Approximate nearest neighbor queries in fixed dimensions
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Vector approximation based indexing for non-uniform high dimensional data sets
Proceedings of the ninth international conference on Information and knowledge management
Time-parameterized queries in spatio-temporal databases
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
A Framework for Generating Network-Based Moving Objects
Geoinformatica
Nearest Neighbor and Reverse Nearest Neighbor Queries for Moving Objects
IDEAS '02 Proceedings of the 2002 International Symposium on Database Engineering & Applications
Location-based spatial queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Nearest neighbor queries in road networks
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
Approximate searches: k-neighbors + precision
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Monitoring k-Nearest Neighbor Queries over Moving Objects
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
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
A generic framework for monitoring continuous spatial queries over moving objects
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
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
Nearest and reverse nearest neighbor queries for moving objects
The VLDB Journal — The International Journal 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
Towards optimal continuous nearest neighbor queries in spatial databases
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
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
Continuous K-Nearest Neighbor Query over Moving Objects in Road Networks
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
CIT '10 Proceedings of the 2010 10th IEEE International Conference on Computer and Information Technology
ER'05 Proceedings of the 24th international conference on Perspectives in Conceptual Modeling
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Continuous K nearest neighbor queries (CKNN) on moving objects retrieves among all moving objects the K-Nearest Neighbors (KNNs) of a moving query point within a given time interval. Since the frequent updates of object locations make it complicated to process CKNN, the cost for retrieving the exact CKNN data set is expensive, particularly in highly dynamic spatiotemporal applications. In some applications (e.g. finding my nearest taxies while I am moving within the next 5 minutes), it is not necessary to obtain the accurate result set. For these applications, we introduce a novel technique, Moving state based Approximate CKNN (MACKNN), to approximate the CKNN query results with certain accuracy to make the query process more efficient by using Moving State of Uncertain Object (MSUO) Model and guarantee certain accuracy. We evaluate the MACKNN technique with simulations and compare it with a traditional approach. Experimental results are presented to demonstrate the utility of our new approach.