Abstract
Graphs can represent various phenomena and are increasingly used to tackle complex problems. Among the challenges associated with graph processing is the ability to analyze and mine massive-scale graphs. While the massive scale is usually associated with distributed systems, the complex nature of graphs makes them an exception to the rule. Currently, most graph processing is performed within a single computer. In this research, we describe a solution at a conceptual level in the context of the Graph-Massivizer architecture. We use two approaches to provide graph analytics and querying functionalities at scale. First, we leverage graph sampling techniques to obtain relevant samples and avoid processing the whole graph. Second, we support heuristic and neural query execution engines. We envision an interface that will decide which queries to execute with a given engine, given constraints (e.g., execution time boundaries, exactness of results, energy saving requirements).
