American Journal of Data Mining and Knowledge Discovery
Volume 5, Issue 1, June 2020, Pages: 1-10
Received: Jan. 16, 2020;
Accepted: Feb. 10, 2020;
Published: Feb. 19, 2020
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Ajwang Stephen Oloo, Department of Informatics and Information Science, School of Information Communication and Media Studies, Rongo University, Rongo, Kenya
The increasing volume of agricultural data and the availability of advanced technologies such as mobile platforms and connected devices have revolutionized the way data is captured, processed, stored and mined. The technologies have been applied in everyday life including agriculture, to enable creation of seamless systems that are intuitive and capable of providing real-time, affordable and accessible data to aid decision making. However, due to the inherent challenges of mobile platforms such as low-bandwidth networks, reduced storage space, limited battery power, slower processors and small screens to visualize the results, have hindered onboard data mining. Also, mobile devices have different platforms, which makes integration with server applications problematic. This paper, therefore, sought to solve these problems by proposing application of service-oriented architecture (SOA) based on web services, and artificial neural network (ANN) to facilitate mobile data mining of large agronomic and climate data, and prediction of yield and weather patterns. The architecture was proposed after a critical review of the available mobile data mining architecture. SOA was an ideal choice since it uses web services to improve inter-operability between clients and server applications independently from the different platforms they execute on hence providing data mining capabilities to mobile devices. The paper proposes a 7-layer architectural design premised on the concept advanced in the SO-M-Miner model. The components of the architecture included an SMS gateway, data client, mobile networks, web service, database and ODBC connector.
Ajwang Stephen Oloo,
Service-oriented Data Mining Architecture for Climate-Smart Agriculture, American Journal of Data Mining and Knowledge Discovery.
Vol. 5, No. 1,
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