The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Group Nearest Neighbor Queries
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Aggregate Nearest Neighbor Queries in Road Networks
IEEE Transactions on Knowledge and Data Engineering
Aggregate nearest neighbor queries in spatial databases
ACM Transactions on Database Systems (TODS)
Two ellipse-based pruning methods for group nearest neighbor queries
Proceedings of the 13th annual ACM international workshop on Geographic information systems
Efficient methods in finding aggregate nearest neighbor by projection-based filtering
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part III
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Developed from Nearest Neighbor (NN) queries, Aggregate Nearest Neighbor (ANN) queries return the object that minimizes an aggregate distance function with respect to a set of query points. Because of the multiple query points, ANN queries are much more complex than NN queries. For optimizing the query processing and improving the query efficiency, many ANN queries algorithms utilizes pruning strategies, with or without an index structure. Obviously, the pruning effect highly depends on the tightness of the bound estimation. In this paper, we figure out a property in vector space and develop some efficient bound estimations for two most popular types of ANN queries. Based on these bounds, we design the indexed and non-index ANN algorithms, and conduct experimental studies. Our algorithms show good performance, especially for high dimensional queries, for both real dataset and synthetic datasets.