Big data analytics toolkit for business data based on social network analysis
University of New Brunswick
Social network analysis (SNA) measures the relationships and structures with a set of metrics by building graphs for capturing in uential actors and patterns. In this thesis, we investigate the SNA approaches for solving real-world business applications, and propose a general-purpose software system that combines big data analytics and social network analysis techniques. The system's work ow consists of data collection, graph generation, graph reuse, network property calculation, SNA result interpretation, and application integration. The system operations are executable in a Hadoop-based distributed cluster with high throughput on large-scale data. We evaluate our prototype system with a case study on stock network. The result shows that the system is capable of analyzing business data at-scale and using SNA approach to solve business problems.