Data Warehouse and Decision Support on Integrated Crop Big Data
- URL: http://arxiv.org/abs/2003.04470v2
- Date: Mon, 12 Apr 2021 08:45:11 GMT
- Title: Data Warehouse and Decision Support on Integrated Crop Big Data
- Authors: V.M. Ngo, N.A. Le-Khac, and M.T. Kechadi
- Abstract summary: We designed and implemented a continental level agricultural data warehouse (ADW)
ADW is characterised by its (1) flexible schema; (2) data integration from real agricultural multi datasets; (3) data science and business intelligent support; (4) high performance; (5) high storage; (6) security; (7) governance and monitoring; (8) consistency, availability and partition tolerant; (9) cloud compatibility.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In recent years, precision agriculture is becoming very popular. The
introduction of modern information and communication technologies for
collecting and processing Agricultural data revolutionise the agriculture
practises. This has started a while ago (early 20th century) and it is driven
by the low cost of collecting data about everything; from information on fields
such as seed, soil, fertiliser, pest, to weather data, drones and satellites
images. Specially, the agricultural data mining today is considered as Big Data
application in terms of volume, variety, velocity and veracity. Hence it leads
to challenges in processing vast amounts of complex and diverse information to
extract useful knowledge for the farmer, agronomist, and other businesses. It
is a key foundation to establishing a crop intelligence platform, which will
enable efficient resource management and high quality agronomy decision making
and recommendations. In this paper, we designed and implemented a continental
level agricultural data warehouse (ADW). ADW is characterised by its (1)
flexible schema; (2) data integration from real agricultural multi datasets;
(3) data science and business intelligent support; (4) high performance; (5)
high storage; (6) security; (7) governance and monitoring; (8) consistency,
availability and partition tolerant; (9) cloud compatibility. We also evaluate
the performance of ADW and present some complex queries to extract and return
necessary knowledge about crop management.
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