A Novel Technique and Infrastructure for Online Analytics of Social Networks
The popularity of online social networks has grown at an exponential scale since they connect people all over the world enabling them to remain in touch with each other despite the geographical distance among them. These networks are a source of enormous amount of data that can be analyzed to make informed decisions on a variety of aspects, ranging from addressing societal problems to discovering potential security and terrorism-related events. Unfortunately, most efforts at analyzing such data tend to be offline, which may not be useful when actions must be taken in a timely fashion or the volume of generated data overwhelms computation, storage and networking resources. This Masters thesis investigates novel mechanisms for online processing of social network data. To validate the ideas, this thesis uses the LDBC social network benchmark provided as a challenge problem at the ACM Distributed and Event-based Systems (DEBS) conference, and demonstrates the techniques developed to address the first query from the challenge problem. The thesis will discuss the architectural choices we made in developing an online social network analysis solution.