Paper Key : IRJ************024
Author: Ayushi Gautam
Date Published: 04 Nov 2024
Abstract
Coffee, the second largest soft commodity globally, can benefit from thorough analysis of both daily and historical market data to make better informed trading choices. Sophisticated ICT and data mining technologies have the potential to alter the operation of the trading market. The current systems face limitations like lack of complete data, inadequate documentation for storage, and the need for a scalable infrastructure for big data analytics, such as a data warehouse or data lake house. In order to tackle this problem, the paper showcases a coffee commodity trading big data warehouse design and implementation that can analyze crucial parameters to support decision-making. Initially, the system is able to autonomously gather data on coffee trading by collecting New York Arabica coffee futures prices from various global reports and financial data portals. Then, the ETL process is utilized to input data from coffee futures trading web scraping into the 3 layers data warehouse. In the end, the analytics system will identify and display specific important factors that impact coffee futures prices across various time frames and viewpoints. Therefore, we develop a model of a coffee trading data repository using the gathered information from January 2000 to October 2022 and present patterns in coffee futures prices using the acquired data for making well-informed decisions. The construction system can effectively handle and manage high amounts of transaction data. This document will serve as a useful reference and decision-making tool for coffee trading businesses and will help advance the creation of predictive models.
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