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DTSTART:20181104T020000
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UID:calendar.347556.field_event_date.0@www.wright.edu
DTSTAMP:20260219T214502Z
CREATED:20181120T221318Z
DESCRIPTION:Committee:  Drs. Krishnaprasad Thirunarayan\, Advisor\, Amit Sh
 eth\, and Valerie Shalin (Psychology)ABSTRACT:With the surge in digital in
 formation systems\, there is a humongous amount of data from various sourc
 es that can be synthesized to produce intelligent systems.  These intellig
 ent systems can provide functionality that caters the user with related an
 d reliable information\, preserving low-level details and surfacing essent
 ial information for better decision making.Therefore\, we employ multi-mod
 al data (i.e.\, unstructured text\, gazetteers\, and imagery) for an aggre
 gate level of analysis and location-centric demand/request matching in the
  context of disaster relief. After classifying the Need expressed in a twe
 et (the WHAT)\, we leverage OpenStreetMap to geolocate thatNeedon a comput
 ationally accessible map of the local terrain (the WHERE) populated with l
 ocation features such as hospitals and housing. Further\, our novel use of
  flood mapping based on satellite images of the affected area supports the
  elimination of candidate resources that are not accessible by road transp
 ortation. The resulting map-based visualization serves two levels of users
 .  A community-level user (first-responders) can visualize aggregated summ
 ary of a selected geographical area and an individual level user can ident
 ify current needs and available resources in their geographic proximity. A
 dditionally\, our pluggable modularized pipeline (DisasterRecord)readily a
 llows for extending the functionality and the addition of any number of la
 yers overlaid on top of the map. The integration of disaster-related tweet
 s\, imagery and pre-existing knowledge-base resources (gazetteers) reduce 
 decision-making latency and enhance resiliency by assisting decision-maker
 s and first responders for relief effort coordination.
DTSTART;TZID=America/New_York:20181129T134500
DTEND;TZID=America/New_York:20181129T154500
LAST-MODIFIED:20181121T144504Z
LOCATION:366 Joshi
SUMMARY:Masters Thesis Defense “Multi-scale and multi-modal streaming data 
 aggregation and processing for decision support during natural disasters” 
 By Shruti Kar
URL;TYPE=URI:/events/masters-thesis-defense-%E2%80%9C
 multi-scale-multi-modal-streaming-data-aggregation-processing
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