Paper Info
Title
On Demand Memory Specialization for Distributed Graph Databases.
Abstract
In this paper, we propose the DN-tree that is a data structure to build lossy summaries of the frequent data access patterns of the queries in a distributed graph data management system. These compact representations allow us an efficient communication of the data structure in distributed systems. We exploit this data structure with a new \textit{Dynamic Data Partitioning} strategy (DYDAP) that assigns the portions of the graph according to historical data access patterns, and guarantees a small network communication and a computational load balance in distributed graph queries. This method is able to adapt dynamically to new workloads and evolve when the query distribution changes. Our experiments show that DYDAP yields a throughput up to an order of magnitude higher than previous methods based on cache specialization, in a variety of scenarios, and the average response time of the system is divided by two.
Year
Venue
Field
2013
CoRR
Data mining,Cache,Computer science,Theoretical computer science,Dynamic data,Throughput,Distributed computing,Data structure,Graph database,Load balancing (computing),Data management,Data access,Database
DocType
Volume
Citations 
Journal
abs/1310.4802
0
PageRank 
References 
Authors
0.34
17
5
Name
Order
Citations
PageRank
Xavier Martinez-Palau151.12
David Dominguez-Sal218916.35
Reza Akbarinia325425.77
Patrick Valduriez434591306.40
Josep-Lluis Larriba-Pey524521.70