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GBLENDER: Towards blending visual query formulation and query processing in graph databases

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I created the slides for presenting the following paper in the class:

http://dl.acm.org/citation.cfm?id=1807182

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GBLENDER: Towards blending visual query formulation and query processing in graph databases

  1. 1. GBLENDER: Towards Blending Visual Query Formulation and Query Processing in Graph DatabasesChangjiu Jin et al. at SIGMOD 2010Presented by: Abolfazl AsudehCSE 6339 – Spring 2013
  2. 2. Outline Motivation Goals and Contributions Preliminaries Indices Query Processing 2 4/12/2013
  3. 3. Motivation Formulating a graph(query)  “programming" skill 3 4/12/2013
  4. 4. Motivation Graph matching  Subgraph Isomorphism  NP- Complete 4 4/12/2013
  5. 5. Outline Motivation Goals and Contributions Preliminaries Indices Query Processing 5 4/12/2013
  6. 6. Goals and Contributions 1. Produce a visual interface  to formulate a query by clicking-and-dragging items 6 4/12/2013
  7. 7. Goals and Contributions Improve System Response Time They blend Visual Query Construction and Query Processing Use the latency of Query production to process current part of query.  Start query processing before the user hits the RUN button They assume user doesn’t make mistake during the query formulation (doesn’t UNDO) 7 4/12/2013
  8. 8. Challenges How to mix query construction and evaluation with MINIMAL DISK ACCESS How to Index the data How to make the pre-fetch processing transparent from the user 8 4/12/2013
  9. 9. Overview: Indexing action-aware frequent index (A2F)  Use Preprocessing action-aware infrequent index (A2I)  If the final query is infrequent, probe A2I 9 4/12/2013
  10. 10. Outline Motivation Goals and Contributions Preliminaries Indices Query Processing 10 4/12/2013
  11. 11. PRELIMINARIES Graph DB: A set of Graphs (V,E) Graph Fragment: a small sub-graph existing in graph databases or query graphs 11 4/12/2013
  12. 12. Example: Fragment samples in a chemicalcompound database12 4/12/2013
  13. 13. PRELIMINARIES: Frequent Fragment A fragment is frequent if its support is not less than ∣ ∣  ∣ ∣: the number of graphs in the data base e.g. if =0.1 and ∣ ∣=10000 13 4/12/2013
  14. 14. PRELIMINARIES: Infrequent Fragment A fragment is frequent if its support is less than ∣ ∣ e.g. if =0.1 and ∣ ∣=10000 14 4/12/2013
  15. 15. Discriminative Infrequent Fragment If all sub-graphs of a fragment are frequent but itself is infrequent √ 15 4/12/2013
  16. 16. Outline Motivation Goals and Contributions Preliminaries Indices Query Processing 16 4/12/2013
  17. 17. Indexing Because of the visual interface structure, the query size is grown by one in each step. The indexing has to (given a list of graphs that satisfy the fragment ′ in Step ) to support efficient strategy for identifyingthe graphs that match the fragment ′′ (generated at Step + 1) 17 4/12/2013
  18. 18. A2F index Being able to fit the matches in the memory , Frequent indices are divide to Memory-Resident and Disk-Resident Smaller frequent fragments are processed more frequently in various visual queries Smaller fragments have more matches If |g|< (threshold) it is saved in memory (MF-index) otherwise it is saved in the disk (DF-index) 18 4/12/2013
  19. 19. MF index structure - example19 4/12/2013
  20. 20. MF index structure - example20 4/12/2013
  21. 21. MF index structure - example21 4/12/2013
  22. 22. MF index structure - example22 4/12/2013
  23. 23. DF-Index23 4/12/2013
  24. 24. DF-Index24 4/12/2013
  25. 25. A2I index Just Index the discriminative infrequent graphs For other infrequent graphs use sub-graph isomorphism test over its discriminative infrequent 25 4/12/2013
  26. 26. Outline Motivation Goals and Contributions Preliminaries Indices Query Processing 26 4/12/2013
  27. 27. GBlender Algorithm27 4/12/2013
  28. 28. example28 4/12/2013
  29. 29. example29 4/12/2013
  30. 30. example30 4/12/2013
  31. 31. example31 4/12/2013
  32. 32. Thank you32 4/12/2013

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