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Big data architecture for environmental analytics


Dutta, R and Li, C and Smith, D and Das, A and Aryal, J, Big data architecture for environmental analytics, Environmental Software Systems: Infrastructure, Services and Applications, 25-27 March 2015, Melbourne, Australia, pp. 578-588. ISBN 978-3-319-15993-5 (2015) [Refereed Conference Paper]

Copyright Statement

Copyright 2014 Springer

DOI: doi:10.1007/978-3-319-15994-2_59


This paper aims to develop big data based knowledge recommendation framework architecture for sustainable precision agricultural decision support system using Computational Intelligence (Machine Learning Analytics) and Semantic Web Technology (Ontological Knowledge Representation). Capturing domain knowledge about agricultural processes, understanding about soil, climatic condition based harvesting optimization and undocumented farmers' valuable experiences are essential requirements to develop a suitable system. Architecture to integrate data and knowledge from various heterogeneous data sources, combined with domain knowledge captured from the agricultural industry has been proposed. The proposed architecture suitability for heterogeneous big data integration has been examined for various environmental analytics based decision support case studies.

Item Details

Item Type:Refereed Conference Paper
Keywords:big data, architecture, machine learning, semantics
Research Division:Engineering
Research Group:Geomatic engineering
Research Field:Photogrammetry and remote sensing
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the earth sciences
UTAS Author:Das, A (Dr Aruneema Das)
UTAS Author:Aryal, J (Dr Jagannath Aryal)
ID Code:99363
Year Published:2015
Deposited By:Geography and Environmental Studies
Deposited On:2015-03-23
Last Modified:2017-10-24

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