Advanced Web User Monitoring with Real-Time Communications Devices

Authors

  • Lukas Cegan University of Pardubice, Faculty of Health Studies, Department of Informatics, Management and Radiology, Pardubice, Czech Republic

Keywords:

Monitoring, Web Performance, WebRTC, Real-Time,

Abstract

This paper presents a new approach to web user monitoring with real-time communications devices. Proposed solution is based on a combination of WebRTC technology, performance data logging and third party services. This solutions allows measurement of a user’s web performance data, evaluated user’s behavior and makes gender and age classification. The results are an important tool in understanding what people think and feel while browsing a website. Thanks to this expertise we can better tailor web page content to achieve our business goals. Experimental results published in this paper were conducted on real data and show that the proposed solution of capturing and transforming face images from a video stream allows achievement of very high accuracy of gender and age classification by third party services.

References

DongshanX. and Juni S., “A New Markov Model for Web Access Prediction,” IEEE Computing in Science and Eng. vol. 4, pp. 34-39, November 2012.

ShafiqAlam, Gillian Dobbie, Patricia Riddl. “Exploiting Swarm Behaviour of Simple Agents for Clustering Web Users’ Session Data,” Data Mining and Multi-agent Integration, pp. 61-75, 2009, Springer US.

Deshpande M. and Karypis G., “Selective Markov Models for Predicting Web Page Accesses,” ACM Trans. Internet Technology, vol. 4, pp. 163-184. May 2004.

Borges J. andLevene M., “Evaluating Variable-Length Markov Chain Models for Analysis of User Web Navigation Sessions,” IEEE Trans. On knowledge and Data Engineering, vol. 19, no. 4, April 2007.

Cegan L., “Intelligent Preloading of Websites Resources Based on Clustering Web User Sessions,” IT Convergence and Security

(ICITCS), pp. 1- 4, Kuala Lumpur. IEEE. Aug 2015.

Talele K.T., Kadam S., “Face detection and geometric face

normalization,” TENCON 2009 - 2009 IEEE Region 10 Conference, Singapore, ISBN 978-1-4244-4546-2, 2009.

Lijing Zhang, Yingli Liang, The 2nd IEEE International Conference

on Advanced Computer Control(ICACC 2010), Shenyang, China, pp.

- 494, 2010, ISBN 978-1-4244-5845-5

Kienzle, W., Bakir, G., Franz, M., Scholkopf, B. “Face detection –efficient and rank deficient ,”Advan. in neural inform. process. systems

vol. 17, pp. 673–680, 2005.

ZulhadiZakaria, Shahrel A. Suandi, “Face Detection Using

Combination of Neural Network and Adaboost ,” TENCON 2011 -2011 IEEE Region 10 Conference, Bali, pp. 335 - 338, 2011ISBN 978-1-4577-0256-3.

Xiaoning Liu, GuohuaGeng, Xiaofeng Wang,“Automatically face detection based on BP neural network and Bayesian decision, Natural Computation (ICNC),” Sixth International Conference on Natural Computation, Yantai, Shandong, pp.1590-1594. ISBN 978-1-42445958-2.

Bauckhage C., Jahanbekam A., Thurau C., “Age Recognition in the Wild,” Pattern Recognition (ICPR), Istanbul, pp. 392-395, 2010, ISBN 978-1-4244-7542-1.

Mliki H., Fourati N., Smaoui S., Hammami M., “Facial Expression Recognition System,” Computer Systems and Applications (AICCSA), 2013, Ifrane, ISSN: 2161-5322.

Wang Z. and Jain A. Navigation Timing. Technical Report

https://dvcs.w3.org/hg/webperf/rawfile/tip/specs/NavigationTiming/O

verview.html, W3C, January 2013.

Alan B. Johnston, Daniel C. Burnett. “WebRTC: APIs and RTCWEB Protocols of the HTML5 Real-Time Web,” Third Edition, pp. 350, Digital Codex LLC; 3 edition, March 11 2014, ISBN 978-0985978860

Kartak S., Kavicka A., “WebRTC Technology as a Solution for a WebBased Distributed Simulation,” Proceedings of the European modeling and Simulation Symposium, pp. 343–349, 2014, Genova: Università di Genova.

PavelJetensky, “Human Hand Image Analysis Extracting Finger Coordinates and Axial Vectors: Finger axis detection using blob extraction and line fitting,” In 2014 Radioelektronika (RADIOELEKTRONIKA), 24th International Conference IEEE. Bratislava (Slovak Republic), pp. 73-77, 2014, ISBN 978-1-47993713-4.

Downloads

Published

2017-03-15

How to Cite

Cegan, L. (2017). Advanced Web User Monitoring with Real-Time Communications Devices. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(1-4), 41–44. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1777