DocumentCode :
2457130
Title :
Effective Data Density Estimation in Ring-Based P2P Networks
Author :
Minqi Zhou ; Heng Tao Shen ; Xiaofang Zhou ; Weining Qian ; Aoying Zhou
Author_Institution :
Software Eng. Inst., East China Normal Univ., Shanghai, China
fYear :
2012
fDate :
1-5 April 2012
Firstpage :
594
Lastpage :
605
Abstract :
Estimating the global data distribution in Peer-to-Peer (P2P) networks is an important issue and has yet to be well addressed. It can benefit many P2P applications, such as load balancing analysis, query processing, and data mining. Inspired by the inversion method for random variate generation, in this paper we present a novel model named distribution-free data density estimation for dynamic ring-based P2P networks to achieve high estimation accuracy with low estimation cost regardless of distribution models of the underlying data. It generates random samples for any arbitrary distribution by sampling the global cumulative distribution function and is free from sampling bias. In P2P networks, the key idea for distribution-free estimation is to sample a small subset of peers for estimating the global data distribution over the data domain. Algorithms on computing and sampling the global cumulative distribution function based on which global data distribution is estimated are introduced with detailed theoretical analysis. Our extensive performance study confirms the effectiveness and efficiency of our methods in ring-based P2P networks.
Keywords :
data mining; peer-to-peer computing; query processing; resource allocation; data mining; distribution-free data density estimation; dynamic ring-based P2P networks; global cumulative distribution function; global data distribution estimation; load balancing analysis; peer-to-peer networks; query processing; random variate generation; ring-based P2P networks; Distribution functions; Estimation; Histograms; Indexes; Peer to peer computing; Probability density function; Probability distribution;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Engineering (ICDE), 2012 IEEE 28th International Conference on
Conference_Location :
Washington, DC
ISSN :
1063-6382
Print_ISBN :
978-1-4673-0042-1
Type :
conf
DOI :
10.1109/ICDE.2012.19
Filename :
6228117
Link To Document :
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