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Michał Kwiecień's CV
Michał Kwiecień's CV
Michał Kwiecień's CV. Created with the moderncv template.
Michał Kwiecień
On Problem Solving
On Problem Solving
Note on problem solving
61plus
Naveen Kommuri Resume
Naveen Kommuri Resume
Naveen Kommuri's Undergraduate Resume (Mechanical Engineering)
Naveen Kommuri
Plano de projeto
Plano de projeto
Plano do projeto Beabá 2.0
Paulo Sestini, Carlos Jedwab, Lucas Sepeda, Gabriel Pereira de Carvalho
Jayeon Yoo's Curriculum Vitae
Jayeon Yoo's Curriculum Vitae
Jayeon Yoo's CV. Created with the Modern CV template ("casual" style).
JayeonYoo
Mukhina Anna's Reéumé
Mukhina Anna's Reéumé
Mukhina Anna's Résumé. Created using the Medium Length CV template.
MUKHINA ANNA
Qianchu Liu's CV
Qianchu Liu's CV
Qianchu Liu's CV. Created with the Medium Length CV template
Qianchu Liu
An Approach Based on Bayesian Networks for Query Selectivity Estimation
An Approach Based on Bayesian Networks for Query Selectivity Estimation
The efficiency of a query execution plan depends on the accuracy of the selectivity estimates given to the query optimiser by the cost model. The cost model makes simplifying assumptions in order to produce said estimates in a timely manner. These assumptions lead to selectivity estimation errors that have dramatic effects on the quality of the resulting query execution plans. A convenient assumption that is ubiquitous among current cost models is to assume that attributes are independent with each other. However, it ignores potential correlations which can have a huge negative impact on the accuracy of the cost model. In this paper we attempt to relax the attribute value independence assumption without unreasonably deteriorating the accuracy of the cost model. We propose a novel approach based on a particular type of Bayesian networks called Chow-Liu trees to approximate the distribution of attribute values inside each relation of a database. Our results on the TPC-DS benchmark show that our method is an order of magnitude. more precise than other approaches whilst remaining reasonably efficient in terms of time and space.
Max Halford, Philippe Saint-Pierre, Franck Morvan
Minor Project
Minor Project
Shape feature extraction for image recognition with CNN using frequency domain
Palash Dahiphale