1. 1 Maintaining Knowledge-Bases of Navigational Patterns from Streams of Navigational Sequences Ajumobi Udechukwu, Ken Barker, Reda Alhajj Proceedings of the 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications (RIDE-SDMA’05) Advisor ： Jia-Ling Koh Speaker ： Chun-Wei Hsieh

2. 2 Introduction Navigational patterns: traversal patterns Two broad techniques for mining navigational patterns – 1. level-wise, apriori-based techniques – 2. tree-based techniques

3. 3 Methodology Sliding window Batch-update strategy – Batch: the web log in the base time unit Example

4. 4 Adapted GST Adapted generalized suffix tree Appending a stop symbol to all strings Mining without thresholds

5. 5 Adapted GST LQR LQ

6. 6 Adapted GST

7. 7 The Challenge of Adapted GST ” LQ ” occurs in B1 with support count of 4 and “ L ” occurs independently in B2 with support count of 2 Total count of “ L ” should be 4 + 2

8. 8 AC-NAP tree 1

9. 9 AC-NAP tree 2 Output all node labels and counts to a database

10. 10 Maintaining patterns within a window

11. 11 Maintaining patterns within a window Count total support Remove out_of_date patterns

12. 12 Experiments OS: Microsoft Windows XP professional edition CPU: 2GHz Intel Pentium 4 RAM: 512MB Program language: Java DBMS: MySQL Data: real-world web logs of ”msnbc.com”

13. 13 Experiments

14. 14 Experiments

15. 15 Experiments