1. Dieter Pfoser, LBS Workshop1 Issues in the Management of Moving Point Objects Dieter Pfoser Nykredit Center for Database Research Aalborg University, Denmark pfoser@cs.auc.dk

2. Dieter Pfoser, LBS Workshop2 Talk Outline n Motivation n Moving Point Objects, Data, and Queries n Query Processing – Access Methods – Infrastructure n Uncertainty n Future Work, Trajectory Mining

3. Dieter Pfoser, LBS Workshop3 Motivation n Spatiotemporal applications deal with spatial phenomena changing over time n Emerging applications that handle moving point objects – fleet management – traffic management – mobile communication – environmental monitoring system New type of data that stems from recording the movement in time  trajectories

4. Dieter Pfoser, LBS Workshop4 Data Sampling the position of a moving object at time points and interpolating in between samples

5. Dieter Pfoser, LBS Workshop5 Data (2) Geometrical representation in 3D space (2D spatial + 1D temporal) The resulting line segments comprise a polyline in 3D, the trajectory of the moving point object.

6. Dieter Pfoser, LBS Workshop6 Generate_Spatio_Temporal_ Data (GSTD) n Fast moving objects (heading)

7. Dieter Pfoser, LBS Workshop7 Data Particularities Typical: Dataset  Objects Now: Dataset  Trajectories  Segments n By adding time, we can derive further information, e.g., speed of the object

8. Dieter Pfoser, LBS Workshop8 Queries n Coordinate-based Queries – point, – range, and – nearest-neighbor queries n Trajectory-based Queries – topological queries: enter, leave, cross, and bypass and – navigational queries using derived information, e.g., speed and heading n Combined Queries n Join

9. Dieter Pfoser, LBS Workshop9 Topological Queries n Trajectories enter, leave, cross, stay within, or bypass a given spatiotemporal range Signature: range  {trajectories}  {trajectories} leave enter cross bypass stays within

10. Dieter Pfoser, LBS Workshop10 Navigational Queries n Considering derived information in query processing, e.g., speed (top and average), heading, traveled distance, covered area, etc. Signature: range  {trajectories}  int|real|bool

11. Dieter Pfoser, LBS Workshop11 … where are we? n Motivation n Moving Point Objects, Data, and Queries n Access Methods n Movement Types n Uncertainty n Future Work, Trajectory Mining

12. Dieter Pfoser, LBS Workshop12 Access Methods n We store segments in the index n R-tree – approximating bounding box, orientation of the segment, and trajectory id n STR-tree (SpatioTemporal R-tree) – spatial discrimination, i.e., preserve spatial proximity of segments in a leaf node and – trajectory preservation, i.e., segments belonging to the same trajectory (proximity with respect to trajectories)

13. Dieter Pfoser, LBS Workshop13 Access Methods (2) n TB-tree (Trajectory Bundle) – strict trajectory preservation, i.e., one leaf node contains segments of only one trajectory – neglecting spatial discrimination with respect to the two spatial dimensions – given temporal discrimination based on transaction time properties of the data

14. Dieter Pfoser, LBS Workshop14 Performance Studies n Datasets – synthetic datasets generated using GSTD n Experiments – Index size – Range Queries – Combined Queries n Parameter – number of moving objects – time horizon – object speed

15. Dieter Pfoser, LBS Workshop15 Index Size R-treeSTR-treeTB-tree Index Size ~ 95 KB per object ~ 57 KB per object ~ 51 KB per object Space Utilization 55%-60%~100% n STR and TB-tree: smaller index through higher space utilization (“packing” of nodes) n TB-tree vs. STR-tree: trajectory id stored per leaf node gives smaller node size

16. Dieter Pfoser, LBS Workshop16 … where are we? n Motivation n Moving Point Objects, Data, and Queries n Access Methods n Movement Types n Uncertainty n Future Work, Trajectory Mining

17. Dieter Pfoser, LBS Workshop17 Query Processing and Infrastructure n Three different movement types – unconstrained movement (vessels at sea) – constrained movement, infrastructure (cars, pedestrians) – movement in networks (trains and, in some cases, cars)

18. Dieter Pfoser, LBS Workshop18 Constrained vs. Unconstrained Movement Objects can be obstructed in their movement  Infrastructure, e.g., buildings, pedestrian zones (cars), roads (pedestrians)

19. Dieter Pfoser, LBS Workshop19 Uncertainty n Errors related to trajectory representation – Measurement error: every measuring technique has an associated error, expressed by a positional probability function – Sampling error: recording a continuous movement at time points introduces uncertainty in between samples n Exploiting information on the movement reduces error – maximum speed – map matching, dead reckonin

20. Dieter Pfoser, LBS Workshop20 Future Work n A detailed view – too many angles for future work for all of them to be mentioned here... – e.g., more complex queries, refining query processing algorithms, different approximation techniques, etc. n A global perspective – considering different kinds of data, e.g., networks – exploring uncertainty further (real data?) – applying the presented approach in a real-world application context

21. Dieter Pfoser, LBS Workshop21 Putting the Stuff to Use... n Traffic – increased number of vehicles – demand for online information n Know more about current traffic condition n Prediction about future traffic conditions and prediction on the fly

22. Dieter Pfoser, LBS Workshop22 Ontologies Trajectory Ontology Traffic Sensor Vehicle Tracking Imagery (aerial photographs) Observations

23. Dieter Pfoser, LBS Workshop23 Objectives (1) n Build a tool (Kinesis-Miner) 1. Extract knowledge about moving vehicles – busy routes in Aalborg at 15:00 – number of vehicles now heading towards Nytorv – roads in which cars speed up during Saturday nights 2. Predict troublesome situations – traffic jams 3. Provide options – alternative routes for 15:00 through Aalborg – suggested routes through Hamburg over the weekend

24. Dieter Pfoser, LBS Workshop24 Spatiotemporal Data Mining n Spatiotemporal Data Mining: knowledge extraction from large spatiotemporal repositories in order to recognize behavioural trends and spatial patterns for prediction purposes – What is the connection between traffic jams in Aalborg and the time of the day? – What are the locations of accidents on highways and accidents on minor roads?

25. Dieter Pfoser, LBS Workshop25 Mining Methods n Classification – roads with many traffic violations  dangerous roads n Characterization – trajectory heading to town and high speed  in city within an hour n Clustering – many vehicles close to each other  possible traffic jam n Association – number of vehicles in town and the time of the day

26. Dieter Pfoser, LBS Workshop26 Deliverables (1) n Mapping of all sorts of data to trajectory ontology n Kinesis-Miner for knowledge extraction n Kinesis language for support and interface n Algorithms to support the mining methods

27. Dieter Pfoser, LBS Workshop27 Deliverables (2)

28. Dieter Pfoser, LBS Workshop28 Application Areas n Everything that moves!! n Traffic (cars, ships,..) n Users with mobile phones – How many users in Aalborg over Saturday night – Which cells will be overloaded