Paper Info
Title
ParallelGDB: a parallel graph database based on cache specialization
Abstract
The need for managing massive attributed graphs is becoming common in many areas such as recommendation systems, proteomics analysis, social network analysis or bibliographic analysis. This is making it necessary to move towards parallel systems that allow managing graph databases containing millions of vertices and edges. Previous work on distributed graph databases has focused on finding ways to partition the graph to reduce network traffic and improve execution time. However, partitioning a graph and keeping the information regarding the location of vertices might be unrealistic for massive graphs. In this paper, we propose Parallel-GDB, a new system based on specializing the local caches of any node in this system, providing a better cache hit ratio. ParallelGDB uses a random graph partitioning, avoiding complex partition methods based on the graph topology, that usually require managing extra data structures. This proposed system provides an efficient environment for distributed graph databases.
Year
DOI
Venue
2011
10.1145/2076623.2076643
IDEAS
Keywords
Field
DocType
massive graph,proteomics analysis,recommendation system,parallel system,proposed system,parallel graph database,cache specialization,new system,graph databases,bibliographic analysis,random graph partitioning,graph topology,data structure,parallel computer,database replication,social network analysis,random graph,requirements management,recommender system,graph database,parallel systems
Data structure,Graph database,Random graph,Computer science,Cache,Social network analysis,Theoretical computer science,Graph partition,Topological graph theory,Graph (abstract data type),Database,Distributed computing
Conference
Citations 
PageRank 
References 
8
0.47
10
Authors
4
Name
Order
Citations
PageRank
Luis Barguñó1211.50
Victor Muntés-Mulero220422.79
David Dominguez-Sal318916.35
Patrick Valduriez434591306.40