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101 0 $aeng
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181   $ctxt$2rdacontent
182   $cc$2rdamedia
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200 00$aProceedings of the 9th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data /$feditors, Varun Chandola, Ranga Raju Vatsavai, Ashwin Shashidharan
210  1$aNew York :$cAssociation for Computing Machinery,$d2020.
215   $a1 online resource (68 pages) $cillustrations
225 0 $aACM Conferences
311   $a1-4503-8162-6 
330   $aBig data is an important area of research for data researchers and scientists. Within the realm of big data, spatial and spatio-temporal data are among the fastest growing types of data. With advances in remote sensors, sensor networks, and the proliferation of location sensing devices in daily life activities and common business practices, the generation of disparate, dynamic, and geographically distributed spatiotemporal data has exploded in recent years. In addition, significant progress in ground, air and space-borne sensor technologies has led to an unprecedented access to earth science data for scientists from different disciplines, interested in studying the complementary nature of different parameters. Analyzing this data poses a massive challenge to researchers.
606   $aNeural networks (Computer science)
615  0$aNeural networks (Computer science)
676   $a006.32
702   $aChandola$b Varun
702   $aVatsavai$b Ranga Raju
702   $aShashidharan$b Ashwin
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906   $aBOOK
912   $a9910510468003321
996   $aProceedings of the 9th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data$92136001
997   $aUNINA