DocumentCode :
263002
Title :
Distributed modelling of big dynamic data with generalized linear models
Author :
Dedecius, Kamil ; Seckarova, Vladimira
Author_Institution :
Inst. of Inf. Theor. & Autom., Prague, Czech Republic
fYear :
2014
fDate :
7-10 July 2014
Firstpage :
1
Lastpage :
8
Abstract :
The big data, characterized by high volume, velocity and variety, often arise in a dynamic way, requiring fast online processing. This contribution proposes a new information-theoretic method for parallel dynamic statistical modelling of such data with a network of (potentially cooperating) processing units and an optional fusion center. The concept strongly exploits the principles of the Bayesian information processing, allowing its abstract formulation for arbitrary distributions. As a particular case, we specialize to the popular exponential family posterior distributions, arising either directly or indirectly from modelling with generalized linear models. Still, the applicability is considerably wider.
Keywords :
Bayes methods; Big Data; statistical distributions; Bayesian information processing; arbitrary distributions; big dynamic data; distributed modelling; exponential family posterior distributions; generalized linear models; online processing; parallel dynamic statistical modelling; Approximation methods; Bayes methods; Data models; Distributed databases; Estimation; Mathematical model;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Fusion (FUSION), 2014 17th International Conference on
Conference_Location :
Salamanca
Type :
conf
Filename :
6916110
Link To Document :
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