Spatial tessellations: concepts and applications of Voronoi diagrams
Spatial tessellations: concepts and applications of Voronoi diagrams
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Optimal multi-step k-nearest neighbor search
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Distance browsing in spatial databases
ACM Transactions on Database Systems (TODS)
Time-parameterized queries in spatio-temporal databases
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Modeling and Querying Moving Objects
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Fast Nearest Neighbor Search in Medical Image Databases
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
K-Nearest Neighbor Search for Moving Query Point
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Nearest neighbor queries in road networks
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
Processing in-route nearest neighbor queries: a comparison of alternative approaches
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
Continuous K-Nearest Neighbor Search for Moving Objects
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
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
Continuous nearest neighbor search
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Continuous K-nearest neighbor queries for continuously moving points with updates
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
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
Distance indexing on road networks
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
PINE-guided cache replacement policy for location-dependent data in mobile environment
Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments
Generalized network Voronoi diagrams: Concepts, computational methods, and applications
International Journal of Geographical Information Science
The V*-Diagram: a query-dependent approach to moving KNN queries
Proceedings of the VLDB Endowment
GPU-based computation of distance functions on road networks with applications
Proceedings of the 2009 ACM symposium on Applied Computing
Continuous obstructed nearest neighbor queries in spatial databases
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Continuous range search based on network Voronoi diagram
International Journal of Grid and Utility Computing
Casper*: Query processing for location services without compromising privacy
ACM Transactions on Database Systems (TODS)
Location-dependent query processing: Where we are and where we are heading
ACM Computing Surveys (CSUR)
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
Incremental approach for Continuous k-Nearest Neighbours queries on road
International Journal of Intelligent Information and Database Systems
Analysis and evaluation of V*-kNN: an efficient algorithm for moving kNN queries
The VLDB Journal — The International Journal on Very Large Data Bases
Local network Voronoi diagrams
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Voronoi-based range and continuous range query processing in mobile databases
Journal of Computer and System Sciences
Continuous nearest-neighbor search in the presence of obstacles
ACM Transactions on Database Systems (TODS)
Path branch points in mobile navigation
Proceedings of the 8th International Conference on Advances in Mobile Computing and Multimedia
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Voronoi-based multi-level range search in mobile navigation
Multimedia Tools and Applications
Continuous visible nearest neighbor query processing in spatial databases
The VLDB Journal — The International Journal on Very Large Data Bases
Approximate continuous K-nearest neighbor queries for uncertain objects in road networks
WAIM'11 Proceedings of the 12th international conference on Web-age information management
A safe-exit approach for efficient network-based moving range queries
Data & Knowledge Engineering
Continuous-time moving network voronoi diagram
Transactions on Computational Science XIV
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Continuous K nearest neighbor queries (C-KNN) are defined as finding the nearest points of interest along an enitre path (e.g., finding the three nearest gas stations to a moving car on any point of a pre-specified path). The result of this type of query is a set of intervals (or split points) and their corresponding KNNs, such that the KNNs of all points within each interval are the same. The current studies on C-KNN focus on vector spaces where the distance between two objects is a function of their spatial attributes (e.g., Euclidean distance metric). These studies are not applicable to spatial network databases (SNDB) where the distance between two objects is a function of the network connectivity (e.g., shortest path between two objects). In this paper, we propose two techniques to address C-KNN queries in SNDB: Intersection Examination (IE) and Upper Bound Algorithm (UBA). With IE, we first find the KNNs of all nodes on a path and then, for those adjacent nodes whose nearest neighbors are different, we find the intermediate split points. Finally, we compute the KNNs of the split points using the KNNs of the surrounding nodes. The intuition behind UBA is that the performance of IE can be improved by determining the adjacent nodes that cannot have any split points in between, and consequently eliminating the computation of KNN queries for those nodes. Our empirical experiments show that the UBA approach outperforms IE, specially when the points of interest are sparsely distributed in the network.