1. Department of Computer Science 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 elmongui@cs.purdue.eduelmongui@cs.purdue.edu, mokbel@cs.umn.edu, aref@cs.purdue.edumokbel@cs.umn.eduaref@cs.purdue.edu

2. Hicham G. Elmongui 2 SSTD’05 Motivation  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, … Spatio-Temporal Database Server

3. Hicham G. Elmongui 3 SSTD’05 Motivation Spatio-Temporal Database Server How many cars on this freeway? Where is my nearest McDonald’s?

4. Hicham G. Elmongui 4 SSTD’05 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

5. Hicham G. Elmongui 5 SSTD’05 Motivation Go to work Return home Lunch hour Not just time makes a difference, but also space makes a difference Normalized Frequency

6. Hicham G. Elmongui 6 SSTD’05 ST-Histograms Histograms aware of the underlying space and time dimensions

7. Hicham G. Elmongui 7 SSTD’05 System Architecture Query Plan feedback Query Executor Query Optimizer ST-Histogram Manager Continuous Query Data

8. Hicham G. Elmongui 8 SSTD’05 Queries as Light Spots 6.25%

9. Hicham G. Elmongui 9 SSTD’05 Queries as Light Spots 6.98% 6.25% 6.01% 6.25% 6.01% Q1 6.25% 10%

10. Hicham G. Elmongui 10 SSTD’05 Queries as Light Spots 6.15% 15.04%9.84% 5.05% 6.01% 5.05% 6.01% Q2 6.01% 6.98% Q1 20%

11. Hicham G. Elmongui 11 SSTD’05 15.04%9.84%15.04%9.84% Queries as Light Spots 6.15% 5.05% Q1 Q2

12. Hicham G. Elmongui 12 SSTD’05 Queries as Light Spots 6.29% 4.22% 15.51% 3.24% 10.15% 5.21% 1% 5.05% Q2 15.04%9.84% 5.05% 6.15% Q1

13. Hicham G. Elmongui 13 SSTD’05 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

14. Hicham G. Elmongui 14 SSTD’05 ST-Histogram Construction/Refining  Initially  Selectivity of a query  Rate of a query to a grid cell

15. Hicham G. Elmongui 15 SSTD’05 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

16. Hicham G. Elmongui 16 SSTD’05 Estimation Relative Error vs. Query Size

17. Hicham G. Elmongui 17 SSTD’05 ST-Histogram’s Stability

18. Hicham G. Elmongui 18 SSTD’05 ST-Histogram vs. Random Sampling

19. Hicham G. Elmongui 19 SSTD’05 Related Work  Spatio-temporal histograms – Choi and Chung (SIGMOD’02) – Tao et al (ICDE’03) – Marios et al (SSDBM’03)  Sampling – Random Sampling – Venn Sampling (ICDS’05)

20. Hicham G. Elmongui 20 SSTD’05 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

21. Hicham G. Elmongui 21 SSTD’05 The END Thank You