Abstract
The rapid growth of Internet usage has dramatically changed the way we interact with the outside world. Many people read news, communicate with friends and purchase goods online. These activities are usually done via web browsing, and web browsers record information about these activities. The recorded data can be used to understand web browsing behavior of users and improve their browsing experience. For example, website usability and the personalization of online services could both benefit from knowledge of user browsing behavior. A number of methods including data mining, text processing and visualization have been used to uncover user browsing patterns. However, these methods are mainly used to analyze and gain insights into collective behavior patterns of either a large amount of separate web users or users within an online community over a prolonged period of time. Very few systems are available for analyzing the detailed behavior of a single user within a relatively short and specific period of time. In an attempt to shorten this gap, we have developed a visual analytic system called WeBeVis. This system offers three different ways of visualizing web browsing data based on our proposed visual metaphors. It also provides a common interface for users to interact with the visualizations. In this paper, we describe this system and present a user study of it. We show that by visualizing the web browsing history of a user, we are able to uncover interesting patterns in the way that individuals use the web.
Similar content being viewed by others
References
Kellar M, Hawkey K, Inkpen K M, et al. Challenges of capturing natural web-based user behaviours. Int J Hum-Comput Interact, 2008, 24: 385–409
World Internet Users and Population Stats. http://www.internetworldstats.com/stats.htm
Hu J, Zeng H J, Li H, et al. Demographic prediction based on user’s browsing behavior. In: The 16th International Conference on World Wide Web. New York: ACM, 2007. 151–160
Srivastava J, Ahmad M A, Pathak N, et al. Data mining based social network analysis from online behavior. Tutorial at the 8th SIAM International Conference on Data Mining, 2008
Liu C, White R W, Dumais S. Understanding web browsing behaviors through Weibull analysis of dwell time. In: The 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2010. 379–386
Kumar S, Zafarani R, Liu H. Understanding user migration patterns in social media. In: Proceedings of the 25th AAAI Conference on Artificial Intelligence, San Francisco, 2011. 1204–1209
Parker J K. BrowsingViz: Visualising web browsing behaviours for HCI research. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.117.5647
Stones C, Sobol S. DMASC: A tool for visualising user paths through a web site. In: Proceedings of the 13th International Workshop on Database and Expert Systems Applications, Aix en Provence, France, 2002. 389–393
Roussas G. Visualisation of client-side web browsing and email activity. Master’s Thesis. Monterey: Naval Postgraduate School, 2009
Costagliola G, Fuccella V. Fine-grained analysis of web tasks through data visualization. In: Proceedings of the 9th International Conference on Web Engineering. Berlin/Heidelberg: Springer-Verlag, 2009. 1–15
Berendt B, Brenstein E. Visualizing individual differences in Web navigation: STRATDYN, a tool for analyzing navigation patterns. Behav Res Methods Instrum Comput, 2001, 33, 243–257
Cadez I, Heckerman D, Meek C, et al. Visualization of navigation patterns on a Web site using model-based clustering. In: The 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2000. 280–284
Chi E. Improving web usability through visualisation. IEEE Internet Comput, 2002, 6: 64–71
Chen J, Zheng T, Thorne W, et al. Visualizing web navigation data with polygon graphs. In: The 11th International Conference Information Visualization, Zurich, 2007. 232–237
Khoury R, Dawborn T, Huang W. Visualizing Web Browsing Data for User Behavior Analysis. In: Proceedings of the 23rd Australian Computer-Human Interaction Conference. New York: ACM, 2011. 177–180
Veldhuizen T L. Dynamic multilevel graph visualization. arXiv: cs.gr/07121549, 2007
Walshaw C. A multilevel algorithm for force-directed graph drawing. In: The 8th International Symposium on Graph Drawing. London: Springer-Verlag, 2000. 171–182
Sugiyama K, Tagawa S, Toda M. Methods for visual understanding of hierarchical system structures. IEEE Trans syst man cybern, 1981, 11: 109–125
Gansner E R, North S C. An open graph visualization system and its applications to software engineering. Softw-Pract Exp, 2000, 30: 1203–1233
O’Madadhain J, Fisher D, White S, et al. Analysis and visualization of network data using JUNG. J Stat Softw, 2005, 10: 1–35
Fruchterman T M J, Reingold E M. Graph drawing by force-directed placement. Softw-Pract Exp, 1991, 21: 1129–1164
Kamada T, Kawai S. An algorithm for drawing general undirected graphs. Inf process lett, 1989, 31: 7–15
Lin C-C, Yen H-C. A new force-directed graph drawing method based on edge-edge repulsion. In: Proceedings of the 9th International Conference on Information Visualisation. Washington DC: IEEE Computer Society, 2005. 329–334
Huang W, Eades P, Hong S-H, et al. Improving force-directed graph drawings by making compromises between aesthetics. In: IEEE Symposium on Visual Languages and Human-Centric Computing, Leganes, 2010. 176–183
Huang W, Huang M L, Lin C-C. Aesthetic of angular resolution for node-link diagrams: validation and algorithm. In: IEEE Symposium on Visual Languages and Human-Centric Computing, Pittsburgh, 2011. 213–216
Huang W, ed. Handbook of Human Centric Visualization. Springer-Verlag, 2013
Huang W, Hong S-H, Eades P. Layout effects: Comparison of sociogram drawing conventions. Technical Report TR No. 575, University of Sydney, 2005
Huang W, Eades P, Hong S-H. Beyond time and error: a cognitive approach to the evaluation of graph drawings. In: Proceedings of the Workshop on Beyond Time and Errors: Novel EvaLuation Methods for Information Visualization. New York: ACM, 2008. 3
Huang W, Murray C, Shen X, et al. Visualisation and analysis of network Motifs. In: Proceedings of the 9th International Conference on Information Visualisation, London, 2005. 697–702
Nguyen Q V, Huang M L. EncCon-an approach to constructing interactive visualisation of large hierarchical data. Inf Vis, 2005, 4: 1–21
Chen J, Forsberg A S, Swartz S M, et al. Interactive multiple scale small multiples. In: IEEE Visualization 2007 Poster Compendium, Sacramento, 2007
Chen Z M, Faragó A, Zhang K. Exploring structural properties of web graphs through 3D visualization. In: Proceedings of the 14th International Conference on Enterprise Information Systems, Wroclaw, 2012. 2 233–238
Si Y-W, Cheong S-H, Fong S, et al. A layered approach to link analysis and visualization of event data. In: Proceedings of the 7th International Conference on Digital Information Management, Macau, 2012. 181–185
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Huang, W., Khoury, R., Dawborn, T. et al. WeBeVis: analyzing user web behavior through visual metaphors. Sci. China Inf. Sci. 56, 1–15 (2013). https://doi.org/10.1007/s11432-013-4869-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11432-013-4869-7