Complex networks such as human social groups, transportation networks and the World Wide Web are frequently represented as graphs. The goal of this project is to construct a new system that both conveniently and efficiently executes queries on collections of large graphs. To achieve this goal, the project develops data storage and processing techniques that can effectively use a server cluster.
The project's outcomes include (1) efficient data storage techniques that take advantage of commonalities between graphs, (2) methods that allow simple implementations of parallel graph processing solutions, (3) a framework that accelerates queries on multiple graphs by sharing computations across graphs, (4) techniques that store data on disks in a manner optimized for query execution, (5) methods that optimize query execution by appropriately distributing data over servers, and (6) techniques that mask server failures while balancing recovery speed and required cost.
This project has significant impacts on many application areas where it is critical to understand networks of various types. Examples include national security, social and political studies, transportation and marketing. Programming assignments and term projects based on this research are developed for undergraduate/graduate courses on data management systems, distributed systems and social network analysis. This project also offers research opportunities to both high school and minority students through summer programs at the University at Albany, State University of New York. The software, experimental data and research papers that result from this project will be disseminated through the project website (www.cs.albany.edu/~jhh/research/G_star/).
