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 Order (1st Req.) Noise (2nd Req.) DG No Yes PPM LBOT
Proposed Yes Yes

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

26. References
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