I/O-Scalable Bregman Co-clustering and Its Application to the Analysis of Social Media

Date of Submission: 
October 10, 2011
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Adoption of social media has experienced explosive growth in recent years, and this trend appears likely to continue. A natural consequence has been the creation of vast quantities of data being generated by social media applications, and hence increased interest from the database community. This data is also providing unique opportunities to understand the sociological and psychological aspects, human interaction, and media production/consumption, and hence the growth in areas such as user modeling, behavior analysis, and social network analysis, which together is being labeled as the emerging area of Computational Social Science (CSS) [37, 59]. These new types of data analysis are leading to the introduction of new computational techniques, e.g. p* modeling, ERGMs [62], co-clustering [6], etc. This paper focuses on a scalable implementation of Bregman co-clustering algorithm and its application to social media analysis. Bregman co-clustering algorithm performs two-way clustering and is theoretically scalable while we discuss an OLAP based implementation to achieve this goal. Principally, we demonstrate how aggregations required by the algorithm can be mapped naturally to summary statistics computed by an OLAP engine and stored in data cubes. Our OLAP based implementation of the algorithm is able to handle large-scale datasets, i.e. datasets that are too large for main memory based implementations. Further, we explore the suitability of the relational model for modeling social media data. Specifically, we argue that data cubes and the star schema are well suited for managing social media data. Our research is a step toward the increasing interest the research community has in connecting three research areas, namely database, data mining, and social media analysis.