Effective Prediction of Web-user Accesses: A Data Mining Approach

1. Effective Prediction of Web-user Accesses: A Data Mining Approach Nanopoulos Alexandros Katsaros Dimitrios Yannis Manolopoulos Aristotle Univ. of Thessaloniki, Greece Presentation: Spyros Papadimitriou, Carnegie Mellon Univ.

2. Introduction (1/2) • Web Prefetching: Deducing forthcoming user accesses based on log information • Focus on: • Predictive prefetching (use of history) • Server initiated (server makes predictions and piggybacks them to the clients)

3. Introduction (2/2) • Within a site, users navigate following links [5] • For server-initiated predictive prefetching interest is for access patterns reflecting this behavior

4. Outline • Motivation & Related work • Proposed method • Comparative performance evaluation • Conclusions

5. Presentation Outline • Motivation & Related work • Proposed method • Comparative performance evaluation • Conclusions

6. Requirements • Site structure and contents impose • The order of dependencies (first or higher) among the documents • The interleaving of documents belonging to patterns with random visits (noise) • Discovered patterns should respect these factors

7. Related work • Dependency graph (DG) [9] • A graph maintains pairwise accesses • Prediction by Partial Match (PPM) [10] • A trie maintains sequences of consecutive accesses • LBOT [6] • Special form of association rules of length 2 • Others (variations of the above) [3,11]

8. Motivation Noise (2nd Req.) Order (1st Req.) Proposed YesYes

9. Presentation Outline • Motivation & Related work • Proposed method • Comparative performance evaluation • Conclusions

10. Proposed Method (1) • Novel Web log mining algorithm (WMo) • Apriori-like • Effective • Immune to noise • Considers high order dependencies • Efficient • Significant reduction in the number of candidates

11. Proposed Method (2) • Session (or transaction): A sequence of requests that occur in a specified time interval from each other [2] • Containment relationship addresses the 1st requirement (avoiding noise) • Example: T = A, X, B, Y, C X, Y noise S = A, B, C the pattern S is contained by T • Comment:With contiguous subsequences based only on support S (the pattern) will be missed.

12. Proposed Method (3) • Candidate generation respects the ordering of accesses in transactions. • Example: A,B B,A • Dramatic increase in the number of candidates • Exploits the site structure for pruning [7,8]

13. Proposed Method (4) Algorithm genCandidates(Lk, G) //Lk the set of large k-paths and G the graph begin foreach L=l1, …, lk, L  Lk { N+(lk) = {v| arc lk v  G} foreach v  N+(lk) { //apply modified apriori pruning if v  L and L’ = l2, …, lk,v Lk { C= l1, …, lk , v if ( S  C, S  L’  S  Lk ) insert C in the candidate-trie } } } end

14. Discussion • Sequential patterns [1] • Reduction when “customer-sequence” = “user-session” • Suffers from large number of candidates (by not considering the site structure) • Path Fragments [4] (containment relationship is performed with regular expressions and the “*” label ) • Focus on semantics (recommendation systems) • Prefetching: patterns are for system and not for human consumption • WMo focuses on efficiency/effectiveness rather on expressiveness (semantics)

15. Presentation Outline • Motivation & Related work • Proposed method • Comparative performance evaluation • Conclusions

16. Methodology • Synthetic (sample site with 1000 nodes) • Synthetic data generator (see the paper) • Modeling site nodes, site linkage, size of documents • Real data sets (see the paper) • Examine the impact of: • noise • order • client cache (see the paper) • efficiency

17. Accuracy w.r.t. noise

18. Usefulness w.r.t. noise

19. Traffic w.r.t. noise

20. Accuracy w.r.t. order

21. Usefulness w.r.t. order

22. Traffic w.r.t. order

23. Efficiency (see also [7,8])

24. Presentation Outline • Motivation & Related work • Proposed method • Comparative performance evaluation • Conclusions

25. Conclusions • Factors that influence Web Prefetching • Noise • Order • A new algorithm WMo was presented based on data mining • Compares favorably with previously proposed algorithms • WMo is an effective and efficient Web prefetching algorithm

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