A new data economy is emerging, based on the analysis of distributed, heterogeneous, and complexly structured data sets. GRADES focuses on the problem of managing such data, specifically when data takes the form of graphs that connect many millions of nodes, and the worth of the data and its analysis is not only in the attribute values of these nodes, but in the way these nodes are connected. Specific application areas that exhibit the growing need for management of such graph shaped data include:

life science analytics, e.g., tracking relationships between illnesses, genes, and molecular compounds.
social network marketing, e.g., identifying influential speakers and trends propagating through a community.
digital forensics, e.g., analyzing the relationships between persons and entities for law enforcement purposes.
telecommunication network analysis, e.g., directed at fixing network bottlenecks and costing of network traffic.
digital publishing, e.g., enriching entities occurring in digital content with external data sources, and finding relationships among the entities.