A Framework for Uncertainty-Aware Visual Analytics in Big Data

Article


Karami, A. 2015. A Framework for Uncertainty-Aware Visual Analytics in Big Data. CEUR Workshop Proceedings. 1510, pp. 146-155.
AuthorsKarami, A.
Abstract

Visual analytics has become an important tool for gaining insight on big data. Numerous statistical tools have been integrated with visualization to help analysts understand big data better and faster. However, data is inherently uncertain, due to sampling error, noise, latency, approximate measurement or unreliable sources. It is very important and vital to quantify and visualize uncertainties for analysts to improve the results of decision making process and gain valuable insights during analytic process on big data. In this paper, we propose a new framework to support uncertainty in the visual analytics process through a fuzzy self-organizing map algorithm running in MapReduce framework for parallel computations on massive amounts of data. This framework uses an interactive data mining module, uncertainty modeling and knowledge representation that supports insertion of the user’s experience and knowledge for uncertainty modeling and visualization in the big data.

JournalCEUR Workshop Proceedings
Journal citation1510, pp. 146-155
ISSN1613-0073
Year2015
PublisherCEUR Workshop Proceedings
Publisher's version
License
CC BY
Web address (URL)http://ceur-ws.org/Vol-1510/
Publication dates
Print12 Nov 2015
Publication process dates
Deposited13 Mar 2017
FunderNational Commission for Scientific and Technological Research (CONICYT) of Chile
National Commission for Scientific and Technological Research (CONICYT) of Chile
National Commission for Scientific and Technological Research
National Commission for Scientific and Technological Research
Copyright information© 2015 the author.
Additional information

This is an article which published in Proceedings of the 3rd International Workshop on Artificial Intelligence and Cognition (AIC), 28th-29th Sept., Turin, Italy

EditorsLieto, Antonio, Battaglino, Cristina, Radicioni, Daniele P. and Sanguinetti, Manuela
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https://repository.uel.ac.uk/item/853yw

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