International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 34 | Number 1 | Year 2016 | Article Id. IJCTT-V34P115 | DOI : https://doi.org/10.14445/22312803/IJCTT-V34P115

Clickstream Analysis using Hadoop

Harshit Makhecha, Dharmendra Singh, Bhagirath Prajapati, Priyanka Puvar

Citation :

Harshit Makhecha, Dharmendra Singh, Bhagirath Prajapati, Priyanka Puvar, "Clickstream Analysis using Hadoop," International Journal of Computer Trends and Technology (IJCTT), vol. 34, no. 1, pp. 89-92, 2016. Crossref, https://doi.org/10.14445/22312803/ IJCTT-V34P115

Abstract

E-Commerce websites generates huge churns of data due to large amount of transactions taking place every second and so their inventory should be updated as per transactions very quickly to remain stable in these competitive market. Analyzing web log files has become one of the important task for ECommerce companies to predict their customer behavior. Clickstream data is very important part of big data marketing as it will tell what customers click on and purchase or (do not purchase). The primary focus of the paper is to prepare web log analysis system which will depict trends based on the users browsing mode using Hadoop MapReduce and handling heterogeneous query execution on log file.

Keywords

The primary focus of the paper is to prepare web log analysis system which will depict trends based on the users browsing mode using Hadoop MapReduce and handling heterogeneous query execution on log file.

References

[1] What is big data: - IBM?
[2] “Why Big Data is a must in E-Commerce”, Guest post by Jerry Jao, CEO of Retention Science. http://www.bigdatalandscape.com/news/why-big-data-is-amust- in-ecommerce
[3] Tom White, (2009) “Hadoop: The Definitive Guide. O’Reilly”, Scbastopol, California.
[4] Apache-Hadoop, http://Hadoop.apache.org
[5] L.K. Joshila Grace, V.Maheswari, Dhinaharan Nagamalai, “ANALYSIS OF WEB LOGS AND WEB USER IN WEB MINING”, International Journal of Network Security & Its Applications (IJNSA), Vol.3, No.1, January 2011
[6] https://en.wikipedia.org/wiki/Semi-structured_data