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DTSTART:20191103T020000
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RDATE:20201101T020000
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UID:calendar.392406.field_event_date.0@www.wright.edu
DTSTAMP:20260220T000913Z
CREATED:20200421T120857Z
DESCRIPTION:Webex Meeting Link:https://wright.webex.com/wright/j.php?MTID=m
 51395b3a53d9a13312664b06f1914484Committee:  Drs. Soon Chung\, Advisor\, Ni
 kolaos Bourbakis\, and Vincent Schmidt (AFRL)ABSTRACT:In the last decade\,
  the advent of social media and microblogging services have inevitably cha
 nged our world. These services produce vast amounts of streaming data\, an
 d one of the most important ways of analyzing and discovering interesting 
 trends in the streaming data is through clustering. In clustering streamin
 g data\, it is desirable to perform a single pass over incoming data\, suc
 h 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 t
 he data. In this research\, we have developed a new clustering system that
  clusters social media data based on their textual content and displays th
 e clusters and their locations on the map. It allows at-a-glance informati
 on to be displayed throughout the evolution of a crisis.Our system takes a
 dvantage of a text stream clustering algorithm\, which uses the two-phase 
 clustering process\, composed of micro-clustering and macro-clustering. Th
 e online micro-clustering phase incrementally creates micro-clusters\, cal
 led text droplets\, that represent enough information about topics occurri
 ng in the text stream. The off-line macro-clustering phase clusters micro-
 clusters for a user-specified time interval. Our system allows users to ch
 ange the macro-clustering algorithm interactively\, in order to evaluate t
 he micro-clustering results in a seamless manner and improve the overall c
 lustering result.  Our experiments demonstrated that the performance of ou
 r system is very scalable\; and it can be easily used by first responders 
 and crisis management personnel to quickly determine if a crisis is happen
 ing\, where it is concentrated\, and what resources are best to deploy to 
 the situation.
DTSTART;TZID=America/New_York:20200428T140000
DTEND;TZID=America/New_York:20200428T160000
LAST-MODIFIED:20200421T140548Z
LOCATION:Webex Virtual Meeting
SUMMARY:Masters Thesis Defense “Stream Clustering and Visualization of Geot
 agged Text Data for Crisis Management” By Nathaniel Crossman
URL;TYPE=URI:/events/masters-thesis-defense-%E2%80%9C
 stream-clustering-visualization-geotagged-text-data-crisis-management
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