Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Overview Shashi Shekhar Professor, Computer Science Department [email protected] www.cs.umn.edu/~shekhar 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 Motivating Example “Black Hawk Down” 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. ( Mark Bowden, Black Hawk Down: A Story of Modern War ) Motivating Example • Homeland Defense • Facility and Base maps 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 Weather map, Plume Modeling • New research needs • Q? What happens after plume simulation, visualization? Demographics, Transportation ( Images from www.fortune.com ) Homeland Defense: Chem-Bio Portfolio "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 ( http://i49south.com/hurricane.htm ) ( National Weather Services) Hurrican Andrew, 1992 Traffic congestions on all highways Great confusions and chaos ( www.washingtonpost.com) Spatial Database Research at U of M • 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 Nest locations Vegetation durability Distance to open water Water depth Spatial Data Mining • Co-locations • Spatial Outliers • A. Spatial Data Mining – Tele connection Patterns!