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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Dutta, R., Li, C., Smith, D., Das, A., Aryal, J. (2015). Big Data Architecture for Environmental Analytics. In: Denzer, R., Argent, R.M., Schimak, G., HĹ™ebĂÄŤek, J. (eds) Environmental Software Systems. Infrastructures, Services and Applications. ISESS 2015. IFIP Advances in Information and Communication Technology, vol 448. Springer, Cham. https://doi.org/10.1007/978-3-319-15994-2_59
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DOI: https://doi.org/10.1007/978-3-319-15994-2_59
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-15993-5
Online ISBN: 978-3-319-15994-2
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