Masters Thesis Defense “Stream Clustering and Visualization of Geotagged Text Data for Crisis Management” By Nathaniel Crossman
Webex Meeting Link:
https://wright.webex.com/wright/j.php?MTID=m51395b3a53d9a13312664b06f1914484
Committee: Drs. Soon Chung, Advisor, Nikolaos Bourbakis, and Vincent Schmidt (AFRL)
ABSTRACT:
In the last decade, the advent of social media and microblogging services have inevitably changed our world. These services produce vast amounts of streaming data, and one of the most important ways of analyzing and discovering interesting trends in the streaming data is through clustering. In clustering streaming data, it is desirable to perform a single pass over incoming data, such that we don' t need to process old data again, and the clustering model should evolve over time not to lose any important feature statistics of the data. In this research, we have developed a new clustering system that clusters social media data based on their textual content and displays the clusters and their locations on the map. It allows at-a-glance information to be displayed throughout the evolution of a crisis.
Our system takes advantage of a text stream clustering algorithm, which uses the two-phase clustering process, composed of micro-clustering and macro-clustering. The online micro-clustering phase incrementally creates micro-clusters, called text droplets, that represent enough information about topics occurring in the text stream. The off-line macro-clustering phase clusters micro-clusters for a user-specified time interval. Our system allows users to change the macro-clustering algorithm interactively, in order to evaluate the micro-clustering results in a seamless manner and improve the overall clustering result. Our experiments demonstrated that the performance of our system is very scalable; and it can be easily used by first responders and crisis management personnel to quickly determine if a crisis is happening, where it is concentrated, and what resources are best to deploy to the situation.
