1. Dec. 13, 2003W 2 GIS2003@Rome Implementation and Evaluation of an Adaptive Neighborhood Information Retrieval System for Mobile Users Yoshiharu Ishikawa Yuichi Tsukamoto Hiroyuki Kitagawa University of Tsukuba, Japan

2. Overview Background and Overview Neighborhood Information Retrieval Method Design and Implementation of the Prototype Experimental Result Demo Conclusions and Future Work

3. Background Progress of digital cartography Development of GPS technologies Wide use of PDA and hand-held devices New types of information services: Providing neighborhood information to moving objects (people with PDAs, cars with navigation systems) considering their locations and trajectories

4. Motivating Example Neighborhood query: A user at point x wants to find nearby gas stations Typical approach: retrieve gas stations with their distances less than 200 meters from x x A spatial query based on the Euclidean distance

5. Our Idea (1) A Use of an ellipsoid region to represent a neighborhood query An ellipsoid region is computed based on the past/future trajectories A neighborhood query is specified as a spatial query with an ellipsoid distance

6. Our Idea (2) Neighborhood Info Retrieval System start point destination start pointdestination initial query parameters : data objects : sampled estimated positions of the moving object Sample positions are taken by unit-time basis At each sample position, a spatial query is generated The system perform queries continuously

7. Overview Background and Overview Neighborhood Information Retrieval Method Influence model of trajectory points Query derivation model Design and Implementation of the Prototype Experimental Result Demo Conclusions and Future Work

8. current point start point destination Representation of Location Information Locations of a moving object: Assumption: past/future trajectory points are given in unit-time basis : current time : estimated arrival time : departure time

9. Influence Model of Trajectory Points (1) The influence model sets the highest weight “1” on location information at time t =  +  (  unit times after the current time  ) The influence values decay exponentially towards past and future with parameters  and, respectively time Influence Value τ+σ τ+σ+1 τ+σ+2 τ+σ － 1 τ+σ － 2

10. Influence Model of Trajectory Points (2) current point start point destination Influence value for each point when  = 1     ’-1      ’-2 highest weight point since  = 1

11. Overview Background and Overview Neighborhood Information Retrieval Method Influence model of trajectory points Query derivation model Design and Implementation of the Prototype Experimental Result Conclusions and Future Work Background and Overview

12. Query Derivation Model Neighborhood queries for moving objects are issued to a spatial database A spatial query is fixed specifying query center q two models (cur, avg) distance function D three models (EU, OV, HB) query task range query and k-nn query q D

13. Derivation of Query Centers Model cur: set the point with the highest importance to the query center Model avg: weighted average based on influence values x 1 current position x  x +'+' cur x ++ avg

14. Derivation of Distance Function (1) Model EU: Euclidian distance-based model Model OV: ellipsoid distance-based model derive a distance matrix A that reflects the sample point [9]: extended Maharanobis distance adaptive, but not robust C is the weighted covariance matrix

15. Model HB: hybrid model integrates the benefits of EU and OV models incorporation of hybrid parameter Derivation of Distance Function (2) : unit matrix becomes an regular matrix regularization

16. Overview Background and Overview Neighborhood Information Retrieval Method Design and Implementation of the Prototype Experimental Result Demo Conclusions and Future Work

17. Query Generation & Execution ArcView GPS Route Calculation Module GUI Tracking Analyst map data & data items start point & destina- tion route info query invocation with event data parameter values query result query result GPS events map data position & map data route info System Architecture 外部 モジュール position data feeding

18. GUI Parameter Setup Dialog Box Result Table Map & Query Result View Current Position qualified items

19. Overview Background and Overview Neighborhood Information Retrieval Method Design and Implementation of the Prototype Experimental Result Demo Conclusions and Future Work

20. Experiment Set-up Query task continual k-nearest neighbor queries (k = 5) query is issued every 5 seconds Driving route Tsukuba city, Japan 5km driving Target data 200 items POI (Point Of Interest) along the route: gas station, shops, schools,...

21. Evaluation Measure Evaluation is based on (extended) precision scores for each movement point, precision score is calculated Ideal retrieval result set for each movement point, we have constructed "ideal" ranking of neighborhood data items simulates "ideal" user's behavior Precision formula Res(k): top-k objects ranked by the system Ans(p): top-p objects based on "ideal" ranking

22. Evaluation Results Overview of the Result On distance derivation models: EU < OV < HB ellipsoid distance-based approach is better in general On query center generation models: cur > avg selecting the current position as the query center is better than the averaging approach On past & future parameter settings moderate biased weighting (  = 0.4, = 0.8) was the best On hybrid parameters moderate setting ( = 0.9) was the best Recommendation Use HB & cur with appropriate parameter settings

23. Overview Background and Overview Neighborhood Information Retrieval Method Design and Implementation of the Prototype Experimental Result Demo Conclusions and Future Work

24. Demo

25. Overview Background and Overview Neighborhood Information Retrieval Method Design and Implementation of the Prototype Experimental Result Demo Conclusions and Future Work

26. Conclusions Neighborhood retrieval system for moving objects Based on ellipsoidal distance Introduction of influence decay model of trajectory points Proposal of spatial query generation models Prototype system ArcView & Tracking Analyist Experimental result precision-based evaluation Future work Use of more detailed information on road & spatial objects Use of large spatial datasets