DocumentCode :
249519
Title :
A Computational Framework for Integrating and Retrieving Biodiversity Data on a Large Scale
Author :
Da Silva, Daniel L. ; Correa, Pedro L. P. ; Stanzani, S.L. ; Filipak, Paulo Andre ; Sheffer Correa, Andreiwid
Author_Institution :
Escola Politec. da Univ. de Sao Paulo, Sáo Paulo, Brazil
fYear :
2014
fDate :
June 27 2014-July 2 2014
Firstpage :
786
Lastpage :
787
Abstract :
The digitization and integration of biodiversity data are essential for supporting environmental conservation and sustainable use of natural resources. Nowadays an increasing amount of data are made available by regional, national and global initiatives, but the efficient use of data still a challenge. New techniques are needed to enable efficient manage and the use of these various types of biotic and abiotic data to generate useful knowledge for decision-making processes. We present a work in progress research that proposes a computational framework to manage biodiversity data and to enable an efficient information retrieval process.
Keywords :
data integration; environmental science computing; information retrieval; natural resources; sustainable development; abiotic data management; abiotic data use; biodiversity data digitization; biodiversity data integration; biodiversity data management; biotic data management; biotic data use; computational framework; decision-making process; environmental conservation; global initiative; information retrieval process; knowledge generation; large-scale biodiversity data integration; large-scale biodiversity data retrieval; national initiative; regional initiative; sustainable natural resource usage; Biodiversity; Bioinformatics; Computational efficiency; Computer architecture; Databases; Informatics; Standards; Architecture; Big Data; Biodiversity; Information Retrieval; Integration; NoSQL;
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.2014.123
Filename :
6906867
Link To Document :
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