Service-oriented Data Mining Architecture for Climate-Smart Agriculture

Ajwang Stephen Oloo

doi:doi:10.11648/j.ajdmkd.20200501.11

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Service-oriented Data Mining Architecture for Climate-Smart Agriculture

Ajwang Stephen Oloo

Published in American Journal of Data Mining and Knowledge Discovery (Volume 5, Issue 1)

Received: 16 January 2020 Accepted: 10 February 2020 Published: 19 February 2020

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Abstract

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.

Published in	American Journal of Data Mining and Knowledge Discovery (Volume 5, Issue 1)
DOI	10.11648/j.ajdmkd.20200501.11
Page(s)	1-10
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Service-oriented Architecture, Data Mining, Climate-smart Agriculture, Artificial Neural Network, Web Services

References

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APA Style

Ajwang Stephen Oloo. (2020). Service-oriented Data Mining Architecture for Climate-Smart Agriculture. American Journal of Data Mining and Knowledge Discovery, 5(1), 1-10. https://doi.org/10.11648/j.ajdmkd.20200501.11

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ACS Style

Ajwang Stephen Oloo. Service-oriented Data Mining Architecture for Climate-Smart Agriculture. Am. J. Data Min. Knowl. Discov. 2020, 5(1), 1-10. doi: 10.11648/j.ajdmkd.20200501.11

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AMA Style

Ajwang Stephen Oloo. Service-oriented Data Mining Architecture for Climate-Smart Agriculture. Am J Data Min Knowl Discov. 2020;5(1):1-10. doi: 10.11648/j.ajdmkd.20200501.11

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@article{10.11648/j.ajdmkd.20200501.11,
  author = {Ajwang Stephen Oloo},
  title = {Service-oriented Data Mining Architecture for Climate-Smart Agriculture},
  journal = {American Journal of Data Mining and Knowledge Discovery},
  volume = {5},
  number = {1},
  pages = {1-10},
  doi = {10.11648/j.ajdmkd.20200501.11},
  url = {https://doi.org/10.11648/j.ajdmkd.20200501.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20200501.11},
  abstract = {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.},
 year = {2020}
}

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TY  - JOUR
T1  - Service-oriented Data Mining Architecture for Climate-Smart Agriculture
AU  - Ajwang Stephen Oloo
Y1  - 2020/02/19
PY  - 2020
N1  - https://doi.org/10.11648/j.ajdmkd.20200501.11
DO  - 10.11648/j.ajdmkd.20200501.11
T2  - American Journal of Data Mining and Knowledge Discovery
JF  - American Journal of Data Mining and Knowledge Discovery
JO  - American Journal of Data Mining and Knowledge Discovery
SP  - 1
EP  - 10
PB  - Science Publishing Group
SN  - 2578-7837
UR  - https://doi.org/10.11648/j.ajdmkd.20200501.11
AB  - 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.
VL  - 5
IS  - 1
ER  -

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Author Information

Ajwang Stephen Oloo

Department of Informatics and Information Science, School of Information Communication and Media Studies, Rongo University, Rongo, Kenya

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APA Style

Ajwang Stephen Oloo. (2020). Service-oriented Data Mining Architecture for Climate-Smart Agriculture. American Journal of Data Mining and Knowledge Discovery, 5(1), 1-10. https://doi.org/10.11648/j.ajdmkd.20200501.11

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ACS Style

Ajwang Stephen Oloo. Service-oriented Data Mining Architecture for Climate-Smart Agriculture. Am. J. Data Min. Knowl. Discov. 2020, 5(1), 1-10. doi: 10.11648/j.ajdmkd.20200501.11

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AMA Style

Ajwang Stephen Oloo. Service-oriented Data Mining Architecture for Climate-Smart Agriculture. Am J Data Min Knowl Discov. 2020;5(1):1-10. doi: 10.11648/j.ajdmkd.20200501.11

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@article{10.11648/j.ajdmkd.20200501.11,
  author = {Ajwang Stephen Oloo},
  title = {Service-oriented Data Mining Architecture for Climate-Smart Agriculture},
  journal = {American Journal of Data Mining and Knowledge Discovery},
  volume = {5},
  number = {1},
  pages = {1-10},
  doi = {10.11648/j.ajdmkd.20200501.11},
  url = {https://doi.org/10.11648/j.ajdmkd.20200501.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20200501.11},
  abstract = {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.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Service-oriented Data Mining Architecture for Climate-Smart Agriculture
AU  - Ajwang Stephen Oloo
Y1  - 2020/02/19
PY  - 2020
N1  - https://doi.org/10.11648/j.ajdmkd.20200501.11
DO  - 10.11648/j.ajdmkd.20200501.11
T2  - American Journal of Data Mining and Knowledge Discovery
JF  - American Journal of Data Mining and Knowledge Discovery
JO  - American Journal of Data Mining and Knowledge Discovery
SP  - 1
EP  - 10
PB  - Science Publishing Group
SN  - 2578-7837
UR  - https://doi.org/10.11648/j.ajdmkd.20200501.11
AB  - 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.
VL  - 5
IS  - 1
ER  -

Copy | Download