MATEC Web Conf.
Volume 125, 201721st International Conference on Circuits, Systems, Communications and Computers (CSCC 2017)
|Number of page(s)||8|
|Published online||04 October 2017|
Website Clickstream Data Visualization Using Improved Markov Chain Modelling In Apache Flume
PhD Candidate, Department of Telecommunication and Information Technology, University Politehnica of Bucharest, Romania.
Clickstream data analysis is considered as the process of collecting, analysing and reporting the aggregate data about the web pages a visitor clicks. Visualizing the clickstream data has gained significant importance in many applications like web marketing, customer prediction, product management, etc. Most existing works employ different tools for visualizing along with techniques like Markov chain modelling. However the accuracy of the methods can be improved when the shortcomings are resolved. Markov chain modelling has problems of occlusion and unable to provide clear display of data visualizing. These issues can be resolved by improving the Markov chain model by introducing a heuristic method of Kolmogorov– Smirnov distance and maximum likelihood estimator for visualizing. These concepts are employed between the underlying distribution states to minimize the Markov distribution. The proposed model named as WebClickviz is performed in Hadoop Apache Flume which is a highly advanced tool. The clickstream data visualization accuracy can be improved when Apache Flume tools are used. The performance evaluation are made on a specific website clickstream data which shows the proposed model of visualization has better performance than existing models like VizClick.
Key words: Clickstream data / VizClick / WebClickviz / Apache Flume / Markov chain / Kolmogorov-Smirnov distance
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.