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SUMMARY:Big Data Meets Geo-Computation: Combining Research Reproducibility
  and Processing Efficiency in High-performance Computing - Giuseppe Amatul
 li (Yale University)
DTSTART:20170908T140000Z
DTEND:20170908T150000Z
UID:TALK79341@talks.cam.ac.uk
CONTACT:Longzhu Shen
DESCRIPTION:In recent years there has been an explosion of geo-datasets de
 rived from\nan increasing number of remote sensors\, field instruments\, s
 ensor\nnetworks\, and other GPS-equipped “smart” devices. “Big Data
 ” processing\nrequires flexible tools that combine efficient processing\
 , either on\nyour local pc or on remote servers (e.g\, clusters - HPCs). H
 owever\,\nleveraging these new data streams requires new tools and increas
 ingly\ncomplex workflows often involving multiple software and/or programm
 ing\nlanguages. This also the case for GIS and Remote Sensing analysis whe
 re\nstatistical/mathematical algorithms are implemented in complex\ngeospa
 tial workflows. I will show few examples of environmental\napplications wh
 ere I combine different open-source geo-libraries for a\nmassive computati
 on at Yale Center for Research Computing using High\nPerformance Computing
  platform.
LOCATION:Rayleigh room\, Maxwell Centre\, JJ Thomson Ave\, Cambridge CB3 0
 HE
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