Selectivity estimation by batch-query based histogram and parametric method

Authors:
Jizhou Luo;Xiaofang Zhou;Yu Zhang;Heng Tao Shen;Jianzhong Li
Affiliations:
Harbin Institute of Technology, China;University of Queensland, Australia;University of Queensland, Australia;University of Queensland, Australia;Harbin Institute of Technology, China
Venue:
ADC '07 Proceedings of the eighteenth conference on Australasian database - Volume 63
Year:
2007

Citing 15
Cited 3

Practical selectivity estimation through adaptive sampling

SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
An instant and accurate size estimation method for joins and selections in a retrieval-intensive environment

SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Adaptive selectivity estimation using query feedback

SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
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
Self-tuning histograms: building histograms without looking at data

SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
STHoles: a multidimensional workload-aware histogram

SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Applying the golden rule of sampling for query estimation

SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Dynamic multidimensional histograms

Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Selectivity Estimation for Spatial Joins

Proceedings of the 17th International Conference on Data Engineering
Optimal Histograms with Quality Guarantees

VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Combining Histograms and Parametric Curve Fitting for Feedback-Driven Query Result-size Estimation

VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Fast Incremental Maintenance of Approximate Histograms

VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
ISOMER: Consistent Histogram Construction Using Query Feedback

ICDE '06 Proceedings of the 22nd International Conference on Data Engineering

Efficient selectivity estimation by histogram construction based on subspace clustering

SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches

Foundations and Trends in Databases
Sensitivity of self-tuning histograms: query order affecting accuracy and robustness

SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management

Quantified Score

Hi-index	0.00

Visualization

Abstract

Histograms are used extensively for selectivity estimation and approximate query processing. Workload-aware dynamic histograms can self-tune itself based on query feedback without scanning or sampling the underlaying datasets in a systematic and comprehensive way. Dynamic histograms allocate more buckets not only for the areas with most skewed data distribution but also according to users' interest. However, it takes long time to 'warm-up' (i.e., a large number of queries need to be processed before the histogram can provide a satisfactory coverage and accuracy). Thus, it is less effective to adapt with workload pattern changes. In this paper, we propose a novel online query scheduling algorithm which can significantly reduce the warm-up time for dynamic histograms. A parametric method is proposed to remedy the problem of inaccurate query selectivity estimation for the areas with poor histogram coverage. Experimental results demonstrate a significant effectiveness and accuracy improvement of our approach.