1. Spatio-Temporal Histograms
Hicham G. Elmongui* Mohamed F. Mokbel+ Walid G. Aref*
*Purdue University, Department of Computer Science
+University of Minnesota, Department of Computer Science

2. Motivation
Spatio-Temporal
Database Server
Infrastructure for keeping track and answering continuous queries on moving objects
Moving Queries / Moving Objects
Stationary Queries / Moving Objects
Moving Queries / Stationary Objects
Range Queries, KNN, …
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3. Motivation Spatio-Temporal Database Server
How many cars on this freeway?
Stationary range query
Moving data
Where is my nearest McDonald’s?
Moving KNN query
Stationary data
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4. Motivation
SELECT M.ID
FROM MovingObjects M
WHERE M.Type = “Truck”
INSIDE Area A;
We cannot collect statistics statically (e.g. histograms) and answer queries efficiently over an extended period of time
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5. Not just time makes a difference, but also space makes a difference
Motivation
Not just time makes a difference, but also space makes a difference
Normalized Frequency
Return home
Go to work
Lunch hour
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6. Histograms aware of the underlying space and time dimensions
ST-Histograms
Histograms aware of the underlying
space and time dimensions
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7. System Architecture ST-Histogram Manager feedback Query Executor Data
Plan
Query Optimizer
Continuous Query
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8. Queries as Light Spots 6.25% 6.25% 6.25% 6.25% 6.25% 6.25% 6.25% 6.25%
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9. Queries as Light Spots 10% 6.98% 6.25% 6.25% 6.98% 6.25% 6.01% 6.01%
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10. Queries as Light Spots 20% 6.98% 6.15% 6.98% 6.15% 6.01% 5.05% 6.01%
15.04%
6.01% Q2
6.01%
9.84%
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11. Queries as Light Spots 6.15% 6.15% 5.05% 5.05% Q1 6.15% 6.15% 5.05%
15.04% Q2
15.04%
9.84%
9.84%
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12. Queries as Light Spots 1% 6.15% 6.29% 6.29% 6.15% 5.21% 5.05% 5.21%
4.22%
5.05%
3.24% Q2
5.21%
5.05%
5.05%
5.21%
15.04%
15.51%
10.15%
9.84%
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13. Features of ST-Histograms
No computing capabilities assumed for the moving objects
Moving objects update their location periodically with the spatio-temporal database server
No patterns assumed for queries
Queries come and go anytime anywhere
Diskless spatio-temporal stream database server
Enable for adaptive query processing
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14. ST-Histogram Construction/Refining
Initially
Selectivity of a query
Rate of a query to a grid cell
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15. Experiments – Data Sets
Network-based Generator of Moving Objects (SSDBM’00, GeoInformatica’02)
Map of Greater Lafayette Area
Every MO updates its location every 10 sec
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16. Estimation Relative Error vs. Query Size
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17. ST-Histogram’s Stability
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18. ST-Histogram vs. Random Sampling
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19. Related Work Spatio-temporal histograms Sampling
Choi and Chung (SIGMOD’02)
Tao et al (ICDE’03)
Marios et al (SSDBM’03)
Sampling
Random Sampling
Venn Sampling (ICDS’05)
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20. Conclusion Aware of the underlying space and time dimensions
Implemented in PLACE (a spatio-temporal database server)
Efficient for spatio-temporal streaming applications
Refined upon feedback from query executor
Used in an online/offline mode
Accommodate periodicity in moving objects’ behavior
Enable adaptive query processing
Average relative error 8% for practical query sizes
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21. The END
Thank You
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