Paper Info
Title
TurboFlux: A Fast Continuous Subgraph Matching System for Streaming Graph Data.
Abstract
A dynamic graph is defined by an initial graph and a graph update stream consisting of edge insertions and deletions. Identifying and monitoring critical patterns in the dynamic graph is important in various application domains such as fraud detection, cyber security, and emergency response. Given a dynamic data graph and a query graph, a continuous subgraph matching system reports positive matches for an edge insertion and reports negative matches for an edge deletion. Previous systems show significantly low throughput due to either repeated subgraph matching for each edge update or expensive overheads in maintaining enormous intermediate results. We present a fast continuous subgraph matching system called TurboFlux which provides high throughput over a fast graph update stream. TurboFlux employs a concise representation of intermediate results, and its execution model allows fast incremental maintenance. Our empirical evaluation shows that TurboFlux significantly outperforms existing competitors by up to six orders of magnitude.
Year
DOI
Venue
2018
10.1145/3183713.3196917
SIGMOD/PODS '18: International Conference on Management of Data Houston TX USA June, 2018
Field
DocType
ISSN
Graph,Data mining,Computer science,Incremental maintenance,Theoretical computer science,Dynamic data,Execution model,Throughput,Overhead (business)
Conference
0730-8078
ISBN
Citations 
PageRank 
978-1-4503-4703-7
5
0.40
References 
Authors
26
8
Name
Order
Citations
PageRank
Kyoungmin Kim162.10
In Seo250.73
Wook-Shin Han380557.85
Jeonghoon Lee4142.24
Sungpack Hong586433.20
Hassan Chafi6111861.11
Hyungyu Shin7302.79
Geonhwa Jeong882.13