems
Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches
Foundations and Trends in Databases
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In many application contexts, like statistical databases, transaction recording systems, scientific databases, query optimizers, OLAP, and so on, data are summarized as histograms of aggregate values. When the task of reconstructing range queries on original data from aggregate data is performed, a certain estimation error cannot be avoided, due to the loss of information in compressing data. Error size strongly depends both on how histograms partition data domains and on how estimation inside each bucket is done. We propose a new type of histogram, based on an unbalanced binary-tree partition, suitable for providing quick answers to hierarchical range queries, and we use adaptive tree-indexing for better approximating frequencies inside buckets. As the results from our experiments demonstrate, our histogram behaves considerably better than state-of-the-art histograms, showing smaller errors in all considered data sets at the same storage space.