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 November 27, 2018 OTB Research Institute for Housing, Urban and Mobility Studies Section GIS Technology

Content Introduction of the subject Generic model for moving object DMBS Case I: static modeling Case II: dynamic modeling Conclusions and recommendations November 27, 2018

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 November 27, 2018

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? November 27, 2018

Introduction Vazirgiannis/Wolfson Map Moving object Trajectory Characteristic: Developed for specific application November 27, 2018

Generic model November 27, 2018

Generic model Base table: CREATE TABLE mov_obj (id, t, geometry) –-prim.key = id,t November 27, 2018

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 November 27, 2018

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) November 27, 2018

Case I: Static modeling November 27, 2018

Case II: Dynamic modeling November 27, 2018

Case II: Dynamic modeling Real-time simulation (growing table) 2D and 3D indexing tests November 27, 2018

Case II: Dynamic modeling 2D Query “SDO_RELATE” 3D Query “SDO_FILTER” x1,y1,t1 x0,y0,t0 November 27, 2018

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 November 27, 2018

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 November 27, 2018