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

GBLENDER: Towards Blending Visual Query Formulation and Query Processing in Graph

Databases

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Outline Motivation Goals and Contributions Preliminaries Indices Query Processing

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Motivation Formulating a graph(query) “programming"

skill

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Motivation Graph matching Subgraph Isomorphism NP-

Complete

Find the Matches

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Outline Motivation Goals and Contributions Preliminaries Indices Query Processing

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Goals and Contributions 1. Produce a visual interface

to formulate a query by clicking-and-dragging items

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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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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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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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Outline Motivation Goals and Contributions Preliminaries Indices Query Processing

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Graph DB: A set of Graphs (V,E)

PRELIMINARIES

Graph Fragment: a small sub-graph existing in graph databases or query graphs

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Example: Fragment samples in a chemical compound database

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PRELIMINARIES: Frequent Fragment A fragment 𝑔 is frequent if its support is not

less than ∣ ∣𝛼 𝒟 ∣ ∣𝒟 : the number of graphs in the data base

e.g. if =0.1 and ∣ ∣=10000𝛼 𝒟

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PRELIMINARIES: Infrequent Fragment A fragment 𝑔 is frequent if its support is less

than ∣ ∣𝛼 𝒟 e.g. if =0.1 and ∣ ∣=10000𝛼 𝒟

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Discriminative Infrequent Fragment If all sub-graphs of a fragment are frequent

but itself is infrequent

√

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Outline Motivation Goals and Contributions Preliminaries Indices Query Processing

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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 𝑔 𝑖efficient strategy for identifyingthe graphs that match the fragment ′′ (generated at 𝑔Step + 1)𝑖

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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|< 𝛽 (threshold) it is saved in memory (MF-

index) otherwise it is saved in the disk (DF-index)

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MF index structure - example

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MF index structure - example

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MF index structure - example

𝐷𝑒𝑙𝐼𝑑 ( 𝑓 𝑖 )=𝑆𝑢𝑝 ( 𝑓 𝑖 )− ¿ ∀ 𝑓 𝑗∨ 𝑓 𝑗 𝑖𝑠 h𝑐 𝑖𝑙𝑑𝑜𝑓 𝑓 𝑖𝑆𝑢𝑝 ( 𝑓 𝑗 )

|𝐷𝑒𝑙𝐼𝑑 ( 𝑓 9 )|=1200−1000=200

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MF index structure - example

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DF-Index

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DF-Index

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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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Outline Motivation Goals and Contributions Preliminaries Indices Query Processing

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GBlender Algorithm

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example

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example

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example

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example

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Thank you