VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Indexing the Distance: An Efficient Method to KNN Processing
Proceedings of the 27th International Conference on Very Large Data Bases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Adaptive filters for continuous queries over distributed data streams
Proceedings of the 2003 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
Holistic aggregates in a networked world: distributed tracking of approximate quantiles
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Sketching streams through the net: distributed approximate query tracking
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Adaptive stream filters for entity-based queries with non-value tolerance
VLDB '05 Proceedings of the 31st international conference on Very large data bases
A Threshold-Based Algorithm for Continuous Monitoring of k Nearest Neighbors
IEEE Transactions on Knowledge and Data Engineering
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Continuous nearest neighbor monitoring in road networks
VLDB '06 Proceedings of the 32nd 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
Distributed set-expression cardinality estimation
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Approximate NN queries on streams with guaranteed error/performance bounds
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
A centroid k-nearest neighbor method
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
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Continuous monitoring knearest neighbors in highly dynamic scenarios appears to be a hot topic in database research community. Most previous work focus on devising approaches with a goal to consume litter computation resource and memory resource. Only a few literatures aim at reducing communication overhead, however, still with an assumption that the query object is static. This paper constitutes an attempt on continuous monitoring knearest neighbors to a dynamicquery object with a goal to reduce communication overhead. In our RFA approach, a Range Filter is installed in each moving object to filter parts of data (e.g. location). Furthermore, RFA approach is capable of answering three kinds of queries, including precise kNN query, non-value-based approximate kNN query, and value-based approximate kNN query. Extensive experimental results show that our new approach achieves significant saving in communication overhead.