Indexing Trajectory Data

1. Indexing Trajectory Data --Presented By SudheerChelluboina. Professor: Dr.Maggie Dunham

2. Contents Outline of Paper Introduction Index Structures

3. Outline of the Paper • Due to rapid increase in the use of location based services applications, large amount of location data of moving object is recorded. Because of that efficient indexing techniques are required to manage these large amounts of trajectory data. All index structures are focused on either indexing past, current and future locations. Every indexing structure or techniques discussed in this paper will make simpler indexing or it will increase the overall query processing performance.

4. Introduction • Trajectory: • It is the path of a moving object or body through space. • In general the position of the moving object is sampled at discrete times, and the series of straight lines connecting successive positions represents the movement of the object. These connected segments is called a Trajectory. • Trajectory Data: • When the moving object or body is continuously moving, and the update or change in location is recorded and that recorded data can be called as Trajectory Data. • Purpose of Indexing: • As the object moves its location constantly changes and there will large number of update operations. • Requires more space to consider this changing data. • Searching becomes the worst due to enormous data.

5. Introduction • Querying in trajectory databases is expensive and complex because of vast amount of data. • Two types of Queries: • Query about future positions for moving objects. • Query about historical positions for moving objects. • Trajectory based queries: • Topological queries • Navigational queries • Applications: • Fleet Management • Traffic Management • Mobile Communication • Environmental Monitoring System

6. Index Structures • R-Tree: • MBR’s are used as the data objects. • Index records in leaf node and points to the actual data. • Advantages: • It can handle any kind of data as trajectory. • Each line segment as one MBR. • Problems: • Line segments of each trajectory is not preserved so its take much time to retrieve the information about one trajectory.

7. Index Structures • STR-Tree: • It is an extension of R-Trees. • It has a insert/split algorithm. • The main idea is to keep the spatial closeness by preserving the trajectories line segments with single trajectory ID. • Advantages: • It can handle any kind of data as trajectory. • Each line segment as one MBR with an additional parameter as trajectory id. • Problems: • Even though the line segments of each trajectory is preserved, its take much time to retrieve the information about one trajectory.

8. Index Structures • TB-Tree: • It is an extension of STR-Trees, proposed to handle only the trajectory data. • Here leaf node can only contain the segments belonging to the same trajectory. • Left most node is the first segment of trajectory and last right node will be the last segment of trajectory. • Advantages: • It index size is low as compared to previous index structures. • Problems: • The main problem is the segment which is connected can be in the same leaf node or it may be in another leaf node, by which the information retrieved will be abnormal.

9. Index Structures • SETI-Tree: • It is a new indexing method build on R* trees. • Uses spatial partitions and sparse temporal indices. • First the input query is filtered spatially than produce candidate cells, on these cells spatial time index is built and outputs the data pages with trajectory ids. • Advantages: • It provides the efficient index structure scheme. • Problems: • In the algorithm, there is a limitation that in the refinement step there is a overhead in checking the temporal condition met by all the line segments from that data page.

10. Index Structures • TS2-Tree: • It is a novel indexing method for time specific queries called time specific similarity tree. • It is similar to the R-tree. Each leaf node in a tree stores a trajectory symbolic representation and also a pointer to the raw trajectory where the actual trajectory is stored. • The proposed structure is dynamic, that is the insert and delete operations are intermixed. • Advantages: • It provides the efficient organization for time specific queries on trajectory. • Problems: • Spatial threshold is the main thing to consider if it increases the query evaluation is expensive.

11. Lessons Learned • Different types of metrics used to analyze the indexes. • Given knowledge about the types of queries used. • It given me idea about how the trajectories will be and how it is stored and retrieved. • Main thing, it is used in my research for simulating the abnormal trajectories into normal data.

12. References • D. Pfoser, C. S. Jensen, and Y. Theodoridis, "Novel Approaches to the Indexing of Moving Object Trajectories," in Proc. 26th VLDB conf., 2000, pp. 395-406. • PetkoBakalov, Eamonn J. Keogh, Vassilis J. Tsotras: TS2-tree - an efficient similarity based organization for trajectory data. GIS 2007: 58 • Chakka, V. P.; Everspaugh, A. & Patel, J. M.Indexing Large Trajectory Data Sets With SETICIDR, 2003. • M. Hadjieleftheriou, G. Kollios, V. J. Tsotras, and D. Gunopulos. Efficient Indexing of Spatiotemporal Objects. In • Proc. of the Intl. Conf. on Extending Database Technology, EDBT, pages 251–268, Czech Republic, Mar. 2002.

13. Thank You….