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Moving objects in a geo-DBMS

This research paper explores the potential and performance of using the Oracle Spatial geo-DBMS to structure, index, query, and visualize spatiotemporal point clouds of moving objects. It discusses a generic model for moving object DBMS, static and dynamic modeling cases, and provides conclusions and recommendations.

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Moving objects in a geo-DBMS

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  1. Moving objects in a geo-DBMS Structuring, indexing, querying and visualizing moving objects in a spatiotemporal DBMS Heraklion, Agile 2004 Marco Baars*, Peter van Oosterom, Edward Verbree, Ben Gorte OTB Research Institute for Housing, Urban and Mobility Studies Section GIS Technology

  2. Content • Introduction of the subject • Generic model for moving object DMBS • Case I: static modeling • Case II: dynamic modeling • Conclusions and recommendations

  3. Introduction • Spatiotemporal DBMSs become popular • Traffic jams, cadastral issues • Database is remained to stay constant • New challenge for moving objects in database • Databases useful for • Large datasets • Easy querying • Consistency, security, redundancy, interoperability

  4. Introduction – Main question What is the potential and performance of the Oracle Spatial geo-DBMS to structure, index, query and visualize spatiotemporal point clouds of moving objects?

  5. Introduction Vazirgiannis/Wolfson • Map • Moving object • Trajectory Characteristic: • Developed for specific application

  6. Generic model

  7. Generic model Base table: CREATE TABLE mov_obj (id, t, geometry) –-prim.key = id,t

  8. Generic model • Base table with (materialized) views CREATE VIEW move_obj_succ AS SELECT t1.*, t2.t as next_t FROM mov_obj t1, mov_obj t2 WHERE t1.id=t2.id and t2.t=(select min(t) from move_obj where t>t1.t); • Flexible, consistent and fast

  9. Case I: Static modeling • Data (id,x,y,t) collected in advance • One “base table” with views in Oracle 9i Spatial • Querying based on operators and functions • Operator: sdo_relate • in where-clause • Index necessary • Function: sdo_geom.relate • Visualization (animation)

  10. Case I: Static modeling

  11. Case II: Dynamic modeling

  12. Case II: Dynamic modeling • Real-time simulation (growing table) • 2D and 3D indexing tests

  13. Case II: Dynamic modeling • 2D Query “SDO_RELATE” • 3D Query “SDO_FILTER” x1,y1,t1 x0,y0,t0

  14. Conclusions • Generic model is flexible, fast and consistent for static and dynamic point data • Choice for “base table” has to be made and depends on: • 2D or 3D queries • 2D or 3D index • Update time for index depends on covered area and number of objects in growing table

  15. Future Research • Prove Generic Model for 4D data (x,y,z,t) • Test the ArcIMS Tracking Server • Implementing more efficient R-tree • Test model for polylines, polygons or polyhedrons

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