Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Hierarchical optimization of optimal path finding for transportation applications
CIKM '96 Proceedings of the fifth international conference on Information and knowledge management
CCAM: A Connectivity-Clustered Access Method for Networks and Network Computations
IEEE Transactions on Knowledge and Data Engineering
Nearest neighbor queries in road networks
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
Clustering objects on a spatial network
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
Aggregate Nearest Neighbor Queries in Road Networks
IEEE Transactions on Knowledge and 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
Reverse Nearest Neighbors in Large Graphs
IEEE Transactions on Knowledge and Data Engineering
BLINKS: ranked keyword searches on graphs
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Query processing in spatial network databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Integrated data management for mobile services in the real world
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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
Fast nearest neighbor search on road networks
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
The islands approach to nearest neighbor querying in spatial networks
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Multiple k nearest neighbor query processing in spatial network databases
ADBIS'06 Proceedings of the 10th East European conference on Advances in Databases and Information Systems
The V*-Diagram: a query-dependent approach to moving KNN queries
Proceedings of the VLDB Endowment
Snapshot location-based query processing on moving objects in road networks
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Efficient Continuous Nearest Neighbor Query in Spatial Networks Using Euclidean Restriction
SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
Analysis and evaluation of V*-kNN: an efficient algorithm for moving kNN queries
The VLDB Journal — The International Journal on Very Large Data Bases
Easiest-to-reach neighbor search
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Towards k-nearest neighbor search in time-dependent spatial network databases
DNIS'10 Proceedings of the 6th international conference on Databases in Networked Information Systems
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Mobile services is emerging as an important application area for spatio-temporal database management technologies. Service users are often constrained to a spatial network, e.g., a road network, through which points of interest, termed data points, are accessible. Queries that implement services will often concern data points of some specific type, e.g., Thai restaurants or art museums. As a result, the relatively few data points are relevant to a query in comparison to the number of network edges, meaning that queries, e.g., k nearest-neighbor queries, must access large portions of the network. Existing query processing techniques pre-compute distances between data points and network vertices for improving the performance. However, precomputation becomes problematic when the network or data points must be updated, possibly concurrently with the querying; and if the data points are moving, the existing techniques are inapplicable. In addition, multiple pre-computed structures must be maintained--one for each type of data point. We propose a versatile pre-computation approach for spatial network data. This approach uses a grid for pre-computing a simplified network. The above-mentioned shortcomings are avoided by making the pre-computed data independent of the data points. Empirical performance studies show that the structure is competitive with respect to the existing, more specialized techniques.