@inproceedings{aac76b5b3cec4e77a329543e152d8cf3,
title = "IBM PAIRS curated big data service for accelerated geospatial data analytics and discovery",
abstract = "IBM's Physical Analytics Integrated Data Repository and Services (PAIRS) is a geospatial Big Data service. PAIRS contains a massive amount of curated geospatial (or more precisely spatio-temporal) data from a large number of public and private data resources, and also supports user contributed data layers. PAIRS offers an easy-to-use platform for both rapid assembly and retrieval of geospatial datasets or performing complex analytics, lowering time-to-discovery significantly by reducing the data curation and management burden. In this paper, we review recent progress with PAIRS and showcase a few exemplary analytical applications which the authors are able to build with relative ease leveraging this technology.",
keywords = "big data analytics, data management systems, GIS, Hadoop \& HBase for geospatial data, machine learning",
author = "Siyuan Lu and Xiaoyan Shao and Marcus Freitag and Klein, \{Levente J.\} and Jason Renwick and Marianno, \{Fernando J.\} and Conrad Albrecht and Hamann, \{Hendrik F.\}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 4th IEEE International Conference on Big Data, Big Data 2016 ; Conference date: 05-12-2016 Through 08-12-2016",
year = "2016",
doi = "10.1109/BigData.2016.7840910",
language = "English",
series = "Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2672--2675",
editor = "Ronay Ak and George Karypis and Yinglong Xia and Hu, \{Xiaohua Tony\} and Yu, \{Philip S.\} and James Joshi and Lyle Ungar and Ling Liu and Aki-Hiro Sato and Toyotaro Suzumura and Sudarsan Rachuri and Rama Govindaraju and Weijia Xu",
booktitle = "Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016",
}