Big Data Architecture for Environmental Analytics

Abstract : 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.
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Ritaban Dutta, Cecil Li, Daniel Smith, Aruneema Das, Jagannath Aryal. Big Data Architecture for Environmental Analytics. 11th International Symposium on Environmental Software Systems (ISESS), Mar 2015, Melbourne, Australia. pp.578-588, ⟨10.1007/978-3-319-15994-2_59⟩. ⟨hal-01328610⟩

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