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Finding Replicated Web Collections

Finding Replicated Web Collections. Junghoo Cho Narayanan Shivakumar Hector Garcia-Molina. Replication is common!. Statistics (Preview). More than 48% of pages have copies!. Reasons for replication. Actual replication Simple copying or Mirroring Apparent replication

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Finding Replicated Web Collections

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  1. Finding ReplicatedWeb Collections Junghoo Cho Narayanan Shivakumar Hector Garcia-Molina

  2. Replication is common!

  3. Statistics (Preview) More than 48% of pages have copies!

  4. Reasons for replication Actual replication • Simple copying or Mirroring Apparent replication • Aliases (multiple site names) • Symbolic links • Multiple mount points

  5. Challenges • Subgraph isomorphism: NP • Hundreds of millions of pages • Slight differences between copies

  6. Outline • Definitions • Web graph, collection • Identical collection • Similar collection • Algorithm • Applications • Results

  7. Web graph • Node: web page • Edge: link between pages • Node label: page content (excluding links)

  8. Identical web collection • Collection: induced subgraph • Identical collection: one-to-one (equi-size)

  9. Collection similarity • Coincides with intuitively similar collections • Computable similarity measure

  10. Collection similarity • Page content 

  11. Page content similarity • Fingerprint-based approach (chunking) • Shingles [Broders et al., 1997] • Sentence [Brin et al., 1995] • Word [Shivakumar et al., 1995] • Many interesting issues • Threshold value • Iceberg query

  12. Collection similarity • Link structure 

  13. Collection similarity • Size

  14. Collection similarity  • Size vs. Cardinality

  15. Growth strategy

  16. Ra a a a b b b |Ra| = Ls = Ld = |Rb| Essential property Ls: # of pages linked from Ld: # of pages linked to Rb

  17. Ra a a a Rb b b b |Ra|  Ls = Ld  |Rb| Essential property Ls: # of pages linked from Ld: # of pages linked to

  18. Algorithm • Based on the property we identified • Input: set of pages collected from web • Output: set of similar collections • Complexity: O(n log n)

  19. Rid Pid 1 10375 1 38950 1 14545 2 1026 2 18633 Algorithm • Step 1: Similar page identification (iceberg query) 25 million pages Fingerprint computation: 44 hours Replicated page computation: 10 hours web pages Step 1

  20. Rid Rid Pid Pid 1 1 10375 10375 1 1 38950 38950 1 1 14545 14545 2 2 1026 1026 Algorithm • Step 2: link structure check R1 Link R2 (Copy of R1) Pid Pid 1 2 1 3 2 6 2 10 Group by (R1.Rid, R2.Rid) Ra = |R1|, Ls = Count(R1.Rid), Ld = Count(R2.Rid), Rb = |R2|

  21. Algorithm • Step 3: S = {} For every (|Ra|, Ls, Ld, |Rb|) in step 2 If (|Ra| = Ls = Ld = |Rb|) S = S U {<Ra, Rb>} Union-Find(S) • Step 2-3: 10 hours

  22. Experiment • 25 widely replicated collections (cardinality: 5-10 copies, size: 50-1000 pages) => Total number of pages : 35,000 + 15,000 random pages • Result: 180 collections • 149 “good” collections • 31 “problem” collections

  23. Results

  24. Applications • Web crawling & archiving • Save network bandwidth • Save disk storage

  25. Application (web crawling) • Before experiment: 48% • With our technique: 13% crawledpages replicationinfo initialcrawl offline copydetection secondcrawl

  26. Applications (web search)

  27. Related work • Collection similarity • Altavista [Bharat et al., 1999] • Page similarity • COPS [Brin et al., 1995]: sentence • SCAM [Shivakumar et al., 1995]: word • Altavista [Broder et al., 1997]: shingle

  28. Summary • Computable similarity measure • Efficient replication-detection algorithm • Application to real-world problems

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