Abstract
When people visit Websites, they desire to efficiently and exactly access the contents they are interested in without delay. However, due to the constant changes of site contents and user patterns, the access efficiency of Websites cannot be optimized, especially in peak hours. In this paper, we first address the problems of access efficiency in Websites during peak hours and then propose new measures to evaluate access efficiency. An efficient algorithm is introduced to detect user access patterns using Website topology and Web-log stream data. Adopting this method, we can online modify a Website topology so that the new topology can improve the Website connectivity to adapt current visitors’ access patterns. A real sports Website is used to evaluate the effectiveness of our proposed method of accelerating user access to related contents. The results of the evaluation presented in this paper suggest that this method is feasible to online improve the connectivity of a Website intelligently.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Berendt, B., Mobasher, B., Nakagawa, M., Spiliopoulou, M.: The Impact of Site Structure and User Environment on Session Reconstruction in Web Usage Analysis. In: Proceeding of the WEBKDD 2002 Workshop, Edmonton, Canada (2002)
Cooley, R., Mobasher, B., Srivastava, J.: Data preparation for mining World Wide Web browsing patterns (1999)
Cooley, R., Tan, P.-N., Srivastava, J.: Websift: The web site information filter system. In: Proceedings of the Web Usage Analysis and User Profiling Workshop (1999)
Ron, K.: Mining E-Commerce Data: The Good, the Bad, and the Ugly. In: Invited talk at KDD 2001 industrial track, San Francisco, California, USA (2001)
Mobasher, B., Dai, H., Luo, T., Nakagawa, M., Sun, Y., Wiltshire, J.: Discovery of aggregate usage profiles for Web personalization (2000)
Mobasher, B., Dai, H., Luo, T., Sun, Y., Zhu, J.: Combining Web Usage and Personalization. In: Proc. the Technologies (2000)
Mobasher, B., Jain, N., Han, E., Srivastava, J.: Web mining: pattern Transactions, Tech. Rep. 96-050
Srivastava, J., Cooley, R., Deshpande, M., Tan, P.N.: Web Usage Mining: Discovery and applications of usage patterns from web data. SIGKDD Explorations 1, 12–23 (2000)
Shen, L., Cheng, L., Ford, J., Makedon, F., Megalooi-konomou, V., Steinberg, T.: Mining the most interesting web access associations. In: Proc. the 5th International Conference on Knowledge Discovery and Data Mining (KDD 1999), pp. 145–154 (1999)
Srikant, R., Yang, Y.: Mining web logs to improve website organization. In: World Wide Web Conference, pp. 430–437 (2001)
Wu, E.H., Ng, M.K.: A Graph-based Optimization Algorithm for Website Topology Using Interesting Association Rules, Proc. the Seventh Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003 (2003)
Yang, Q., Huang, J., Ng, M.: A data cube model for prediction-based Web prefetching. Journal of Intelligent Information Systems 20, 11–30 (2003)
Zaiane, O.R., Xin, M., Han, J.: Discovering Web Access Patterns and Trends by Applying OLAP and Data Mining Technology on Web Logs. In: Proc. ADL 1998 (Advances in Digital Libraries), Santa Barbara (April 1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, E.H., Ng, M.K., Huang, J.Z. (2004). On Improving Website Connectivity by Using Web-Log Data Streams. In: Lee, Y., Li, J., Whang, KY., Lee, D. (eds) Database Systems for Advanced Applications. DASFAA 2004. Lecture Notes in Computer Science, vol 2973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24571-1_32
Download citation
DOI: https://doi.org/10.1007/978-3-540-24571-1_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21047-4
Online ISBN: 978-3-540-24571-1
eBook Packages: Springer Book Archive