Construct rough approximation based on GAE

Recently cloud computing has emerged as a new paradigm which focuses on web-scale problems, large data centers, multiple models of computing and highly-interactive web applications. It is high available and scalable for distributed and parallel data storage and computing based on a large amount of cheap PCs. As the representative product, Google app engine (GAE), which acts a platform as a service (PaaS) cloud computing platform, mainly contains Google File System (GFS) and MapReduce programming model for massive data process. This paper analyses GAE from the point of Granular computing (GrC) and explain why it is suitable for massive data mining. Further we present an example of how to use it to construct neighborhoods of rough set and compute lower and upper approximations accurately and strictly.