DocumentCode :
249534
Title :
Scientific Data Infrastructure for Sustainability Science Mobile Applications
Author :
Herbert, Katherine G. ; Hill, Emily ; Fails, Jerry Alan ; Ajala, Joseph O. ; Boniface, Richard T. ; Cushman, Paul W.
Author_Institution :
Dept. of Comput. Sci., Montclair State Univ., Montclair, NJ, USA
fYear :
2014
fDate :
June 27 2014-July 2 2014
Firstpage :
804
Lastpage :
805
Abstract :
With the recent ecological and volatile climate issues, numerous concerns have arisen which have led to the rapid growth of sustainability sciences. In sustainability studies and related areas such as crisis data management, multiple communities need to interact and contribute data, and then have this data modeled for them in an effective manner. The National Science Foundation Advisory Committee for Environment Research and Education states that, "Because of the complex relationships among people, ecosystems, and the biosphere, human health and well-being are closely linked to the integrity of local, regional and global ecosystems." In our work, we look towards developing a mobile application platform that allows data integration for multiple information sources that allows the user flexibility to learn about and actively participate in understanding and helping their environment. In this paper, we address our current work with the scientific aspects of this data.
Keywords :
data integration; ecology; environmental science computing; mobile computing; National Science Foundation Advisory Committee for Environment Research and Education; data integration; multiple information sources; scientific data infrastructure; sustainability science mobile applications; Communities; Data collection; Data models; Decision making; Ecosystems; Meteorology; Mobile communication; Data Integration; Scientific Big Data; Sustainability Science;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Big Data (BigData Congress), 2014 IEEE International Congress on
Conference_Location :
Anchorage, AK
Print_ISBN :
978-1-4799-5056-0
Type :
conf
DOI :
10.1109/BigData.Congress.2014.130
Filename :
6906874
Link To Document :
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