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.
@article{arxiv.1310.4802,
title = {On Demand Memory Specialization for Distributed Graph Databases},
author = {Xavier Martinez-Palau and David Dominguez-Sal and Reza Akbarinia and Patrick Valduriez and Josep Lluís Larriba-Pey},
journal= {arXiv preprint arXiv:1310.4802},
year = {2013}
}