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This analysis explores predictive spatio-temporal queries and their applications in spatial and temporal databases, presented by Venu Madhav. It examines the limitations and modeling of these databases while highlighting core concepts like spatio-temporal window queries, k-nearest neighbors, and spatio-temporal joins. Furthermore, the paper addresses the selectivity and expected nearest distance for common queries involving moving objects and arbitrary data types in various dimensionalities. Future work will focus on innovative forecasting techniques for dynamic environments where velocity-based predictions are inadequate.
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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
Introduction Predictive Spatio – Temporal Queries ON Spatio – Temporal Databases
Outline of spatial database • What are Spatial Databases ? • Examples / Applications of Spatial Databases • Modeling of Spatial Databases
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”
Limitations… • It Assumes that queries and objects have zero velocity • The results of this database works only on static environment
Outline of Temporal Databases • Temporal database ? • Examples / Applications of Temporal Databases • Modeling of Temporal Databases
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
Keywords while Querying… • Selectivity • Cardinality • Histogram
Points to focus… • Spatio – Temporal Window Query (STWQ) • Spatio – Temporal k Nearest Neighbor (STkNN) • Spatio – Temporal Join (STJ)
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
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
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
Extending the results to Non Uniform Data… • Incremental Spatio – Temporal histograms • Non Uniform Estimation with Spatio Temporal Histogram
Evaluation of Techniques • Prediction Accuracy • Computational Overhead • Performance deterioration along with time
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
Future Works… • To invent alternative forecasting method for applications where velocity based prediction is unsuitable • Eg:- Exponential Smoothing
Thank you Questions ? - Venu Madhav
