Professor Ismail Toroslu gave a lecture on "Web Usage Mining and Using Ontology for Capturing Web Usage Semantic" in the Distinguished Lecturer Series - Leon The Mathematician. More Information available at: http://dls.csd.auth.gr

1. İsmail Hakkı Toroslu Middle East Technical University Department of Computer Engineering Ankara, Turkey Web Usage Mining and Using Ontology for Capturing Web Usage Semantic

2. 08/28/11 PART I A New Approach for Reactive Web Usage Data Processing

5. Web Usage Mining (WUM) Application of data mining techniques to web log data in order to discover user access patterns. Example User Web Access Log Web Mining 4130 200 HTTP/1.0 C.html GET [25/Apr/2005:03:04:48–05] 144.123.121.23 2050 200 HTTP/1.0 B.html GET [25/Apr/2005:03:04:43–05] 144.123.121.23 3290 200 HTTP/1.0 A.html GET [25/Apr/2005:03:04:41–05] 144.123.121.23 Number of Bytes Transmitted Success of Return Code Protocol URL Method Request Time IP Address

6. Phases of Web Usage Mining Web Mining Pre-Processing Pattern Analysis Raw Server log User session File Rules and Patterns Interesting Knowledge Applications Session Reconstruction Heuristics Pattern Discovery Apriori, GSP, SPADE

9. Example Web Topology Graph Example Web Page Request Sequence Previous Session Reconstruction Heuristics 47 32 29 15 6 0 Timestamp P 23 P 34 P 49 P 13 P 20 P 1 Page

13. Navigation-Oriented Heuristic Previous Session Reconstruction Heuristics [P 1 , P 20 , P 1 , P 13 , P 49 , P 13 , P 34 , P 23 ] P 23 Link[P 34 , P 23 ] =1 [P 1 , P 20 , P 1 , P 13 , P 49 , P 13 , P 34 ] P 34 Link[P 49 , P 34 ] = 0 Link[P 13 , P 34 ] = 1 [P 1 , P 20 , P 1 , P 13 , P 49 ] P 49 Link[P 13 , P 49 ] = 1 [P 1 , P 20 , P 1 , P 13 ] P 13 Link[P 20 , P 13 ] = 0 Link[P 1 , P 13 ] = 1 [P 1 , P 20 ] P 20 Link[P 1 , P 20 ] = 1 [P 1 ] P 1 [ ] New Page Condition Curent Session

16. Example Candidate Session Example Web Topology Smart-SRA 15 14 12 9 6 0 Timestamp P 23 P 34 P 49 P 13 P 20 P 1 Page

17. Smart-SRA [P 1 , P 13 , P 34 , P 23 ] , [P 1 , P 13 , P 49 , P 23 ] [P 1 , P 20 , P 23 ] [P 1 ,P 13 ,P 34 ], [P 1 , P 13 , P 49 ] [P 1 , P 20 ] New Session Set (after) [P 1 , P 13 , P 34 , P 23 ] [P 1 , P 13 , P 49 , P 23 ], [P 1 , P 20 , P 23 ] [P 1 ,P 13 ,P 34 ] [P 1 , P 13 , P 49 ] Temp Session Set {P 23 } {P 49 , P 34 } Temp Page Set [P 1 ,P 13 ,P 34 ] [P 1 , P 13 , P 49 ] [P 1 , P 20 ] [P 1 ,P 20 ] [P 1 ,P 13 ] New Session Set (before) [P 23 ] [P 49 , P 34 , P 23 ] Candidate Session 4 3 Iteration [P 1 ,P 20 ] [P 1 ,P 13 ] [P 1 ] New Session Set (after) [P 1 ,P 20 ] [P 1 ,P 13 ] [P 1 ] Temp Session Set {P 20 , P 13 } {P 1 } Temp Page Set [P 1 ] New Session Set (before) [P 20 , P 13 , P 49 , P 34 , P 23 ] [P 1 , P 20 , P 13 , P 49 , P 34 , P 23 ] Candidate Session 2 1 Iteration

19. Web user can start a new session with any one of the possible entry pages of the web site Agent Simulator User-Behavior I

20. Web user can select a new page having a link from the most recently accessed page P 13 P 1 P 49 P 20 P 23 P 34 2 1 Agent Simulator User-Behavior II

21. Web user can select as the next page having a link from any one of the previously browsed pages Agent Simulator User-Behavior III P 13 P 1 P 49 P 20 P 23 P 34 2 1 3 4 5

22. Web user can terminate the session Agent Simulator User-Behavior IV P 13 P 1 P 49 P 20 P 23 P 34 2 1 3 4 5 6

26. Parameters for generating user sessions and web topology Experimental Results 30% 0%-90% NIP : Fixed & Range 30% 0%-90% LPP : Fixed & Range 5% 1%-20% STP : Fixed & Range 10000 Number of agents 0,5 min Deviation for page stay time 2,2 min Average number of page stay time 15 Average number of outdegree 300 Number of web pages (nodes) in topology

27. Accuracy vs. STP Experimental Results

28. Accuracy vs LPP Experimental Results

29. Accuracy vs. NIP Experimental Results

31. 08/28/11 PART II Semantically Enriched Event Based Model f or W eb Usage Mining

43. 08/28/11

51. 08/28/11 Music Streaming Site - Events

54. 08/28/11 Mobile Network Operator Site - Ontology