Paper Info
Title
Designing and Implementing Data Warehouse for Agricultural Big Data.
Abstract
In recent years, precision agriculture that uses modern information and communication technologies is becoming very popular. Raw and semi-processed agricultural data are usually collected through various sources, such as: Internet of Thing (IoT), sensors, satellites, weather stations, robots, farm equipment, farmers and agribusinesses, etc. Besides, agricultural datasets are very large, complex, unstructured, heterogeneous, non-standardized, and inconsistent. Hence, the agricultural data mining is considered as Big Data application in terms of volume, variety, velocity and veracity. It is a key foundation to establishing a crop intelligence platform, which will enable resource efficient agronomy decision making and recommendations. In this paper, we designed and implemented a continental level agricultural data warehouse by combining Hive, MongoDB and Cassandra. Our data warehouse capabilities: (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) distributed and cloud deployment. We also evaluate the performance of our data warehouse.
Year
DOI
Venue
2019
10.1007/978-3-030-23551-2_1
Lecture Notes in Computer Science
Keywords
Field
DocType
Data warehouse,Big Data,Precision agriculture
Data integration,Data warehouse,Agribusiness,Computer science,Precision agriculture,Agriculture,Information and Communications Technology,Robot,Big data,Database
Journal
Volume
ISSN
Citations 
11514
0302-9743
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Vuong M. Ngo185.59
Nhien-An Le-Khac222449.63
M. Tahar Kechadi332659.59