Data and Visual Analytics for Emerging Databases

With advances in technology, high volumes of valuable data of different veracity can be generated at a high velocity in wide varieties of data sources in various real-life applications. Examples of these big data include social media data. As a popular data mining tasks, frequent pattern mining discovers implicit, previously unknown and potentially useful knowledge in the form of sets of frequently co-occurring items or events. Many existing data mining algorithms return to users with long textual lists of frequent patterns, which may not be easily comprehensible. Given a picture is worth a thousand words, having a visual means for humans to interact with computers would be beneficial. In this paper, we present a framework for data and visual analytics for emerging databases. In particular, our data and visual analytic framework focuses on mining and analyzing social media data, as well as visualizing the mined ` following' patterns that reveal those groups of frequently followed social entities in a social network.