Icics2017085 by ASDF Administartor

International Conference on Intelligent Computing and Systems

International Conference on Intelligent Computing and Systems 2017 [ICICS 2017]

ISBN Website Received Article ID

978-81-933235-5-7 icics.asia 10 – January – 2017 ICICS085

VOL eMail Accepted eAID

01 icics@asdf.res.in 28 - January – 2017 ICICS.2017.085

Analysis of Multiple Account Holders in Various Banks Using HADOOP Technology Abishek Rajagopal1, Mukundhan Lakshmanan2, Gowri Sankari A3, Poongodi Palaniswamy4 1,2,3,4 Department of Information Technology, Sri Krishna College of Technology, India Abstract: In this electronic age, there is an increasing account holders in all banks. Hadoop is an open source projects that gives us the framework to deal with big data. Big data analytics is the method of inspecting large data sets and analysing large volumes of data to uncover unknown correlations, customer preferences, hidden patterns and other business information. The analytical findings will be more effective for marketing, improved operational efficiency and analyse large amount of data. In this paper each user’s data is divided into multiple Tuples and stored in Database of different sets, and so it will be easy to track the account holders. We have sketched an Application to track multiple account holders who have accounts in different banks by the same user and who have withdrawn 2500 per day. For tracking the user’s details, we are using unique ID (pan card). Now a day, plan card number will be easy to track the user’s details and it will be very essential for banking sector.

ISBN Website Received Article ID

978-81-933235-5-7 icics.asia 10 – January – 2017 ICICS086

VOL eMail Accepted eAID

01 icics@asdf.res.in 28 - January – 2017 ICICS.2017.086

Incorporating Virtualization in Map Reduce for Efficient Data Movement in HADOOP 1

K Sindhanaiselvan1, P Elakkiya2 Assistant Professor, PG Scholar, Department of CSE, M Kumarasamy College of Engineering, India 2

Abstract: Map reduce is a parallel programming model for efficient process and generating information sets. It has been widely used in database systems for big data analytics. Big information analytics is a technique used for analysing giant volume of knowledge and provides higher cognitive process and conjointly support optimization processes primarily for the information collected from the social media websites. Map Reduce needs a shuffling section to globally exchange the intermediate information segments generated by the mapping phase. The existing system has the virtual shuffling that enable information movement and scale back I/O for map reduce shuffling and reduction in disk I/O access but it is not efficient. In the proposed system, the efficient data action in map reduce for fast analytics of big data thereby saving the power consumption and energy conservation which represent common input division in a essential manner and to minimize the shuffling of data and balancing the data shuffling process then to dynamically coordinate and merge without degrading the performance. The proposed method for shuffling in map reduce is performed through a mixture of some techniques together with a dynamic and balanced sub trees, merge process ,three-level segment table and data ranking method.

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