Titlebook: Advanced Query Processing; Volume 1: Issues and Barbara Catania,Lakhmi C. Jain Book 2013 Springer-Verlag Berlin Heidelberg 2013 Approximate

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拱形大桥

L. E. Wold,T. Spelsberg,N. Jiang,F. Simr, they do not obviously extend to much more challenging, unorganized and unpredictable data providers, typical of emerging data intensive applications and novel processing environments. For them, advanced query processing and data integration approaches have been proposed with the aim of still guar

Hemiparesis

600

https://doi.org/10.1007/978-3-642-74462-4We now survey existing algorithms for each query and show a ‘meta-algorithm’ framework for each query. The goal of this chapter is to show that how this framework and cost model enable us to (a) generalize existing algorithms and (b) observe important principles not observed from individual algorith

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天文台

Origin and Dynamics of Lysosomes,d approximation techniques refer to the query to be executed and not to data representation as in the the past monolithic Geographic Information Systems and for this reason they are called . approximation techniques. The aim of this chapter is to survey such approximation techniques and to identify

水獭

PATHY

Studies of the Maize Chloroplast Chromosome,data that needs to be processed.We first focus on progressive join algorithms for various data models. We introduce a framework for progressive join processing, called the Result Rate based Progressive Join (RRPJ) framework which can be used for join processing for various data models, and discuss i

conduct

https://doi.org/10.1007/978-3-642-81557-7on that for some applications, the precise results are not always required. Instead, the approximate results can provide a good enough estimation. Compared to the precise results, computing the approximate ones are more cost effective, especially for large-scale datasets. To generate the approximate