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A Framework for Exploring Algorithms for Big Data Mining
Objectives: To proposed and implement a framework that facilitates exploration of algorithms for big data mining. Methods/Analysis: To achieve objectives, a framework is built in order to realize algorithms for big data mining and even provide Mining as a Service in cloud. As the existing data mining techniques can not work for MapReduce programming in distributed environment, we proposed new and equivalent method for k-Anonymity that can leverage the parallel processing power. Single host Hadoop is used to demonstrate the proof of concept of the proposed framework. Findings: The framework has ability to mind big data. It has the centralized service that can be used to mine data of different users. However, as of now, the framework is realized with only one algorithm that is MapReduce version of k-Anonymity which is meant for privacy preserving data mining. The application is proved to be scalable in distributed environment. The framework has provision for supporting cloud users to outsource their data for mining big data with different algorithms of their choice. The results revealed that the proposed framework can provide mining services to cloud users and help them to save money by reusing the service instead of reinventing the wheel. Novelty/Improvement: In the proposed work a mining service for cloud is proposed which is a novel idea that has not been implemented so far. It can save money and time to enterprises in the real world.
Algorithms, Big Data, Big Data Mining, Mining Service.
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