1. 2005/11/09 Continuous Queries in P2P Networks

2. Motivation

3. Motivation - Cardinality How many people are currently listening POP music? Rock Classic POP Result =2

4. Motivation – Top-K “Because of you” by Kelly Clarkson What is the Top-2 Songs? “Because of you” by Kelly Clarkson “Wake Me Up When September Ends” by Green Day “Wake Me Up When September Ends” by Green Day “Shake It Off” by Mariah Carey

5. Motivation – Top-K “Because of you” by Kelly Clarkson What is the Top-2 Songs? “Because of you” by Kelly Clarkson “Wake Me Up When September Ends” by Green Day “Wake Me Up When September Ends” by Green Day “Shake It Off” by Mariah Carey

6. Motivation – Social Network Rock Classic POP I want to make friends who have similar interests as I have

7. Motivation – Social Network Rock Classic POP I want to make friends who have similar interests as I have

8. Motivation – Social Network Rock Classic POP I want to make friends who have similar interests as I have

9. Motivation – Social Network Rock Classic POP I want to make friends who have similar interests as I have

10. Motivation - Ontology Search “Shake Your Bon Bon” by Ricky Martin

11. Motivation - Ontology Search “Shake Your Bon Bon” by Ricky Martin Sorry, I have none I have “Shake Your Bon Bon” Dude, get away from me I do have this song but its name is “Martin’s Ass”

12. Motivation - Ontology Search “Shake Your Bon Bon” by Ricky Martin The result of exact matching = 1 By we want to get the actual result 2 Ontolog y

13. Continuous Queries Cardinality Top-K Social network Ontology

14. Cardinality The state-of-the-art  Aggregation in P2P system Montresor et al. DSN'04  Epidemic, adaptive  Aggregation with streaming data Das et al. VLDB04  Global knowledge of frequent items

15. Cardinality (Cont’d.) As far as we know, there is no study focus on this issue in P2P environment with streaming data Progress after our summer presentation  Use statistics distribution to estimate changes

16. Top-K The state-of-the-art  Traditional Top-K problem Combine information for database systems [Fagin]  Approximation on data streams Proposed for data streams under guaranteed tolerance, but can’t be deployed to P2P.  Super peer based Top-k in P2P Iteratively query  Locality was mentioned, but the method is straight-forward

17. Top-K (Cont’d.) Assumptions  Based on superpeer-structured P2P networks due to the heterogeneity of peers  Each super-peer would maintain a routing table and some metadata for Top-k query P P P P P P P P SP 2 SP 1 SP 3 SP 4

18. Top-K (Cont’d.) Problem  Query whom? →Locality space Interest  Query routing (routing table)  Reduce the size of table  Minimize the communication peer’s update occurs load balance T2T2 SP 4 T1T1 SP 3 T1T1 SP 2 TypeNode

19. Social network + Ontology The state-of-the-art  Similarity computing Compute the similarity between two nodes/peers, and fix-point scores will be assigned 

20. Social network + Ontology  Our thoughts  Using some hierarchical domain structures  Ontology / classification Avril Lavigne A: a1, a2 B: a3, a4 C: a1, b1, b2 Rock Bon Jovi a1a2a3a4b1b2b3

21. Thank You!

22. What is ontology A formal, explicit specification of a shared conceptualization Object attribute Object relation Class Back

23. Example

24. Ballet Swim Kung Fu

25. Example Ballet Swim Kung Fu Back