Exhausting Agile Processing and Data Mining in Electronic Commerce

Authors

  • Dr. Santosh Kumar Dwivedi  Assistant Professor, Department of Computer Science, SRMGPC. College, Tewarigunj, Lucknow, Uttar Pradesh, India
  • Dr. Rajeev Tripathi  Assistant Professor, Department of Computer Science, SRMGPC. College, Tewarigunj, Lucknow, Uttar Pradesh, India

DOI:

https://doi.org//10.32628/CSEIT19547

Keywords:

Agile, Data Mining, Big Data, E-Commerce

Abstract

Agile software developments are hotspots of software development field in foreign countries. Metrics and big data will boost today's marketing leaders toward success. While the notion of the metrics-driven CMO is well-understood, marketers still struggle with how to apply agile and big data to deliver big business value. To do so successfully, they must get bigger their point of view further than promotional activities and center movements around their customers and their purchasing processes. Data mining metrics like this would give us great insight into whether the change was successful; whether it led to hairline fracture how it has changed user behavior, and whether it is delivering net value to the business in terms of the A/B test. The bottom few in the list could be used to track and improve it over time, considering there is some benefit to the business in terms of capturing the new data.

References

  1. Rao, T.K.R.K., Khan, S.A., Begun, Z. and Divakar, Ch. (2013) Mining the E- Commerce Cloud: A Survey on Emerging Relationship between Web Mining, E-Commerce and Cloud Computing. IEEE International Conference on ComputationalIntelligence and Computing Research, Enathi, 26-28 December 2013, 1-4.http://dx.doi.org/10.1109/iccic.2013.6724234
  2. Wu, M., Zhang, H. and Li, Y. (2013) Data Mining Pattern Valuation in Apparel Industry E-Commerce Cloud. IEEE4th International Conference on Software Engineering and Service Science (ICSESS), 689-690.
  3. Srinniva, A., Srinivas, M.K. and Harsh, A.V.R.K. (2013) A Study on Cloud Computing Data Mining. InternationalJournal of Innovative Research in Computer and Communication Engineering,1, 1232-1237.
  4. Carbone, P.L. (2000) Expanding the Meaning and Application of Data Mining. International Conference on Systems, Man and Cybernetics,3, 1872-1873. http://dx.doi.org/10.1109/icsmc.2000.886383
  5. Barry, M.J.A. and Linoff, G.S. (2004) On Data Mining Techniques for Marketing, Sales and Customer Relationship Management. Indianapolis Publishing Inc., Indiana.
  6. Pan, Q. (2011) Research of Data Mining Technology in Electronic Commerce. IEEE Computer Society, Wuhan,12-14
  7. August 2011, 1-4. http://dx.doi.org/10.1109/icmss.2011.5999185
  8. Verma, N., Verma, A., Rishma and Madhuri (2012) Efficient and Enhanced Data Mining Approach for Recommender System. International Conference on Artificial Intelligence and Embedded Systems (ICAIES2012), Singapore, 15-16 July 2012.
  9. Kamba, M. and Hang, J. (2006) Data Mining Concept and Techniques. Morgan Kaufmann Publishers, San Fransisco.
  10. News Stack (2015). http://thenewstack.io/six-of-the-best-open-source-data-mining-tools/
  11. Witten, I.H. and Frank, E. (2014) The Morgan Kaufmann Series on Data Mining Management Systems: Data Mining. 2nd Edition, Publisher Morgan Kaufmann, San Francisco, 365-528.
  12. Liu, X.Y. And Wang, P.Z. (2008) Data Mining Technology and Its Application in Electronic Commerce. IEEE ComputerSociety, Dalian, 12-14 October 2008, 1-5.
  13. Zeng, D.H. (2012) Advances in Computer Science and Engineering. Springer Heidelberg, NewYork.

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Published

2019-07-30

Issue

Section

Research Articles

How to Cite

[1]
Dr. Santosh Kumar Dwivedi, Dr. Rajeev Tripathi, " Exhausting Agile Processing and Data Mining in Electronic Commerce, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 4, pp.80-84, July-August-2019. Available at doi : https://doi.org/10.32628/CSEIT19547