SpeedTracer: A Web usage mining and analysis tool

IBM Systems Journal

Volumn 37, Issue 1, 1998, Pages 89-104

  Wu, K L ^{a,b,c,d,e,f,g,h,i,j}   Yu, P S ^a,b,f,k,l,m   Ballman, A ⁿ  

^a IBM T J WATSON RESEARCH CENTER   (United States)

^b NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^c UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^d University of Illinois at Urbana Champaign   (United States)

^e TEXAS INSTRUMENTS   (United States)

^f Internet Technology Department   (Taiwan)

^g Organizing and Program Committee   (United States)

^h IEEE   (United States)

ⁱ ACM   (United States)

^j Phi Kappa Phi   (United States)

^k STANFORD UNIVERSITY   (United States)

^l NEW YORK UNIVERSITY   (United States)

^m Program Committee

ⁿ Program Committee ^*

more..

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA ACQUISITION; DISTRIBUTED DATABASE SYSTEMS; INFORMATION RETRIEVAL; SECURITY OF DATA; WIDE AREA NETWORKS;

DATA MINING; SOFTWARE PACKAGE SPEEDTRACER; WORLD WIDE WEB (WWW);

ONLINE SEARCHING;

EID: 0031646959     PISSN: 00188670     EISSN: None     Source Type: Journal    
DOI: 10.1147/sj.371.0089     Document Type: Article

References (14)

1. The uniform resource locator is used to uniquely identify a resource on the Internet. An example of a URL is "http: //www.ibm.com/," which represents the IBM home page on the Internet.
2. HyperText Transfer Protocol is the basic protocol used by the Web to transfer documents between a browser and a Web server.
3. J. Pitkow, "In Search of Reliable Usage Data on the WWW," Proceedings of Sixth International World Wide Web Conference (1997).
4. The National Center for Supercomputing Applications is located in the University of Illinois at Urbana-Champaign, Illinois.
5. NCSA HTTPd is an HTTP/1.0-compatible server for making hypertext and other documents available to Web browsers. It is copyrighted by the University of Illinois and owned by the university.
6. SpeedTracer is available for download from IBM AlphaWorks™ at http://www.alphaworks.ibm.com.
7. B. Mobasher et al., Web Mining: Pattern Discovery from World Wide Web Transactions, Technical Report 96-050, Department of Computer Science, University of Minnesota, Minneapolis (September 1996).
8. P. Pirolli, R. Rao, and J. Pitkow, "Silk from a Sow's Ear: Extracting Usable Structures from the Web," Proceedings of 1996 Conference on Human Factors in Computing Systems (1996), pp. 118-125.
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10. R. Agrawal and R. Srikant, "Fast Algorithms for Mining Association Rules in Large Databases," Proceedings of Very Large Data Bases (1994), pp. 478-499.
11. J. Han and Y. Fu, "Discovery of Multiple-Level Association Rules from Large Databases," Proceedings of the 21st VLDB Conference (1995), pp. 420-431.
12. J.-S. Park, M.-S. Chen, and P. S. Yu, "An Effective Hash Based Algorithm for Mining Association Rules," Proceedings of ACM SIGMOD International Conference on Management of Data (1995), pp. 175-186.
13. R. Agrawal and R. Srikant, "Mining Sequential Patterns," Proceedings of 11th International Conference on Data Engineering (1995), pp. 3-14.
14. M.-S. Chen, J. S. Park, and P. S. Yu, "Data Mining for Path Traversal Patterns in a Web Environment," Proceedings of International Conference on Distributed Computing Systems (1996), pp. 385-392.