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Publication Type : Journal Article
Publisher : 2017 International Conference on Intelligent Computing and Control
Source : 2017 International Conference on Intelligent Computing and Control (I2C2), IEEE (2018)
Keywords : Big data, data analytics, Discretization, ECLAT algorithm, Frequent item sets, Hadoop, Map-reduce, R
Campus : Mysuru
School : School of Arts and Sciences
Department : Computer Science
Year : 2018
Abstract : Business in today's world mainly depends on business analytics like market analysis, product analysis, and price prediction etc. Analyzing such a huge data is a herculean task. Almost all data which is recurring day to day can be collectively termed as Big Data. Processing and analyzing such a Big data requires a parallel processing platform like Hadoop. Hadoop mainly does a mapping function and reduce function which together helps in faster processing. Business analytics also requires a faster data processing and faster execution. Developing such a environment requires an effective Analytical platform. The research carried forward in the paper put forwards an integrated platform of Hadoop for data processing and R platform for data analytics. The platform has been tested with the help of an enterprise data of employees of a company and their accidental insurance claims. The data has been preprocessed initially with Discretization function in R and then converted to transactions and put in for a Map reduce with an Éclat algorithm to find out the frequent items occurring in the record. Thus the research in the paper portrays an efficient processing and analytic platform for business data.
Cite this Research Publication : Rakshith P., S., M., and Sushmitha P., “Enterprise data analytics and processing with an integrated hadoop and R platforms”, in 2017 International Conference on Intelligent Computing and Control (I2C2), 2018.