On Querying Historical Evolving Graph Sequences
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. The authors propose that historical graph-structured data be maintained for analytical processing. They call a historical evolving graph sequence an EGS. They observe that in many applications, graphs of an EGS are large and numerous, and they often exhibit much redundancy among them. They 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, they show that their FVF framework is highly efficient in EGS query processing.