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Forecasting the Demand of Agricultural Crops/Commodity Using Business Intelligence Framework

Forecasting the Demand of Agricultural Crops/Commodity Using Business Intelligence Framework

Satyadhyan Chickerur, Supreeth Sharma, Prashant M. Narayankar
Copyright: © 2018 |Pages: 15
ISBN13: 9781522522553|ISBN10: 1522522557|EISBN13: 9781522522560
DOI: 10.4018/978-1-5225-2255-3.ch073
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MLA

Chickerur, Satyadhyan, et al. "Forecasting the Demand of Agricultural Crops/Commodity Using Business Intelligence Framework." Encyclopedia of Information Science and Technology, Fourth Edition, edited by Mehdi Khosrow-Pour, D.B.A., IGI Global, 2018, pp. 847-861. https://doi.org/10.4018/978-1-5225-2255-3.ch073

APA

Chickerur, S., Sharma, S., & Narayankar, P. M. (2018). Forecasting the Demand of Agricultural Crops/Commodity Using Business Intelligence Framework. In M. Khosrow-Pour, D.B.A. (Ed.), Encyclopedia of Information Science and Technology, Fourth Edition (pp. 847-861). IGI Global. https://doi.org/10.4018/978-1-5225-2255-3.ch073

Chicago

Chickerur, Satyadhyan, Supreeth Sharma, and Prashant M. Narayankar. "Forecasting the Demand of Agricultural Crops/Commodity Using Business Intelligence Framework." In Encyclopedia of Information Science and Technology, Fourth Edition, edited by Mehdi Khosrow-Pour, D.B.A., 847-861. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-2255-3.ch073

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Abstract

Information technology now a days is playing a very important role in all the spheres of life, starting from healthcare to entertainment. The agricultural community is not far behind in utilizing information technology for increasing the efficiency and productivity of agriculture and allied activities. This paper proposes how the concepts of BI (Business Intelligence), BI tools, Data mining tools might be used for forecasting the agricultural demand of various crops reliably and more efficiently. The paper clearly elaborates how BI tools could be used during various stages of ETL (Extract, transform and load) and how cleansed, quality data could be used by data mining tool for forecasting. Experiments are carried out for forecasting the demands for various agricultural crops by using the previous year's demand and the results are encouraging. The experimental set up involved the open source tools like Pentaho's Kettle and Weka.

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