Social microblogging cube

Abstract

Microblogging sites have become a staple in our modern world. They provide the users with the ability to keep in touch with their contacts, using up of 140 characters in the case of Twitter sites. Responding to this emerging trend, it becomes critically important to interactively view and analyze the massive amount of microblogging data from different perspectives and with multiple granularities. In the area of Business intelligence, On-line analytical processing (OLAP) is a powerful primitive for data analysis. However, OLAP tools face major challenges in manipulating unstructured text such as microblogging data. In this paper, we suggest a new multidimensional model called "Microblogging Cube" to achieve OLAP techniques on unstructured microblogging data. It provides the possibility to analyze microblogs users and locations according to semantic, geographic and temporal axes. The semantic axe is defined by using the Open Directory Project (ODP) taxonomy. Different from existing classical multidimensional models, the measures in Microblogging Cube may vary depending on the aggregation levels. Further, in order to define the multiple granularities associated with microblogs users we propose a new process to extract the list of their communities.