MSSG: A Framework for Massive-Scale Semantic Graphs

This paper presents a middleware framework for storing, accessing and analyzing massive-scale semantic graphs. The framework, MSSG, targets scale-free semantic graphs with O(1012 ) (trillion) vertices and edges. Here, we present the overall architectural design of the framework, as well as a prototype implementation for cluster architectures. The sheer size of these massive-scale semantic graphs prohibits storing the entire graph in memory even on medium- to large-scale parallel architectures. We therefore propose a new graph database, grDB, for the efficient storage and retrieval of large scale-free semantic graphs on secondary storage. This new database supports the efficient and scalable execution of parallel out-of-core graph algorithms which are essential for analyzing semantic graphs of massive size. We have also developed a parallel out-of-core breadth-first search algorithm for performance study. To the best of our knowledge, it is the first of such algorithms reported in ...

• Type: Conference
• Year: 2006
• Where: CLUSTER
• Authors: Timothy D. R. Hartley, Ümit V. Çatalyürek, Füsun Özgüner, Andy Yoo, Scott Kohn, Keith W. Henderson
• Keywords: CLUSTER 2006, Cluster Computing, Massive-scale Semantic Graphs, Scale-free Semantic Graphs, Semantic Graphs