Techniques and Systems for Large Dynamic Graphs

Abstract

Many applications regularly generate large graph data. Many of these graphs change dynamically, and analysis techniques for static graphs are not suitable in these cases. This thesis proposes an architecture to process and analyze dynamic graphs. It is based on a new computation model called Grab'n Fix. The architecture includes a novel distributed graph storage layer to support dynamic graph processing. These proposals were inspired by an extensive quantitative and qualitative analysis of existing graph analytics platform.