Results 1 - 10 of 75

Improved Histograms for Selectivity Estimation of Range Predicates

by Viswanath Poosala, Peter J. Haas, Yannis E. Ioannidis, Eugene J. Shekita , 1996
"... Many commercial database systems maintain histograms to summarize the contents of relations and permit efficient estimation of query result sizes and access plan costs. Although several types of histograms have been proposed in the past, there has never been a systematic study of all histogram aspec ..."
Abstract - Cited by 276 (20 self) - Add to MetaCart
dimensions, and derive new histogram types by combining choices in effective ways. We also show how sampling techniques can be used to reduce the cost of histogram construction. Finally, we present results from an empirical study of the proposed histogram types used in selectivity estimation of range

Abstract Improved Histograms for Selectivity Estimation of Range Predicates

by Viswanath Poosala, Peter J. Haas
"... Many commercial database systems maintain histograms to sum-marize the contents of relations and permit efficient estimation of query result sizes and access plan costs. Although several types of histograms have been proposed in the past, there has never been a systematic study of all histogram aspe ..."
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dimensions, and derive new histogram types by combining choices in effective ways. We also show how sampling techniques can be usedto reduce the cost of histogram construction. Finally, we present results from an empirtcal study of the proposed histogram types used in selectivity estimation of range

Range Selectivity Estimation for Continuous Attributes

by Flip Korn, Theodore Johnson, H. V. Jagadish - In Proc. International Conference on Scientific and Statistical Database Management , 1999
"... Many commercial database systems maintain histograms to efficiently estimate query selectivities as part of query optimization. Most work on histogram design is implicitly geared towards discrete or categorical attribute value domains. In this paper, we consider approaches that are better suited for ..."
Abstract - Cited by 22 (0 self) - Add to MetaCart
for the continuous valued attributes commonly found in scientific and statistical databases. We propose two methods based on spline functions for estimating the selectivity of range queries over univariate and multivariate data. These methods are more accurate than histograms. As the results from our experiments

Fast Algorithms For Hierarchical Range Histogram Construction

by Sudipto Guha, et al. , 2002
"... Data Warehousing and OLAP applications typically view data as having multiple logical dimensions (e.g., product, location) with natural hierarchies de ned on each dimension. OLAP queries usually involve hierarchical selections on some of the dimensions, and often aggregate measure attributes (e.g., ..."
Abstract - Cited by 20 (4 self) - Add to MetaCart
.g., sales, volume). Accurately estimating the distribution of measure attributes, under hierarchical selections, is important in a variety of scenarios, including approximate query evaluation and cost-based optimization of queries. In this paper, we propose fast (near linear time) algorithms for the problem

Fast Algorithms For Hierarchical Range Histogram Construction

by Sudipto Guhaupenn
"... ABSTRACT Data Warehousing and OLAP applications typically view data as having multiple logical dimensions (e.g., product, location) with natural hierarchies defined on each dimension. OLAP queries usually involve hierarchical selections on some of the dimensions, and often aggregate measure attribut ..."
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attributes (e.g., sales, volume). Accurately estimating the distribution of measure attributes, under hierarchical selections, is important in a variety of scenarios, including approximate query evaluation and cost-based optimization of queries.

Exploring Spatial Datasets with Histograms

by Chengyu Sun, Divyakant Agrawal, Amr El Abbadi - Proc. of ICDE , 2001
"... As online spatial datasets grow both in number and sophistication, it becomes increasingly difficult for users to decide whether a dataset is suitable for their tasks, especially when they do not have prior knowledge of the dataset. In this paper, we propose browsing as an effective and efficient wa ..."
Abstract - Cited by 18 (0 self) - Add to MetaCart
aggregation and selectivity estimation, spatial dataset browsing poses some unique challenges. In this paper, we identify a set of spatial relations that need to be supported in browsing applications, namely, the contains, contained and the overlap relations.

Averaged Shifted Histogram

by Univariate Data For
"... > k , precisely which histogram with bin width h should be selected? Clearly, there are exactly m such shifted histograms , with explicit bin intervals ranging from [t k\Gammam+1 ; t k+1 ) to [t k ; t k+m ). The ordinary averaged shifted histogram (ASH) estimates the density at x 2 B k as the ari ..."
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> k , precisely which histogram with bin width h should be selected? Clearly, there are exactly m such shifted histograms , with explicit bin intervals ranging from [t k\Gammam+1 ; t k+1 ) to [t k ; t k+m ). The ordinary averaged shifted histogram (ASH) estimates the density at x 2 B k

A Comparison of Selectivity Estimators for Range Queries on Metric Attributes

by Bjdrn Blohsfeld, Dieter Korus, Bernhard Seeger - In Proceedings of the ACM SIGMOD Conference , 1999
"... In this paper, we present a comparison of nonparametric esti-mation methods for computing approximations of the selec-tivities of queries, in particular range queries. In contrast to previous studies, the focus of our comparison is on metric attributes with large domains which occur for example in s ..."
Abstract - Cited by 27 (1 self) - Add to MetaCart
In this paper, we present a comparison of nonparametric esti-mation methods for computing approximations of the selec-tivities of queries, in particular range queries. In contrast to previous studies, the focus of our comparison is on metric attributes with large domains which occur for example

Constructing Irregular Histograms by Penalized Likelihood

by Yves Rozenholca, Thoralf Mildenberger, Ursula Gatherb
"... We propose a fully automatic procedure for the construction of irregular histograms. For a given number of bins, the maximum likelihood histogram is known to be the result of a dynamic programming algorithm. To choose the number of bins, we propose two different penalties motivated by recent work in ..."
Abstract - Cited by 2 (2 self) - Add to MetaCart
in model selection by Castellan [6] and Massart [26]. We give a complete description of the algorithm and a proper tuning of the penalties. Finally, we compare our procedure to other existing proposals for a wide range of different densities and sample sizes. Key words: irregular histogram, density

Selectivity estimation for spatial joins

by Ning An, Zhen-yu Yang, Anand Sivasubramaniam - IEEE ICDE , 2001
"... Spatial Joins are important and time consuming operations in spatial database management systems. It is crucial to be able to accurately estimate the performance of these operations so that one can derive efficient query execution plans, and even develop/refine data structures to improve their perfo ..."
Abstract - Cited by 14 (0 self) - Add to MetaCart
their performance. While estimation techniques for analyzing the performance of other operations, such as range queries, on spatial data has come under scrutiny, the problem of estimating selectivity for spatial joins has been little explored. The limited forays into this area have used parametric techniques, which
Results 1 - 10 of 75