Volume 5, Issue 1, June 2020, Page: 1-10
Service-oriented Data Mining Architecture for Climate-Smart Agriculture
Ajwang Stephen Oloo, Department of Informatics and Information Science, School of Information Communication and Media Studies, Rongo University, Rongo, Kenya
Received: Jan. 16, 2020;       Accepted: Feb. 10, 2020;       Published: Feb. 19, 2020
DOI: 10.11648/j.ajdmkd.20200501.11      View  388      Downloads  109
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.
Keywords
Service-oriented Architecture, Data Mining, Climate-smart Agriculture, Artificial Neural Network, Web Services
To cite this article
Ajwang Stephen Oloo, Service-oriented Data Mining Architecture for Climate-Smart Agriculture, American Journal of Data Mining and Knowledge Discovery. Vol. 5, No. 1, 2020, pp. 1-10. doi: 10.11648/j.ajdmkd.20200501.11
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
Niknejad N., Hussin A. R. C., Amiri I. S. (2019) Introduction of Service-Oriented Architecture (SOA) Adoption. In: The Impact of Service Oriented Architecture Adoption on Organizations. Springer Briefs in Electrical and Computer Engineering. Springer, Cham.
[2]
Talia, D., & Trunfio, P. (2012). Service-oriented distributed knowledge discovery. CRC: Chapman and Hall.
[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.
[6]
Newcomer, E. (2002). Understanding Web services: XML, WSDL, SOAP, and UDDI. Boston: Addison-Wesley.
[7]
Adacal, M., & Bener. A. B. (2006). Mobileweb services: A new agent based framework. IEEE Internet Computing, 10 (3): 5865.
[8]
Chu, H., You, C., & Teng, C. (2004). Challenges: wireless web services. Proceedings of International Conference on Parallel and Distributed Systems, (ICPADS 04), IEEE CS Press.
[9]
Zahreddine, W., & Mahmoud, Q. H. (2010). An Agent based Approach to Composite Mobile Web Services. Proceedings of the International Conference on Advanced Information Networking and Applications, IEEE CS Press.
[10]
M. Musolesi, "Big Mobile Data Mining: Good or Evil?," in IEEE Internet Computing, vol. 18, no. 1, pp. 78-81, Jan.-Feb. 2014. doi: 10.1109/MIC.2014.2.
[11]
Kagal, L., Korolev, V., Chen, H., Joshi, A. and Finin, T., 2001, April. Project centaurus: A framework for indoor services mobile services. In Proceedings of the 21st International Conference on Distributed Computing Systems.
[12]
Gaber M. M., Stahl F., Gomes J. B. (2014) Potential Applications of Pocket Data Mining. In: Pocket Data Mining. Studies in Big Data, vol 2. Springer, Cham.
[13]
Kargupta, H., Park, B., Pitties, S., Liu, L., Kushraj, D., & Sarkar, K. (2002). Mobimine: monitoring the stock marked from a PDA. ACM SIGKDD Explorations, 3 (2): 3746.
[14]
S. Kumari and S. K. Rath, “Performance comparison of SOAP and REST based Web Services for Enterprise Application Integration,” in International Conference on Advances in Computing, Communications and Informatics (ICACCI), Kochi, 2015, pp. 1656–1660.
[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.
[19]
Sanjay, S., Pushpinder, S. P., & Akhilesh A. W. (2011). Challenges for Mobile Wireless Devices for Next Generation in Pervasive Computing. International Journal of Soft Computing and Engineering, 1 (1), 6-13.
[20]
Nam, Min-Young. (2015). ERACES: Complexity Metrics Tool User Guide. https://github.com/cmusei/eraces/tree/master/scade-metrics/doc.
[21]
FAO, 2017. FAOSTAT. Food and Agriculture Organization of the United Nations, Rome, Italy.
[22]
Immaculate Maina, Andrew Newshamii and Michael Okotii (2013). Agriculture and Climate Change in Kenya: Climate Chaos, Policy Dilemmas. Future Agricultures.
[23]
CIAT (2016). CIAT Annual Report 2015-2016.
[24]
S. Ajwang. (2016). Thesis: An architecture for m-mining agricultural information.
[25]
Gowri T. M. and Reddy V. V. C. (2008). Load Forecasting by a Novel Technique using ANN. ARPN Journal of Engineering and Applied Sciences. 3 (2): 19-25.
Browse journals by subject