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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
May 22, 2017
1
OTB
Vermelding
Research
onderdeel
Instituteorganisatie
for Housing, Urban and Mobility Studies
Section GIS Technology
Content
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Introduction of the subject
Generic model for moving object DMBS
Case I: static modeling
Case II: dynamic modeling
Conclusions and recommendations
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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
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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?
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Introduction
Vazirgiannis/Wolfson
•Map
•Moving object
•Trajectory
Characteristic:
•Developed for specific application
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Generic model
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Generic model
Base table:
CREATE TABLE mov_obj
(id, t, geometry) –-prim.key = id,t
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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
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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)
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Case I: Static modeling
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Case II: Dynamic modeling
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Case II: Dynamic modeling
Quality (Oracle def)
• Real-time simulation
4000000000
(growing table)
3500000000
• 2D and 3D
3000000000
2500000000
indexing tests
2D
2000000000
3D
1500000000
1000000000
500000000
13000
12000
11000
10000
90000
80000
70000
60000
50000
40000
30000
20000
10000
0
0
Number of objects
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Case II: Dynamic modeling
• 2D Query “SDO_RELATE”
• 3D Query “SDO_FILTER”
x1,y1,t1
x0,y0,t0
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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
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Future Research
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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
May 22, 2017
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