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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

Interactive Visualization of Big Data

verfasst von : Parke Godfrey, Jarek Gryz, Piotr Lasek, Nasim Razavi

Erschienen in: Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery

Verlag: Springer International Publishing

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Abstract

Data becomes too big to see. Yet visualization is a central way people understand data. We need to learn new ways to accommodate data visualization that scales up and out for large data to enable people to explore visually their data interactively in real-time as a means to understanding it. The five V’s of big data—value, volume, variety, velocity, and veracity—each highlights the challenges of this endeavor.
We present these challenges and a system, Skydive, that we are developing to meet them. Skydive presents an approach that tightly couples a database back-end with a visualization front-end for scaling up and out. We show how hierarchical aggregation can be used to drive this, and the powerful types of interactive visual presentations that can be supported. We are preparing for the day soon when visualization becomes the sixth V of big data.

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Nächstes Kapitel Big Data Management in the Cloud: Evolution or Crossroad?
Fußnoten
1
This includes U.S.A. with 319 million, Mexico with 122 million, and Canada with 35 million, as of 2013.
 
2
Though they are cognizant of the need, and are working toward addressing this.
 
3
We shall also show ways that categorical data as measures can be accommodated.
 
4
We use the same number of divisions—power of two—along each of the dimensions, without loss of generality. It is trivial to allow for different “aspect” ratios with different numbers of divisions for different dimensions, however.
 
5
For simplicity, we shall refer to strata \(s_0\), ..., \(s_l\), from the top to the bottom, respectively, forgoing the minus sign when understood in context.
 
6
At least not standard versions of these.
 
9
Or vice versa: the bins of the t-pyramid are then hierarchically aggregated by x,y. This is commutative.
 
10
Also called Morton order [22]. This is a one-dimensional, linear ordering for any multi-dimensional data.
 
11
“Bins” into which no base data aggregates—“empty bins”—are never created. These numbers in the Z-order are simply skipped over.
 
12
This is sometimes referred to as a linear quadtree (for 2-D) [10].
 
13
The dataset is over three dimensions—\(X\), \(Y\), and \(T\)—so assume \(B = 2^{3d}\) for some \(d\), without loss of generality.
 
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Metadaten
Titel
Interactive Visualization of Big Data
verfasst von
Parke Godfrey
Jarek Gryz
Piotr Lasek
Nasim Razavi
Copyright-Jahr
2016
Buch
Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery

Print ISBN: 978-3-319-34098-2

Electronic ISBN: 978-3-319-34099-9

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-34099-9_1

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