Swathi Prabhu - Academia.edu (original) (raw)

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Papers by Swathi Prabhu

Research paper thumbnail of High Performance Computation of Big Data: Performance Optimization Approach towards a Parallel Frequent Item Set Mining Algorithm for Transaction Data based on Hadoop MapReduce Framework

International Journal of Intelligent Systems and Applications

The Huge amount of Big Data is constantly arriving with the rap id development of business organi... more The Huge amount of Big Data is constantly arriving with the rap id development of business organizations and they are interested in ext racting knowledgeable info rmation fro m co llected data. Frequent item min ing of Big Data helps with business decision and to provide high quality service. The result of traditional frequent item set min ing algorith m on Big Data is not an effective way which leads to high co mputation time. An Apache Hadoop MapReduce is the most popular data intensive distributed computing framework for large scale data applications such as data mining. In this paper, the author identifies the factors affecting on the performance of frequent item mining algorith m based on Hadoop MapReduce technology and proposed an approach for optimizing the performance of large scale frequent item set mining. The Experiments result shows the potential of the proposed approach. Performance is significantly optimized for large scale data min ing in MapReduce technique. The author believes that it has a valuable contribution in the high performance co mputing of Big Data.

Research paper thumbnail of High Performance Computation of Big Data: Performance Optimization Approach towards a Parallel Frequent Item Set Mining Algorithm for Transaction Data based on Hadoop MapReduce Framework

International Journal of Intelligent Systems and Applications

The Huge amount of Big Data is constantly arriving with the rap id development of business organi... more The Huge amount of Big Data is constantly arriving with the rap id development of business organizations and they are interested in ext racting knowledgeable info rmation fro m co llected data. Frequent item min ing of Big Data helps with business decision and to provide high quality service. The result of traditional frequent item set min ing algorith m on Big Data is not an effective way which leads to high co mputation time. An Apache Hadoop MapReduce is the most popular data intensive distributed computing framework for large scale data applications such as data mining. In this paper, the author identifies the factors affecting on the performance of frequent item mining algorith m based on Hadoop MapReduce technology and proposed an approach for optimizing the performance of large scale frequent item set mining. The Experiments result shows the potential of the proposed approach. Performance is significantly optimized for large scale data min ing in MapReduce technique. The author believes that it has a valuable contribution in the high performance co mputing of Big Data.

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