Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Strip trees: a hierarchical representation for curves
Communications of the ACM
Novel Approaches in Query Processing for Moving Object Trajectories
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
MDM '01 Proceedings of the Second International Conference on Mobile Data Management
Query Processing for Moving Objects with Space-Time Grid Storage Model
MDM '02 Proceedings of the Third International Conference on Mobile Data Management
SEB-tree: An Approach to Index Continuously Moving Objects
MDM '03 Proceedings of the 4th International Conference on Mobile Data Management
Indexing of network constrained moving objects
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
Indexing the Trajectories of Moving Objects in Networks*
Geoinformatica
Indexing Spatio-Temporal Trajectories with Efficient Polynomial Approximations
IEEE Transactions on Knowledge and Data Engineering
Computational Geometry: Algorithms and Applications
Computational Geometry: Algorithms and Applications
Using B+-trees for processing of line segments in large spatial databases
Journal of Intelligent Information Systems
Studying (non-planar) road networks through an algorithmic lens
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Efficient search of moving objects on a planar graph
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Indexing the Past, Present and Future Positions of Moving Objects on Fixed Networks
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 04
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We discuss a spatio-temporal data structure to index objects moving on a graph. It is designed to efficiently answer rectangle R plus time instance and time interval queries about the past positions of moving objects. Such data structures are useful, for example, when searching which vehicles moving on a road network in specific areas at specific times. Unlike other data structures that use R-trees to index bounding boxes of moving object trajectories, our data structure indexes oriented line segments representing positions of moving objects at different times. For n moving object instances (unique entries of moving objects) on a graph with E edges, we show that $O(log_2 E + |L| log_2^2(n/E) + k)$ time is required to answer a rectangle R plus time interval query, for |L| the number of edges intersected by R and k the number of line segments containing moving object instances in range. Space O(n2/E+E) is required in the worst case to store n moving object instances in E ordered polyline trees. Space Ω(n+E) is required to store the history of all n moving object instances.