Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Time-parameterized queries in spatio-temporal databases
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Efficient Processing of Spatial Queries in Line Segment Databases
SSD '91 Proceedings of the Second International Symposium on Advances in Spatial Databases
K-Nearest Neighbor Search for Moving Query Point
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Semantic Caching in Location-Dependent Query Processing
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Location-based spatial queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Main Memory Evaluation of Monitoring Queries Over Moving Objects
Distributed and Parallel Databases
SINA: scalable incremental processing of continuous queries in spatio-temporal databases
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Monitoring k-Nearest Neighbor Queries over Moving Objects
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
Continuous nearest neighbor monitoring in road networks
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Continuous nearest neighbor search
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Query processing in spatial network databases
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
Scalable network distance browsing in spatial databases
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
S-GRID: a versatile approach to efficient query processing in spatial networks
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
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
Local network Voronoi diagrams
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
A novel framework for processing continuous queries on moving objects
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Direction-based surrounder queries for mobile recommendations
The VLDB Journal — The International Journal on Very Large Data Bases
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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In this paper, we propose an efficient method to answer continuous k nearest neighbor (Ck NN) queries in spatial networks. Assuming a moving query object and a set of data objects that make frequent and arbitrary moves on a spatial network with dynamically changing edge weights, Ck NN continuously monitors the nearest (in network distance) neighboring objects to the query. Previous Ck NN methods are inefficient and, hence, fail to scale in large networks with numerous data objects because: 1) they heavily rely on Dijkstra-based blind expansion for network distance computation that incurs excessively redundant cost particularly in large networks, and 2) they blindly map all object location updates to the network disregarding whether the updates are relevant to the Ck NN query result. With our method, termed ER-Ck NN (short for Euclidian Restriction based Ck NN), we utilize ER to address both of these shortcomings. Specifically, with ER we enable 1) guided search (rather than blind expansion) for efficient network distance calculation, and 2) localized mapping (rather than blind mapping) to avoid the intolerable cost of redundant object location mapping. We demonstrate the efficiency of ER-Ck NN via extensive experimental evaluations with real world datasets consisting of a variety of large spatial networks with numerous moving objects.