Paper Info
Title
Arbor: Efficient Large-Scale Graph Data Computing Model
Abstract
Graph data is the default data organization mechanism used in large-scale Social Network Service (SNS) applications. Traditional graph data computing models are used to dig out useful hidden information inside the data. However, the ever growing data volume is adding more and more pressures. To retrieve and discover the information, the system has to introduce a larger number of data iterations. This makes the data analysis operations becoming slower. To speed up these operations on large-scale graph data, recent research works focus on developing efficient parallel iteration processing strategies. However, the synchronization requirements between successive iterations can severely jeopardize the effectiveness of parallel operations. In this paper, we propose a novel large-scale graph data processing model, Arbor, to address these issues. Arbor substitutes time-constrained synchronization operations with non-time-constrained control message transmissions to increase the degree of parallelism. Furthermore, it develops a new graph data organization format, which can not only save storage space, but also accelerate graph data processing operations. We compare Arbor with other graph processing models using a large-scale experimental graph data, and the results show that it outperforms the state-of-the-art systems.
Year
DOI
Venue
2013
10.1109/HPCC.and.EUC.2013.51
2013 IEEE 15TH INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING AND COMMUNICATIONS & 2013 IEEE INTERNATIONAL CONFERENCE ON EMBEDDED AND UBIQUITOUS COMPUTING (HPCC_EUC)
Keywords
Field
DocType
graph data, graph data processing, graph query, graph aggregation, graph analysis
Data modeling,Graph,Synchronization,Graph database,Data processing,Computer science,Degree of parallelism,Theoretical computer science,Graph (abstract data type),Distributed computing,Speedup
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Wei Zhou1106.30
Bo Li22610.93
Jizhong Han335554.72
Zhiyong Xu415615.97