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selectivity estimation for String Predicates Based on Modified Pruned Count-Suffix Tree

Chinese Journal of Electronics 2015年第1期24卷 76-82页

作者： LI Dong ZHANG Qixu LIANG Xiaochong GUAN Jida XU Yang School of Software Engineering South China University of Technology

The accuracy of predicates selectivity estimation is one of the important factors affecting query optimization performance. State-of-art selectivity estimation algorithms for string predicates based on Pruned countsuffix tree(PST) often suffer severe underestimating and overestimating problems, thus their average relative errors are not good. We analyze the main causes of the underestimating and overestimating prob-lems, propose a novel Restricted pruned count-suffix tree(RPST) and a new pruning strategy. Based on these, we present the EKVI algorithm and the EMO algorithm which are extended from the KVI algorithm and the MO algorithm respectively. The experiments compare the EKVI algorithm and the EMO algorithm with the traditional KVI algorithm and the MO algorithm, and the results show that the average relative errors of our selectivity estimation algorithms are significantly better than the traditional selectivity estimation algorithms. The EMO algorithm is the best among these algorithms from the overall view.

关键词： Query optimization selectivity estimation String predicate Oruned count-suffix tree.

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An accurate selectivity estimation method for window queries and an implementation thereof

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Geo-Spatial Information Science 2015年第2期18卷 81-89页

作者： Changxiu CHENG Jing YANG Xiaomei SONG Shanli YANG Lijun WANG Academy of Disaster Reduction and Emergency Management Beijing Normal UniversityBeijing 100875China State Key Laboratory of Resources and Environmental Information Systems Institute of Geographic Science and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China China Internet Network Information Center Beijing 100101China

Spatial selectivity estimation is crucial to choose the cheapest execution plan for a given query in a query *** article proposes an accurate spatial selectivity estimation method based on the cumulative density(CD)histograms,which can deal with any arbitrary spatial query *** this method,the selectivity can be estimated in original logic of the CD histogram,after the four corner values of a query window have been accurately interpolated on the continuous surface of the elevation *** the interpolation of any corner points,we first identify the cells that can affect the value of point(x,y)in the CD *** cells can be categorized into two classes:ones within the range from(0,0)to(x,y)and the other overlapping the range from(0,0)to(x,y).The values of the former class can be used directly,whereas we revise the values of any cells falling in the latter class by the number of vertices in the corresponding cell and the area ratio covered by the range from(0,0)to(x,y).This revision makes the estimation method more *** CD histograms and estimation method have been implemented in *** results show that the method can accurately estimate the selectivity of arbitrary query windows and can help the optimizer choose a cheaper query plan.

关键词： cumulative density(CD)histogram selectivity estimation window queries spatial database spatial query optimization

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The Generic Annular Bucket Histogram for Estimating the selectivity of Spatial Selection and Spatial Join

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Geo-Spatial Information Science 2011年第4期14卷 262-273页

作者： Changxiu Cheng Chenghu Zhou Rongguo Chen State Key Laboratory of Resources and Environmental Information Systems Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

selectivity estimation is crucial for query optimizers choosing an optimal spatial execution plan in a spatial database management *** paper presents an Annular Bucket spatial histogram(AB histogram)that can estimate the selectivity in finer spatial selection and spatial join operations even when the spatial query has more operators or more *** AB histogram is represented as a set of bucket-range,bucket-count value *** bucket-range often covers an annular region like a sin-gle-cell-sized photo *** bucket-count is the number of objects whose Minimum Bounding Rectangles(MBRs)fall between outer rectangle and inner rectangle of the *** that all MBRs in each a bucket distribute evenly,for every buck-et,we can obtain serial probabilities that satisfy a certain spatial selection or join conditions from the operations' semantics and the spatial relations between every bucket-range and query ***,according to some probability theories,spatial selection or join selectivity can be estimated by the every bucket-count and its *** paper also shows a way to generate an updated AB histogram from an original AB histogram and those *** tests show that the AB histogram not only supports the selectivity estimation of spatial selection or spatial join with "disjoint","intersect","within","contains",and "overlap" operators but also provides an approach to generate a reliable updated histogram whose spatial distribution is close to the distribution of ac-tual query result.

关键词： selectivity estimation AB histogram annular bucket spatial selection spatial join

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基于优化规则的图查询选择性估计方法

基于优化规则的图查询选择性估计方法

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第二十二届中国数据库学术会议

作者：钱乾冯建华廖雨果李国良塔娜清华大学计算机科学与技术系

1前言目前,图结构越来越多地应用于描述复杂的无固定模式的数据,比如蛋白质、化学分子式和XML文档等。在众多与图结构数据相关的应用中,如何高效地对图结构数据进行查询成为一个至关重要且极富挑战性的问题。为了给复杂的图结构数据查... 详细信息

1前言目前,图结构越来越多地应用于描述复杂的无固定模式的数据,比如蛋白质、化学分子式和XML文档等。在众多与图结构数据相关的应用中,如何高效地对图结构数据进行查询成为一个至关重要且极富挑战性的问题。为了给复杂的图结构数据查询找到最有效的查询执行计划,对图查询的选择性估

关键词： Graph-structured data Graph query selectivity estimation Optimized rule

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