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
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
IEEE Transactions on Parallel and Distributed Systems
Optimal multi-step k-nearest neighbor search
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
Distance browsing in spatial databases
ACM Transactions on Database Systems (TODS)
ACM Computing Surveys (CSUR)
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
Performance of Nearest Neighbor Queries in R-Trees
ICDT '97 Proceedings of the 6th International Conference on Database Theory
Indexing the Distance: An Efficient Method to KNN Processing
Proceedings of the 27th International Conference on Very Large Data Bases
Constrained Nearest Neighbor Queries
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Group Nearest Neighbor Queries
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Challenges in spatiotemporal stream query optimization
MobiDE '06 Proceedings of the 5th ACM international workshop on Data engineering for wireless and mobile access
Finding aggregate nearest neighbor efficiently without indexing
Proceedings of the 2nd international conference on Scalable information systems
A shortest path algorithm with novel heuristics for dynamic transportation networks
International Journal of Geographical Information Science
Efficient Bounds in Finding Aggregate Nearest Neighbors
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Query optimization for spatio-temporal data stream management systems
SIGSPATIAL Special
Privacy preserving group nearest neighbor queries
Proceedings of the 13th International Conference on Extending Database Technology
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
Flexible aggregate similarity search
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Finding the sites with best accessibilities to amenities
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
Nearest-neighbor searching under uncertainty
PODS '12 Proceedings of the 31st symposium on Principles of Database Systems
A time cost optimization for similar scenarios mobile GIS queries
Journal of Visual Languages and Computing
Fast k-clustering queries on embeddings of road networks
Proceedings of the 3rd International Conference on Computing for Geospatial Research and Applications
Aggregate keyword routing in spatial database
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Continuous aggregate nearest neighbor queries
Geoinformatica
Hi-index | 0.00 |
Group nearest neighbor (GNN) queries are a relatively new type of operations in spatial database applications. Different from a traditional kNN query which specifies a single query point only, a GNN query has multiple query points. Because of the number of query points and their arbitrary distribution in the data space, a GNN query is much more complex than a kNN query. In this paper, we propose two pruning strategies for GNN queries which take into account the distribution of query points. Our methods employ an ellipse to approximate the extent of multiple query points, and then derive a distance or minimum bounding rectangle (MBR) using that ellipse to prune intermediate nodes in a depth-first search via an R$^*$-tree. These methods are also applicable to the best-first traversal paradigm. We conduct extensive performance studies. The results show that the proposed pruning strategies are more efficient than the existing methods.