论文标题
使用有监督的机器学习算法在Web API上实施数据挖掘的数据挖掘
Implementation of Data Mining on a Secure Cloud Computing over a Web API using Supervised Machine Learning Algorithm
论文作者
论文摘要
自从Internet时代迎来了云计算以来，通过云计算可用的数据分析，模式识别和技术进步的无限数据的需求增加了。因此，还带来了可扩展性，效率和安全威胁的问题。本研究论文重点介绍如何使用决策树算法和随机森林的组合在安全的云计算环境中实时动态地挖掘数据，以通过静止的应用程序编程界面（API）组合。我们能够成功地实现云计算上的数据挖掘绕过或避免与数据仓库的直接互动，并且通过使用IBM云存储设施的组合，出色的Web服务，应用程序编程接口和窗口服务以及决策树和随机的森林算法，而无需任何终端涉及。我们能够成功绕过与数据仓库和云终端的直接连接，在我们的模型中精度为94％。
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.
