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Analysing Big Data sets Using Descriptive Analytics

Author Affiliations

  • 1Department of Computer Science & Engineering, Shri Shankaracharya Group of Institutions, Chhattisgarh Swami Vivekan and Technical University, Bhilai - 490006, Chhattisgarh, India
  • 2Department of Computer Science & Engineering, Shri Shankaracharya Group of Institutions, Chhattisgarh Swami Vivekan and Technical University, Bhilai - 490006, Chhattisgarh, India

Res. J. Computer & IT Sci., Volume 4, Issue (9), Pages 5-8, September,20 (2016)

Abstract

Big data is generally a term used to delineate massive volume of data that is difficult to process using conventional techniques of data processing. Big data, for any enterprise, refers to the data sets that exceeds its current data processing capacity. As big data is arriving from many different sources with a huge velocity, volume and variety, it is necessary to handle them and extract meaningful information that could be beneficial for an enterprise. For this various kinds of analytics are done. The objective of any analytics solution is to provide the enterprise with actionable insights for better business outcomes and smarter decisions. This paper focusses on analysing big data sets considering data from banking sector thus helping banks in various aspects like customer segmentation, sentiment analysis, transactional analysis, security and fraud management etc. Here Descriptive Analytics is done which uses business intelligence and data mining for learning from past behaviours and thus helping in decision making.

References

  1. Schroeck M., Schockley R., Smart J., Romero-Morales D. and Tufano P. (2012)., Analytics: The Real-World Use of Big Data., IBM Institute for Business Value, http://www-935.ibm.com/services/us/gbs/thoughtleadership/ibv-big-data-at-work.html.22/08/16.
  2. Ammu N. and Irfanuddin M. (2013)., Big Data Challenges., International Journal of Advanced Trends in Computer Science and Engineering, 2(1), 1-4.
  3. Verma R. and Mani S.R. (2012)., Use of Big Data Technologies in Capital Markets., Infosys® Limited, http://www.slideshare.net/Infosys/use-of-big-data-technologies-in-capital-markets, 22/08/16.
  4. Srivastava U. and Gopalkrishnan S. (2015)., Impact of Big Data Analytics on Banking Sector: Learning for Indian Banks., Procedia Computer Science, 50, 643-652, 2nd International Symposium on Big Data and Cloud Computing, 1-10.
  5. Bhosale H.S. and Gadekar D.P. (2014)., A Review Paper on Big Data and Hadoop., International Journal of Scientific and Research Publications, 4(10), 1-3.
  6. Hoppermann J. and Bennett M. (2014)., Big Data In Banking:, It’s Time To Act. Forrester Research, http://www.pentaho.com/sites/default/files/uploads/resources/forrester-research-bigdata-in-banking.pdf.01-05
  7. Siddaraju D., Soumya C.L., Rashmi K. and Rahul M. (2014)., Efficient Analysis of Big Data Using Map Reduce Framework., International Journal of Recent Development in Engineering and Technology., 2(6), 1-3.
  8. Mridul M., Khajuria A., Dutta S. and Kumar N. (2014)., Analysis of Big Data using Apache Hadoop and MapReduce., International Journal of Advanced Research in Computer Science and Software Engineering, 4(5), 1-4.
  9. Kumbhare D. (2016)., Understanding the Hadoop Ecosystem., http://hadooptutorials.co.in/tutorials/hadoop /understanding-hadoop-ecosystem.html.10/08/16.
  10. Patil P.S. and Phursule R.N. (2014)., Survey Paper on Big Data Processing and Map Reduce Components., International Journal of Science and Research, 3(10), 1-6.
  11. Apache Hadoop (2016)., Working of Mapreduce., https://encrypted-tbn2.gstatic.com/images?q=tbn:ANd9Gc Q7LQ02nbkgmPCbrb8_UJJcr6r1YFdAw1_3sCPhXH43urbjqSIKQ.10/08/16.