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Analysis of E-Commerce Backend Operations Data

Published on May 2016 by Amol Modi, Anjana Pradeep, Rohan Jain, Payal Rathod
journal_cover_thumbnail

National Conference on Advancements in Computer & Information Technology
Foundation of Computer Science USA
NCACIT2016 - Number 3
May 2016
Authors: Amol Modi, Anjana Pradeep, Rohan Jain, Payal Rathod
99a5938f-ec2c-4426-b490-e485471cb081

Amol Modi, Anjana Pradeep, Rohan Jain, Payal Rathod . Analysis of E-Commerce Backend Operations Data. National Conference on Advancements in Computer & Information Technology. NCACIT2016, 3 (May 2016), 26-28.

@article{
author = { Amol Modi, Anjana Pradeep, Rohan Jain, Payal Rathod },
title = { Analysis of E-Commerce Backend Operations Data },
journal = { National Conference on Advancements in Computer & Information Technology },
issue_date = { May 2016 },
volume = { NCACIT2016 },
number = { 3 },
month = { May },
year = { 2016 },
issn = 0975-8887,
pages = { 26-28 },
numpages = 3,
url = { /proceedings/ncacit2016/number3/24715-3054/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advancements in Computer & Information Technology
%A Amol Modi
%A Anjana Pradeep
%A Rohan Jain
%A Payal Rathod
%T Analysis of E-Commerce Backend Operations Data
%J National Conference on Advancements in Computer & Information Technology
%@ 0975-8887
%V NCACIT2016
%N 3
%P 26-28
%D 2016
%I International Journal of Computer Applications
Abstract

Data analysis is the process of finding the analyzing data to find useful results for decision-making, understanding fundamental the data, discovering the significant pattern in the data. Analysis of e-commerce data (which includes guidelines details, record details, stuffing details, delivery details, etc. ) involves scanning through the database and extracting data and probing pattern that can be helpful to the company in classify to get better their competence in the backend behavior like selection, superiority checks, packing and shipping. Such information obtained from analysis can provide insight into the various operations and can help the company to make informed decisions to speed up activities and ensure delivery in shortest possible time. The raw data from the various sources needs to be extracted, transformed and loaded into the data warehousebefore beginning the proper analysis on the data. This consolidated data is availableto run a series of patterns for knowledge discovery. In this paper, ETL process, architecture of a system involving Pentaho ETL tool for analysis of large e-commerce data sets and the final visualization of the analysis are discussed.

References
  1. Yoshinori Fukue, Kessoku Masayuki, Kazuhiko Tsuda, 2004. "Extracting Purchase Patterns in Convenience Store E-Commerce Market Using Customer Cube Analysis".
  2. Marin Fotache, Catalin Strimbei, 2015. "SQL and dataanalysis. Some implications for data analysis and highereducation".
  3. Thi Thi Zin, Pyke Tin and Takashi Toriu, HiromitsuHama, 2012. "A Big Data Application Framework forConsumer Behaviour Analysis".
  4. Sachchidanand Singh, Nirmala Singh, 2012. "Big DataAnalytics".
  5. Abdelkarim Ben Ayed, Mohamed Ben Halima, Adel M. Alim, 2015. "Big Data Analytics for Logistics andTransportation".
  6. YANG Hao, Song Hongwei, ZHANG Zili,, 2011. "The application of e-commerce System based on data warehouse".
  7. Sean Kandel, Andreas Paepcke, Joseph M. Hellerstein, and Jeffrey Heer, 2012. "Enterprise Data Analysis and Visualisation: An Interview Study".
  8. D. Gotz and M. X. Zhou,. "Characterizing users' visual analytic activity for insight provenance". Information Visualization, 8:42–55, 2009. ?
  9. Wang Hongding, Yu Bo, Tang Shiwei. , "An EffectiveApproach to Design Data Warehouse" [ J ]. COMPUTERENGINEERING AND APPLICATIONS, 2004 40(9) 12 82.
  10. Borkar, V. R. , Carey, M. J. , Li, C. (2012), Inside "Big Data Management": Ogres, Onions, or Parfaits, Proc. of the 15th International Conference on Extending Database Technology (EDBT '12), ACM, New York, USA, pp. 3-14?.
  11. T. K. Das, P. Mohan Kumar, "BIG Data Analytics: A Framework for Unstructured Data Analysis," Intl. Journal of Engineering and Technology (IJET), vol. 5, pp. 153-156, Feb-Mar 2013.
  12. R. Amar, J. Eagan, and J. Stasko,"Low-level components of analytic activity in information visualization". In Proc. IEEE Information Visualization (InfoVis), 2005.
Index Terms

Computer Science
Information Sciences

Keywords

Database Management Information Storage And Retrieval Information Systems Applications