Equi-depth multidimensional histograms
SIGMOD '88 Proceedings of the 1988 ACM SIGMOD international conference on Management of data
Practical selectivity estimation through adaptive sampling
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Error-constrained COUNT query evaluation in relational databases
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
Molecular docking using shape descriptors
Journal of Computational Chemistry
Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
Adaptive selectivity estimation using query feedback
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Query size estimation by adaptive sampling
Selected papers of the 9th annual ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Wavelet-based histograms for selectivity estimation
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
STHoles: a multidimensional workload-aware histogram
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Access path selection in a relational database management system
SIGMOD '79 Proceedings of the 1979 ACM SIGMOD international conference on Management of data
Accurate estimation of the number of tuples satisfying a condition
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
The TV-tree: an index structure for high-dimensional data
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
Feature-Based Retrieval of Similar Shapes
Proceedings of the Ninth International Conference on Data Engineering
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Selectivity Estimation Without the Attribute Value Independence Assumption
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
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Query optimization is an important functionality of modern database systems and often based on estimating the selectivity of queries before actually executing them. Well-known techniques for estimating the result set size of a query are sampling and histogram-based solutions. Sampling-based approaches heavily depend on the size of the drawn sample which causes a trade-off between the quality of the estimation and the time in which the estimation can be executed for large data sets. Histogram-based techniques eliminate this problem but are limited to low-dimensional data sets. They either assume that all attributes are independent which is rarely true for real-world data or else get very inefficient for high-dimensional data. In this paper we present the first multivariate parametric method for estimating the selectivity of window queries for large and high-dimensional data sets. We use clustering to compress the data by generating a precise model of the data using multivariate Gaussian distributions. Additionally, we show efficient techniques to evaluate a window query against the Gaussian distributions we generated. Our experimental evaluation shows that this approach is significantly more efficient for multidimensional data than all previous approaches.