Shashi Shekhar Professor, Computer Science Department Teaching: Csci 8705: Topics in Scientific Databases Csci.

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Presentation transcript:

Shashi Shekhar Professor, Computer Science Department Teaching: Csci 8705: Topics in Scientific Databases Csci 8701: Database Research Csci 5708: Database II(spring 2003) Csci 4707: Database I(Fall 2002) Service (2002-3): Colloquium Computing Committee, Departmental Web-site Redesign Research Area: Spatial Databases, Spatial Data Mining Overview

Motivating Example Mogadishu, Somalia, 10/3/1993 Soldiers trapped by roadblocks No alternate evacuation routes Rescue team got lost in alleys having no planned route to crash site 18 Army Rangers and elite Delta Force soldiers killed, 73 wounded. “Black Hawk Down” ( Mark Bowden, Black Hawk Down: A Story of Modern War )

Motivating Example Facility and Base maps Demographics, Transportation Weather map, Plume Modeling ( Images from ) Homeland Defense Responding to a chem-bio attack GIS and SDBMS needed at every step! Gathering initial conditions Facility location Weather data from NWS Terrain maps (State of federal Govt.) Building geometry (City Govt.) Plume simulation using supercomputers Visualizing results – map, 3D graphics Response planning New research needs Q? What happens after plume simulation, visualization?

Homeland Defense: Chem-Bio Portfolio Hurrican Andrew, 1992 Traffic congestions on all highways Great confusions and chaos "We packed up Morgan City residents to evacuate in the a.m. on the day that Andrew hit coastal Louisiana, but in early afternoon the majority came back home. The traffic was so bad that they couldn't get through Lafayette." - Morgan City, Louisiana Mayor Tim Mott ( ) ( National Weather Services) (

Spatial Data poses new challenge for Computer Science –Parallel formulations for terrain visualization –Efficient storage methods, e.g. CCAM –Scalable routing algorithms for very large maps –Spatial Data Mining Spatial Database Research at U of M Nest locationsDistance to open water Vegetation durability Water depth

Spatial Data Mining A. Co-locations Spatial Outliers

Spatial Data Mining – Tele connection Patterns!