Tracking Activity of Real Individuals in Web Logs
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Tracking Activity of Real Individuals in Web Logs

Authors: Sándor Juhász, Renáta Iváncsy

Abstract:

This paper describes an enhanced cookie-based method for counting the visitors of web sites by using a web log processing system that aims to cope with the ambitious goal of creating countrywide statistics about the browsing practices of real human individuals. The focus is put on describing a new more efficient way of detecting human beings behind web users by placing different identifiers on the client computers. We briefly introduce our processing system designed to handle the massive amount of data records continuously gathered from the most important content providers of the Hungary. We conclude by showing statistics of different time spans comparing the efficiency of multiple visitor counting methods to the one presented here, and some interesting charts about content providers and web usage based on real data recorded in 2007 will also be presented.

Keywords: Cookie based identification, real data, user activitytracking, web auditing, web log processing

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1072114

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References:


[1] Z. Pabarskaite, A. Raudys, "A process of knowledge discovery from web log data: Systematization and critical review." Journal of Intelligent Information Systems 28(1): pp. 79-104, 2007.
[2] Median Public Opinion and Market Research Institute. http://www.median.hu/ and http://www.webaudit.hu/
[3] R. Kosala, H. Blockeel, "Web mining research: A survey." ACM SIGKDD Explorations, 1, pp. 1-15, 2000.
[4] W3C, Common Log Format, http://www.w3.org/Daemon/User/Config/Logging.html
[5] G. Fleishman, "Web log analysis, who-s doing what, when?" Web Developer. 1996.
[6] M. Spiliopoulou, "Managing interesting rules in sequence mining." 3rd European Conference on Principles and Practice of Knowledge Discovery in Databases PKDD-99. Prague, Czech Republic: Springer- Verlag. 1999.
[7] H. Ishikawa, M. Ohta, Sh. Yokoyama, J. Nakayama, K. Katayama, "On The Effectiveness of Web Usage Mining for Page Recommendation and Restructuring," Lecture Notes In Computer Science; Vol. 2593, pp: 253- 267.
[8] S. Baron, M. Spiliopoulou, "Monitoring the Evolution of Web Usage Patterns, Web Mining: From Web to Semantic Web," First European Web Mining Forum, (EMWF 2003), Cavtat-Dubrovnik, Croatia, September, pp. 181-200, 2003.
[9] M. Spiliopoulou, C. Pohle, L. C. Faulstich, "Improving the Effectiveness of a Web Site with Web Usage Mining, International Workshop on Web Usage Analysis and User Profiling," WEBKDD, pp. 142-162. 2000.
[10] M. Spiliopoulou, B. Mobasher, B. Berendt, M. Nakagawa, "A Framework for the Evaluation of session reconstruction heuristics in Web-usage analysis." INFORMS Journal on Computing 15: pp. 171- 190, 2003.
[11] L. D. Catledge, J. E. Pitkow, "Characterizing browsing strategies in the world-wide web." Computer Networks and ISDN Systems, 6, 10-65, 1995.
[12] R. Cooley, P. Tan, J. Srivastava, "Discovery of interesting usage patterns from Web data." B. Masand, M. Spiliopoulou, eds. Advances in Web Usage Analysis and User Profiling. LNAI 1836, Springer, Berlin, Germany. 163-182, 2000.
[13] Brandt Dainow, "3rd Party Cookies Are Dead," Web Analytics Associations, 2005. http://www.webanalyticsassociation.org/en/art/?2
[14] "WebTrends Advises Sites to Move to First-Party Cookies Based on Four-Fold Increase in Third-Party Cookie Rejection Rates," WebTrends, 2005. http://www.webtrends.com/CookieRejection.