1. AAU Novel Approaches to the Indexing of Moving Object Trajectories Presented by YuQing Zhang  Dieter Pfoser Christian S. Jensen Yannis Theodoridis

2. AAU 2 Contents Introduction 1 Moving Objects 2 Access Methods 3 Query Processing 4 Performance Comparison 5 Conclusion and Future Works 6 Strength and Weakness 7 Relate to My Project 8

3. AAU 3 Introduction  Objects in Real World  Space  Time  Preservation of trajectories  Line segments belong to the same trajectory  With respect to time  Access Methods Spatio-Temporal R-Tree (STR-tree) Methods Trajectory-Bundle Tree (TB-tree)

4. AAU 4 Moving Object  Trajectories  How to represent the movements of objects 1 Simply Store the position samples 2 Linear Interpolation

5. AAU 5 Moving Objects  Trajectories  Spatiotemporal Workspace  Temporal Dimension

6. AAU 6 Moving Objects  Queries  Coordinate-based Queries: point, range and nearest neighbor Example: Find all buses within AAU during 8.00AM - 9.00PM  Trajectory-based Queries  Topological Queries : important but expensive Example: Whether the BUS 17 entered AAU at 8.00AM  Navigation Queries: speed or heading Example: What is Bus 17’s top speed?  Combined Queries  Example: What were the trajectories of buses after they left AAU between 7am-8am today in the next hour? Querying trajectory identifier Selecting a segment Using a topological query Using derived information Select the trajectories Select the parts of each trajectory

7. AAU 7 Access Methods --- R tree  What is R-tree  Height balanced tree  Index records in leaf nodes  Pointer to actual data  Inefficiencies of R-tree  Dead Space  Hard to determine a line segment belongs to

8. AAU 8 Access Methods --- STR-tree  Difference with R-tree Insertion/split Strategy  Insertion Strategy  Not only spatial closeness, but also trajectory preservation  R-tree: least enlargement criterion  STR-tree: keep line segments belong to the same trajectory  Insertion Algorithm  FindNode()  Preservation parameter

9. AAU 9 Access Methods --- STR-tree  Spilt Strategy  General idea: put newer and thus more recent segments into new nodes  A node can contain: 1 Disconnect ed segments 2 Forward (backward) Connected segments 3 Bi- connected segments a Quadratic Spilt Algorithm b The disconnected segments are placed into the newly created node. c The most recent backward- connected segment is placed into the newly created node.

10. AAU 10 Access Methods --- TB-tree  Take a radical step  Concession: node overlap or spatial discrimination R-tree STR- tree line segments are parts of trajectories and this knowledge is only implicitly maintained TB- tree strictly preserves trajectories such that a leaf node only contains segments belonging to the same trajectory

11. AAU 11 Access Methods --- TB-tree  Insertion Algorithm Goal: cut the whole trajectory of a moving object into pieces

12. AAU 12 Access Methods --- TB-tree  Trajectory Preservation  A double linked List: preserves trajectory evolution simple solution to retrieve segments based on trajectory identifier

13. AAU 13 Query Processing  Combined Search in the R-tree and STR-tree  retrieve an initial set of segments based on a spatiotemporal range  extract partial trajectories  not retrieving the same trajectory twice 3 4

14. AAU 14 Query Processing  Combined Search in the TB-tree  the difference lies in how the partial trajectories are retrieved the linked list allow us to retrieve connected segments without searching  two possibilities: a connected segment can be in the same leaf node or in another node  Same: finding it is trivial  Another: follow the pointer

15. AAU 15 Performance comparison  Datasets  GSTD generator  Space Utilization and Index Size  Space Utilization: R-tree is the smallest  Index size: R-tree is the biggest TB-tree is smaller than that of STR-tree

16. AAU 16 Performance comparison  Summary Time slice Queries Trajectory- based Queries Combined Queries R-tree STR-tree√ TB-tree√Number of MO

17. AAU 17 Conclusion and Future Work  Conclusion  presents a set of pure spatiotemporal queries  trajectory-based queries  combined queries  Shortcomings of R-tree  STR-tree TB-tree STR-tree performance stays behind the TB-tree

18. AAU 18 Conclusion and Future Work  Future Work  Refine navigational and topological queries more detail.  Pay attention to some expensive spatial queries.  Investigating geometric shapes other than MBBs as approximations for moving objects’ trajectories deserves further research

19. AAU 19 Relate to my Project  My project  Range queries  Use Oracle  Maybe…  Give another view of questions

20. AAU 20 Strength and weakness  Strength  Describe each method quite clearly  Use some comparison  Some figures are helpful

21. AAU 21 Strong and weakness  Weakness  No Related Work introduction  Some parameters in some algorithms are ambiguous  Reader must have the knowledge of R-tree

22. AAU 22 Questions?

23. AAU Presented by YuQing Zhang