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http://dx.doi.org/10.5762/KAIS.2021.22.3.527

A Self-Service Business Intelligence System for Recommending New Crops  

Kim, Sam-Keun (School of Computer Engineering & Applied Mathematics, Hankyong National University)
Kim, Kwang-Chae (School of Computer Engineering & Applied Mathematics, Hankyong National University)
Kim, Hyeon-Woo (School of Computer Engineering & Applied Mathematics, Hankyong National University)
Jeong, Woo-Jin (School of Computer Engineering & Applied Mathematics, Hankyong National University)
Ahn, Jae-Geun (School of Computer Engineering & Applied Mathematics, Hankyong National University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.22, no.3, 2021 , pp. 527-535 More about this Journal
Abstract
Traditional business intelligence (BI) systems have been used widely as tools for better decision-making on time. On the other hand, building a data warehouse (DW) for the efficient analysis of rapidly growing data is time-consuming and complex. In particular, the ETL (Extract, Transform, and Load) process required to build a data warehouse has become much more complex as the BI platform moves to a cloud environment. Various BI solutions based on the NoSQL database, such as MongoDB, have been proposed to overcome these ETL issues. Decision-makers want easy access to data without the help of IT departments or BI experts. Recently, self-service BI (SSBI) has emerged as a way to solve these BI issues. This paper proposes a self-service BI system with farming data using the MongoDB cloud as DW to support the selection of new crops by return-farmers. The proposed system includes functions to provide insights to decision-makers, including data visualization using MongoDB charts, reporting for advanced data search, and monitoring for real-time data analysis. Decision makers can access data directly in various ways and can analyze data in a self-service method using the functions of the proposed system.
Keywords
Business Intelligence; Self-Service Business Intelligence; Data Visualization; Self-reliant Analysis; Recommendation;
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