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Service-oriented Data Mining Architecture for Climate-Smart Agriculture

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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[3] Erl, T. (2005). Service-Oriented Architecture (SOA): Concepts, technology, and design. NJ: Prentice Hall.
[4] Cardoso J, Sheth A (2005) Introduction to semantic web services and web process composition. Semantic Web Services and Web Process Composition: 1–13.
[5] S. Ali, O. F. Rana and I. J. Taylor, (2005). "Web services composition for distributed data mining," International Conference on Parallel Processing Workshops (ICPPW'05), Oslo, Norway, 2005, pp. 11-18. doi: 10.1109/ICPPW.2005.87.
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[15] Talia, D., & Trunfio, P. (2007, October). How distributed data mining tasks can thrive as services on grids. Paper presented at National Science Foundation Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation (NGDM'07), Baltimore, USA.
[16] Devika, M., Shelke, S. B., Tina B. M., Pratik, N. G., Manowar, D. J., & Dubey, S. S. (2014). Data Store and Multi-Keyword Search on Encrypted Cloud Data. International Journal of Computer Science and Mobile Computing, 3 (4), 1227-1232.
[17] Kargupta, H., Bhargava, R., Liu, K., Powers, M., Blair P., Bushra, S., & Dull, J. (2003). VEDAS: A mobile and distributed data stream mining system for real time vehicle monitoring. Proceeding of SIAM Data Mining Conference, 300.
[18] Derya (2011). New fundamental technologies in data mining. In Kimito Funatsu (Eds), ISBN 978-953-307-547-1, Published: January 21, 2011 under CC BY-NC-SA 3.0 license.
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Cite This Article
  • 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
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    PB  - Science Publishing Group
    SN  - 2578-7837
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    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
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Author Information
  • Department of Informatics and Information Science, School of Information Communication and Media Studies, Rongo University, Rongo, Kenya

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