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An accurate selectivity estimation method for window queries and an implementation thereof
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作者 Changxiu CHENG Jing YANG +2 位作者 Xiaomei SONG Shanli YANG Lijun WANG 《Geo-Spatial Information Science》 SCIE CSCD 2015年第2期81-89,共9页
Spatial selectivity estimation is crucial to choose the cheapest execution plan for a given query in a query optimizer.This article proposes an accurate spatial selectivity estimation method based on the cumulative de... Spatial selectivity estimation is crucial to choose the cheapest execution plan for a given query in a query optimizer.This article proposes an accurate spatial selectivity estimation method based on the cumulative density(CD)histograms,which can deal with any arbitrary spatial query window.In 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 histogram.For the interpolation of any corner points,we first identify the cells that can affect the value of point(x,y)in the CD histogram.These 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 accurate.The CD histograms and estimation method have been implemented in INGRES.Experiment results show that the method can accurately estimate the selectivity of arbitrary query windows and can help the optimizer choose a cheaper query plan. 展开更多
关键词 cumulative density(CD)histogram selectivity estimation window queries spatial database spatial query optimization
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Multi-stream join answering for mining significant cross-stream correlations
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作者 Robert GWADERA 《Frontiers of Computer Science》 SCIE EI CSCD 2012年第2期131-142,共12页
Sliding-window multi-stream join (SWMJ) is a fundamental operation for correlating information from dif- ferent streams. We provide a solution to the problem of as- sessing significance of the SWMJ result by focusin... Sliding-window multi-stream join (SWMJ) is a fundamental operation for correlating information from dif- ferent streams. We provide a solution to the problem of as- sessing significance of the SWMJ result by focusing on the relative frequency of windows satisfying a given equijoin predicate as the most important parameter of the SWMJ re- suit. In particular, we derive a formula for computing the expected relative frequency of windows satisfying a given equijoin predicate that can be. evaluated in quadratic time in the window size given a proposed probabilistic model of the multi-stream. In experiments conducted on a daily rain- fall data set we demonstrate the remarkable accuracy of our method, which confirms our theoretical analysis. 展开更多
关键词 probabilistic data streams stream summariza-tion stream sketch window aggregate queries
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