On querying historical evolving graph sequences
Chenghui Ren, Eric Lo, Ben Kao, Xinjie Zhu, Reynold C. K. Cheng · Proceedings of the VLDB Endowment · 2011
In many applications, information is best represented as graphs. In a dynamic world, information changes and so the graphs representing the information evolve with time. We propose that historical graph-structured data be maintained for analytical processing. We call a historical evolving graph sequence an EGS. We observe that in many applications, graphs of an EGS are large and numerous, and they often exhibit much redundancy among them. We study the problem of efficient query processing on an EGS and put forward a solution framework called FVF. Through extensive experiments on both real and synthetic datasets, we show that our FVF framework is highly efficient in EGS query processing.