The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Survey of Spatio-Temporal Databases
Geoinformatica
Novel Approaches in Query Processing for Moving Object Trajectories
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
MV3R-Tree: A Spatio-Temporal Access Method for Timestamp and Interval Queries
Proceedings of the 27th International Conference on Very Large Data Bases
STAR-Tree: An Efficient Self-Adjusting Index for Moving Objects
ALENEX '02 Revised Papers from the 4th International Workshop on Algorithm Engineering and Experiments
SEB-tree: An Approach to Index Continuously Moving Objects
MDM '03 Proceedings of the 4th International Conference on Mobile Data Management
Shape-Based Similarity Query for Trajectory of Mobile Objects
MDM '03 Proceedings of the 4th International Conference on Mobile Data Management
Indexing of Moving Objects for Location-Based Services
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Real-Time Video Analysis on an Embedded Smart Camera for Traffic Surveillance
RTAS '04 Proceedings of the 10th IEEE Real-Time and Embedded Technology and Applications Symposium
STRIPES: an efficient index for predicted trajectories
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
SEA-CNN: Scalable Processing of Continuous K-Nearest Neighbor Queries in Spatio-temporal Databases
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Indexing mobile objects using dual transformations
The VLDB Journal — The International Journal on Very Large Data Bases
Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Robust B+-Tree-Based Indexing of Moving Objects
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
The TPR*-tree: an optimized spatio-temporal access method for predictive queries
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Query and update efficient B+-tree based indexing of moving objects
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Efficient and compact indexing structure for processing of spatial queries in line-based databases
Data & Knowledge Engineering
The Bdual-Tree: indexing moving objects by space filling curves in the dual space
The VLDB Journal — The International Journal on Very Large Data Bases
Adaptive indexing of moving objects with highly variable update frequencies
Journal of Computer Science and Technology
Continuous online index tuning in moving object databases
ACM Transactions on Database Systems (TODS)
Optimized algorithms for predictive range and KNN queries on moving objects
Information Systems
Predictive line queries for traffic prediction
Transactions on Large-Scale Data- and Knowledge-Centered Systems VI
The SMO-index: a succinct moving object structure for timestamp and interval queries
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
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This paper develops a novel, compressed B^+-tree based indexing scheme that supports the processing of moving objects in one-, two-, and multi- dimensional spaces. The past, current, and anticipated future trajectories of movements are fully indexed and well organized. No parameterized functions and geometric representations are introduced in our data model so that update operations are not required and the maintenance of index structures can be accomplished by basic insertion and deletion operations. The proposed method has two contributions. First, the spatial and temporal attributes of trajectories are accurately preserved and well organized into compact index structures with very efficient memory space utilization and storage requirement. Second, index maintenance overheads are more economical and query performance is more responsive than those of conventional methods. Both analytical and empirical studies show that our proposed indexing scheme outperforms the TPR-tree.