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2005/11/09

2005/11/09. Continuous Queries in P2P Networks. Motivation. Motivation - Cardinality. How many people are currently listening POP music?. POP. POP. Classic. Result =2. Rock. Classic. Motivation – Top-K. What is the Top-2 Songs?. “Because of you” by Kelly Clarkson. “Shake It Off”

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2005/11/09

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

  2. Motivation

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

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

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

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

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

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

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

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

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

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

  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 SP1 P P SP2 SP4 P P SP3 P

  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 Node Type SP2 T1 SP3 T1 SP4 T2

  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 Rock Avril Lavigne Bon Jovi A: a1, a2 B: a3, a4 C: a1, b1, b2 a1 a2 a3 a4 b1 b2 b3

  21. Thank You!

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

  23. Example

  24. Example Ballet Kung Fu Swim

  25. Example Ballet Swim Kung Fu Back

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