GraphGen: Exploring Interesting Graphs in Relational Data

This video demonstrates our system for enabling graph analytics on top of relational datasets by allowing users to declaratively specify graph extraction tasks over relational databases, visually explore the extracted graphs, and write and execute graph algorithms over them, either directly using our vertex-centric framework or using existing graph libraries like the widely used NetworkX Python library.

The motivation for a system like this comes from the observation that analyzing interconnection structures among the data through the use of graph algorithms and graph analytics has been shown to provide tremendous value in many application domains. However, graphs are not the primary choice for how most data is currently stored, and users who want to employ graph analytics are forced to extract data from their data stores, construct the requisite graphs, and then use a specialized engine to write and execute their graph analysis tasks. This cumbersome and costly process not only raises barriers in using graph analytics, but also makes it hard to explore and identify hidden or implicit graphs in the data. We also found that unifying the extraction tasks and the graph algorithms enables significant optimizations that would not be possible otherwise.
(VLDB 2015 Demonstration)