Analysis of Predictive Spatio – Temporal Queries

1. Analysis of Predictive Spatio – Temporal Queries By, YUFEI TAO (City University of Hongkong, China) JIMENG SUN (Carnegie Mellon University, Pittsburgh) DIMITRIS PAPADIAS (Honkong University of Science & Technology, China) Presented By - Venu Madhav

2. Introduction Predictive Spatio – Temporal Queries ON Spatio – Temporal Databases

3. Outline of spatial database • What are Spatial Databases ? • Examples / Applications of Spatial Databases • Modeling of Spatial Databases

4. Querying a Spatial Database ? A sample query with fundamental spatial algebra • Spatial selection:returning those objects satisfying a spatial predicate with the query object Example: All big cities no more than 300Kms from Cleveland “SELECT cname FROM cities c WHERE dist(c.center, Cleveland.center) < 300 and c.pop > 500K”

5. Limitations… • It Assumes that queries and objects have zero velocity • The results of this database works only on static environment

6. Outline of Temporal Databases • Temporal database ? • Examples / Applications of Temporal Databases • Modeling of Temporal Databases

7. Applications of Spatio - Temporal Databases • Applications may involve objects with continuous motion • Navigational Systems • Applications dealing with discrete changes of and among objects • Flight Control • Applications may manage objects integrating continuous motion as well as changes of shape • Weather Forecast

8. An Abstract view of Spatio Temporal Databases

9. Keywords while Querying… • Selectivity • Cardinality • Histogram

10. Points to focus… • Spatio – Temporal Window Query (STWQ) • Spatio – Temporal k Nearest Neighbor (STkNN) • Spatio – Temporal Join (STJ)

11. Goal of Analysis… To Represent • Selectivity for Spatio - Temporal Window Query • Selectivity for Spatio – Temporal k Nearest Neighbor • Expected Nearest Distance for Spatio – Temporal Join

12. Problems Addressed… It covers • All common queries • All query/object mobility combinations • Moving object • Moving Query • Both • Arbitrary types of data (Points / Rectangles) in any dimensionality

13. Analysis to Predict the selectivity of STWQ • Reduce the problem to Single point data • Map the results to the Moving point data • Based on the results calculate for a Moving rectangle data

14. Extending the results to Non Uniform Data… • Incremental Spatio – Temporal histograms • Non Uniform Estimation with Spatio Temporal Histogram

15. Evaluation of Techniques • Prediction Accuracy • Computational Overhead • Performance deterioration along with time

16. Sample Spatio – Temporal Query • Select the farms that contain electricity poles • f | f є Farm Λ p є Pole Λ p.type = “electricity” Λ INSIDE(SP(p), SP(f)) Select f from f in Farm, p in poles Where p.kind = “electricity” and inside (p->sp, f->sp) • What was the area occupied by farms from 01/01/97 to 01/01/98? • ST_AREA (f, INTERVAL (01/01/97,01/01/98)) | f є Farm Select tuple (farm:f, area:f->st_area(interval(“01/01/97”,”01/01/98”))) from f in Farms

17. Future Works… • To invent alternative forecasting method for applications where velocity based prediction is unsuitable • Eg:- Exponential Smoothing

18. Thank you Questions ? - Venu Madhav