NDLI: Large interactive visualization of density functions on big data infrastructure

Content Provider	IEEE Xplore Digital Library
Author	Perrot, A. Bourqui, R. Hanusse, N. Lalanne, F. Auber, D.
Copyright Year	2015
Description	Author affiliation: LaBRI, Univ. Bordeaux, Bordeaux, France (Perrot, A.; Bourqui, R.; Hanusse, N.; Lalanne, F.; Auber, D.)
Abstract	Point set visualization is required in lots of visualization techniques. Scatter plots as well as geographic heat-maps are straightforward examples. Data analysts are now well trained to use such visualization techniques. The availability of larger and larger datasets raises the need to make these techniques scale as fast as the data grows. The Big Data Infrastructure offers the possibility to scale horizontally. Designing point set visualization methods that fit into that new paradigm is thus a crucial challenge. In this paper, we present a complete architecture which fully fits into the Big Data paradigm and so enables interactive visualization of heatmaps at ultra-scale. A new distributed algorithm for multi-scale aggregation of point set is given and an adaptive GPU based method for kernel density estimation is proposed. A complete prototype working with Hadoop, HBase, Spark and WebGL has been implemented. We give a benchmark of our solution on a dataset having more than 2 billion points.
Starting Page	99
Ending Page	106
File Size	8252834
Page Count	8
File Format	PDF
ISBN	9781467385176
DOI	10.1109/LDAV.2015.7348077
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Heating Data visualization Estimation Distributed databases Human-centered computing-Information visualization Human-centered computing-Heat maps Density functional theory Big data Kernel
Content Type	Text
Resource Type	Article

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