A Java Toolbox for Analysis of MassIve Data STreams using Probabilistic Graphical Models

Andres Masegosa; Ana Maria Martinez; Darío  Ramos-López; Helge Langseth; Thomas Dyhre Nielsen; Antonio Salmerón; Rafael Cabanas; Anders Læsø Madsen

A Java Toolbox for Analysis of MassIve Data STreams using Probabilistic Graphical Models

Andres Masegosa, Ana Maria Martinez, Darío Ramos-López, Helge Langseth, Thomas Dyhre Nielsen, Antonio Salmerón, Rafael Cabanas, Anders Læsø Madsen

Publikation: Konferencebidrag uden forlag/tidsskrift › Poster › Forskning › peer review

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Abstract

Description:
• Analysis of big data streams: A complete collection of algorithms for inference and learning of both static and dynamic Bayesian networks from streaming data. Existing software systems for PGMs only focus on stationary datasets.
• Distributed parallel algorithms: AMIDST provides parallel multi-core and distributed implementations of Bayesian parameter learning, using streaming variational Bayes and variational message passing.

Originalsprog	Engelsk
Publikationsdato	2016
Status	Udgivet - 2016
Begivenhed	European Data Forum 2016: EDF - Eindhoven, Holland Varighed: 29 jun. 2016 → 30 jun. 2016 http://2016.data-forum.eu

Konference

Konference	European Data Forum 2016
Land/Område	Holland
By	Eindhoven
Periode	29/06/2016 → 30/06/2016
Internetadresse	http://2016.data-forum.eu

Adgang til dokumentet

EDFposter

AUB Link

Søg efter materialet i Aalborg Universitetsbiblioteks søgemaskine

AMIDST: Analysis of MassIve Data STreams - AMIDST
Madsen, A. L., Rommerdahl Bock, A., Nielsen, T. D. & Martinez, A. M.
European Union’s Seventh Framework Programme for research, technological development and demonstration
01/01/2014 → 31/12/2016
Projekter: Projekt › Forskning

Citationsformater

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AU - Masegosa, Andres

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AU - Ramos-López, Darío

AU - Langseth, Helge

AU - Nielsen, Thomas Dyhre

AU - Salmerón, Antonio

AU - Cabanas, Rafael

AU - Madsen, Anders Læsø

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AB - Description:• Analysis of big data streams: A complete collection of algorithms for inference and learning of both static and dynamic Bayesian networks from streaming data. Existing software systems for PGMs only focus on stationary datasets.• Distributed parallel algorithms: AMIDST provides parallel multi-core and distributed implementations of Bayesian parameter learning, using streaming variational Bayes and variational message passing.

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A Java Toolbox for Analysis of MassIve Data STreams using Probabilistic Graphical Models

Abstract

Konference

Adgang til dokumentet

AUB Link

Fingeraftryk

Projekter

AMIDST: Analysis of MassIve Data STreams - AMIDST

Citationsformater