I created the slides for presenting the following paper in the class: http://dl.acm.org/citation.cfm?id=1807182

1. GBLENDER: Towards Blending Visual Query
Formulation and Query Processing in Graph
Databases
Changjiu Jin et al. at SIGMOD 2010
Presented by: Abolfazl Asudeh
CSE 6339 – Spring 2013

2. Outline
 Motivation
 Goals and Contributions
 Preliminaries
 Indices
 Query Processing
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3. Motivation
 Formulating a graph(query)  “programming" skill
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4. Motivation
 Graph matching  Subgraph Isomorphism  NP-
Complete
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5. Outline
 Motivation
 Goals and Contributions
 Preliminaries
 Indices
 Query Processing
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6. Goals and Contributions
 1. Produce a visual interface
 to formulate a query by clicking-and-dragging items
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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)
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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
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9. Overview: Indexing
 action-aware frequent index (A2F)
 Use Preprocessing
 action-aware infrequent index (A2I)
 If the final query is infrequent, probe A2I
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10. Outline
 Motivation
 Goals and Contributions
 Preliminaries
 Indices
 Query Processing
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11. PRELIMINARIES
 Graph DB: A set of Graphs (V,E)
 Graph Fragment: a small sub-graph existing in
graph databases or query graphs
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12. Example: Fragment samples in a chemical
compound database
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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
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14. PRELIMINARIES: Infrequent Fragment
 A fragment is frequent if its support is less than ∣
∣
 e.g. if =0.1 and ∣ ∣=10000
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15. Discriminative Infrequent Fragment
 If all sub-graphs of a fragment are frequent but itself
is infrequent
√
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16. Outline
 Motivation
 Goals and Contributions
 Preliminaries
 Indices
 Query Processing
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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)
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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)
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19. MF index structure - example
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20. MF index structure - example
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21. MF index structure - example
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22. MF index structure - example
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23. DF-Index
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24. DF-Index
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25. A2I index
 Just Index the discriminative infrequent graphs
 For other infrequent graphs use sub-graph
isomorphism test over its discriminative infrequent
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26. Outline
 Motivation
 Goals and Contributions
 Preliminaries
 Indices
 Query Processing
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27. GBlender Algorithm
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28. example
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29. example
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30. example
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31. example
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32. Thank you
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