Selectivity estimators for multidimensional range queries over real attributes

Gunopulos, Dimitrios; Kollios, George; Tsotras, Vassilis J.; Domeniconi, Carlotta

doi:10.1007/s00778-003-0090-4

Cited by 103 publications

(125 citation statements)

References 23 publications

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“…The problem with this is the heterogeneous and string-oriented nature of RDF. The number of dimensions is unknown and much higher than the numbers typically applied on multi-dimensional histograms (Gunopulos et al [2005] evaluated GENHIST for up to 10 dimensions).…”

Section: Discussionmentioning

confidence: 99%

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A Mobile Data Management Architecture for Interoperability of Resource and Context Data

Brodt

Schiller

Sathish

et al. 2011

2011 IEEE 12th International Conference on Mobile Data Management

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Section: Discussionmentioning

confidence: 99%

“…As the number of non-overlapping equal size buckets grows exponentially with increasing dimensions in the data, Gunopulos et al [2005] propose an approach named GENHIST (for GENeralized HISTograms). GENHIST uses overlapping variable-size buckets to address skew in high-dimensional data.…”

Section: Related Work Cardinality Estimation For Spatial Features In mentioning

confidence: 99%

A Mobile Data Management Architecture for Interoperability of Resource and Context Data

Brodt

Schiller

Sathish

et al. 2011

2011 IEEE 12th International Conference on Mobile Data Management

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“…Among the various techniques proposed for non-parametric density estimation [20], histogram estimation [34], kernel estimation [1,11] and nearest neighbor estimation [37] are the most popular. In this paper, we use kernel estimation, because it can estimate unknown data distributions effectively [28].…”

Section: Likelihood Computationmentioning

confidence: 99%

Top-k typicality queries and efficient query answering methods on large databases

Hua

Pei

et al. 2009

The VLDB Journal

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Finding typical instances is an effective approach to understand and analyze large data sets. In this paper, we apply the idea of typicality analysis from psychology and cognitive science to database query answering, and study the novel problem of answering top-k typicality queries. We model typicality in large data sets systematically. Three types This paper is a substantial extension of the conference version [32]. The research of Ming Hua and Jian Pei is supported in part by an NSERC Discovery grant and an NSERC Discovery Accelerator Supplements grant. The research of Xuemin Lin is supported in part by the Australian Research Council Discovery Grants DP0987557, DP0881035 and DP0666428 and a Google research award. All opinions, findings, conclusions and recommendations in this paper are those of the authors and do not necessarily reflect the views of the funding agencies. We are grateful to the anonymous reviewers and Dr. Peter Haas, the associate editor, for their constructive comments which help to improve the quality of the paper substantially. Particularly, we thank them for their advice in improving the mathematical exposition and their suggestions about some important related work. of top-k typicality queries are formulated. To answer questions like "Who are the top-k most typical NBA players?", the measure of simple typicality is developed. To answer questions like "Who are the top-k most typical guards distinguishing guards from other players?", the notion of discriminative typicality is proposed. Moreover, to answer questions like "Who are the best k typical guards in whole representing different types of guards?", the notion of representative typicality is used. Computing the exact answer to a top-k typicality query requires quadratic time which is often too costly for online query answering on large databases. We develop a series of approximation methods for various situations: (1) the randomized tournament algorithm has linear complexity though it does not provide a theoretical guarantee on the quality of the answers; (2) the direct local typicality approximation using VP-trees provides an approximation quality guarantee; (3) a local typicality tree data structure can be exploited to index a large set of objects. Then, typicality queries can be answered efficiently with quality guarantees by a tournament method based on a Local Typicality Tree. An extensive performance study using two real data sets and a series of synthetic data sets clearly shows that top-k typicality queries are meaningful and our methods are practical. M. Hua (B)·

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“…In addition, selectivity estimation has been shown to be useful in many other areas of database processing, e.g., top-k query processing, skyline query processing, load-balancing in parallel join query execution, and spatio-temporal query processing [3,6,21,22,26,27]. Motivated by such applications, there has been a great deal of work on the problem of selectivity estimation such as histograms [1,2,8,11,12,19,24,28], wavelet transformation [18,29], SVD [23], discrete cosine transform [17], and sampling [13]. Among these approaches, histograms have been shown to be one of the most popular and effective ways to obtain accurate estimates of selectivity for multi-dimensional queries [8].…”

Section: Introductionmentioning

confidence: 99%

Hierarchically organized skew-tolerant histograms for geographic data objects

Roh

Kim

Chung

et al. 2010

Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data

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Histograms have been widely used for fast estimation of query result sizes in query optimization. In this paper, we propose a new histogram method, called the Skew-Tolerant Histogram (STHistogram) for two or three dimensional geographic data objects that are used in many real-world applications in practice.The proposed method provides a significantly enhanced accuracy in a robust manner even for the data set that has a highly skewed distribution. Our method detects hotspots present in various parts of a data set and exploits them in organizing histogram buckets. For this purpose, we first define the concept of a hotspot, and provide an algorithm that efficiently extracts hotspots from the given data set. Then, we present our histogram construction method that utilizes hotspot information. We also describe how to estimate query result sizes by using the proposed histogram. We show through extensive performance experiments that the proposed method provides better performance than other existing methods.

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Selectivity estimators for multidimensional range queries over real attributes

Cited by 103 publications

References 23 publications

A Mobile Data Management Architecture for Interoperability of Resource and Context Data

A Mobile Data Management Architecture for Interoperability of Resource and Context Data

Top-k typicality queries and efficient query answering methods on large databases

Hierarchically organized skew-tolerant histograms for geographic data objects

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