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
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
SAC '98 Proceedings of the 1998 ACM symposium on Applied Computing
Indexing moving points (extended abstract)
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A Framework for Generating Network-Based Moving Objects
Geoinformatica
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Efficient Cost Models for Spatial Queries Using R-Trees
IEEE Transactions on Knowledge and Data Engineering
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
A Spatiotemporal Model and Language for Moving Objects on Road Networks
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Indexing the Current Positions of Moving Objects Using the Lazy Update R-tree
MDM '02 Proceedings of the Third International Conference on Mobile Data Management
On nearest neighbor indexing of nonlinear trajectories
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Indexing of Moving Objects for Location-Based Services
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Nearest neighbor queries in road networks
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
Indexing of network constrained moving objects
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
Computational data modeling for network-constrained moving objects
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
Managing Moving Objects on Dynamic Transportation Networks
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
Prediction and indexing of moving objects with unknown motion patterns
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
STRIPES: an efficient index for predicted trajectories
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Indexing the Trajectories of Moving Objects in Networks*
Geoinformatica
Change Tolerant Indexing for Constantly Evolving Data
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
An efficient and scalable approach to CNN queries in a road network
VLDB '05 Proceedings of the 31st international conference on Very large data bases
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Modeling and Predicting Future Trajectories of Moving Objects in a Constrained Network
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Continuous nearest neighbor monitoring in road networks
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Supporting frequent updates in R-trees: a bottom-up approach
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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 processing in spatial network databases
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
Voronoi-based K nearest neighbor search for spatial network databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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
Fast indexing and updating method for moving objects on road networks
WISEW'03 Proceedings of the Fourth international conference on Web information systems engineering workshops
Tracking network-constrained moving objects with group updates
WAIM '06 Proceedings of the 7th international conference on Advances in Web-Age Information Management
Time constrained range search queries over moving objects in road networks
Proceedings of the 8th International Conference on Advances in Mobile Computing and Multimedia
Boosting moving object indexing through velocity partitioning
Proceedings of the VLDB Endowment
Predictive line queries for traffic prediction
Transactions on Large-Scale Data- and Knowledge-Centered Systems VI
International Journal of Intelligent Information and Database Systems
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Recent advances in wireless sensor networks and positioning technologies have boosted new applications that manage moving objects. In such applications, a dynamic index is often built to expedite evaluation of spatial queries. However, the development of efficient indexes is a challenge due to frequent object movement. In this paper, we propose a new update-efficient index method for moving objects in road networks. We introduce a dynamic data structure, called adaptive unit, to group neighboring objects with similar movement patterns. To reduce updates, an adaptive unit captures the movement bounds of the objects based on a prediction method, which considers road-network constraints and the stochastic traffic behavior. A spatial index (e.g., R-tree) for the road network is then built over the adaptive unit structures. Simulation experiments, carried on two different datasets, show that an adaptive-unit based index is efficient for both updating and querying performances.