Global Positioning System Reference
In-Depth Information
CHAPTER 1
Preparing Array Analytics for
the Data Tsunami
Peter Baumann,
a,
* Jinsongdi Yu,
a,c
Dimitar Misev,
a
Kinga Lipskoch,
a
Alan Beccati,
a
Piero Campalani
a
and
Michael Owonibi
b
Introduction
Array data, commonly also known as “grid” or “raster” data, represent a
large part of today's operational data, including 1D timeseries, 2D remote
sensing imagery, 3D x/y/t image timeseries and x/y/z geophysical data,
4D x/y/z/t ocean data, as well as 5D grid meteo simulations, which are 3D
spatial cubes over two time axes—today's analysis and yesterday's one-day
forecast have the same validity time but different forecast times (Domenico
et al. 2006). Being one particular class of the general category of space/time
varying data it forms a good study fi eld for further scientifi c progress into
additional data access and analytics.
Management and analytics of scientifi c data are a central challenge
today, with the growth doubling every year (Szalay and Gray 2006). Sensors
are becoming more and more powerful and ubiquitous; resolution is
a
Jacobs University Bremen, Campus Ring 1, 28759 Bremen, Germany.
Emails: p.baumann@jacobs-university.de; j.yu@jacobs-university.de;
d.misev@jacobs-university.de; k.lipskoch@jacobs-university.de;
a.beccati@jacobs-university.de; p.campalani@jacobs-university.de
b
Institut für Informatik, Friedrich-Schiller-University, Humboldtstraße 11 07743 Jena
Germany.
Email: michael.owonibi@uni-jena.de
c
Fuzhou University, 523 Gongye Rd, Gulou, Fuzhou, Fujian, China.
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