Pregel meets UnCAL: A systematic framework for transforming big graphs

Graph is a multi-purpose tool to represent many different kinds of data from tranditional datasets to social networks. At present, Pregel is a popular graph computation model to deal with big graphs up to billion vertices and trillion edges. However, Pregel programming model is very low-level and requires developers to write programs that are hard to maintain and need careful optimizations. In this thesis we are developing Gito, a systematic framework on top of Pregel to do transformations over big graphs. Transformations in Gito are expressed in a SQL-like language - UnQL - whose internal algebra is UnCAL, and then are compiled into Pregel code. In particular, in this paper, we show the feasibility of integrating UnCAL and Pregel, and propose a scalable Pregel-based computation for a subclass of UnCAL. Our preliminary results are encouraging and allow us to go further for a complete framework.