Implementation of Data Mining on a Secure Cloud Computing over a Web API using Supervised Machine Learning Algorithm

TL;DR

This paper presents a method for real-time data mining in secure cloud environments using supervised machine learning algorithms, specifically decision trees and Random Forest, via a web API, achieving high accuracy without direct data warehouse interaction.

Contribution

It introduces a novel approach combining decision tree and Random Forest algorithms over a RESTful API for secure, efficient, and real-time data mining in cloud computing environments.

Findings

Achieved 94% accuracy in data classification.

Successfully bypassed direct data warehouse interaction.

Implemented real-time data mining using cloud and web API integration.

Abstract

Ever since the era of internet had ushered in cloud computing, there had been increase in the demand for the unlimited data available through cloud computing for data analysis, pattern recognition and technology advancement. With this also bring the problem of scalability, efficiency and security threat. This research paper focuses on how data can be dynamically mine in real time for pattern detection in a secure cloud computing environment using combination of decision tree algorithm and Random Forest over a restful Application Programming Interface (API). We are able to successfully Implement data mining on cloud computing bypassing or avoiding direct interaction with data warehouse and without any terminal involve by using combination of IBM Cloud storage facility, Amazing Web Service, Application Programming Interface and Window service along with a decision tree and Random Forest algorithm for our classifier. We were able to successfully bypass direct connection with the data warehouse and cloud terminal with 94% accuracy in our model.