Supervisor: Dr Athen Ma
The big data phenomena has generated information in an unprecedented rate in terms of its volume, variety and velocity, providing opportunities to curate meaningful information that can be used to address local problems and questions at larger spatial and temporal scales. Online systems (or applications) often have the ability to capture our behaviour with a specific focus; for example, social networking applications such as Facebook provides information on friendships and interactions among friends; however, in reality, our cyber presence is richer and much more heterogeneous due to the vast number of application domains, and we are still at an embryonic stage in understanding the underlying patterns. Interestingly, Information from one context can be useful to make sense in another context. For example, links in one social network (Facebook) can be indicative of interactions in another social network (Pinterest/last.fm), images in Pinterest could link to sales in ebay, and opinions on twitter could have an influence on brand perception. Here, we aim to develop novel techniques to identify interrelated patterns from data across different systems and examine the underlying dependency, which will help in devising strategies in converting data into useful information using a reduced number of data sources but yet providing information that will be suitable for cross-platform exploitation. In addition, knowledge on the co-variance between systems will provide an insight into areas that are unique to their respective systems, and information of this kind can be used to address local issues or enhance local decision.