Multi-Way Distance Join Queries in Spatial Databases
Geoinformatica
An approximate algorithm for top-k closest pairs join query in large high dimensional data
Data & Knowledge Engineering
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Continuous monitoring of exclusive closest pairs
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
Top-K probabilistic closest pairs query in uncertain spatial databases
APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
Evaluating probabilistic spatial-range closest pairs queries over uncertain objects
WAIM'11 Proceedings of the 12th international conference on Web-age information management
| Hi-index | 0.00 |
Spatial databases have grown in importance in various fields. Together with them come various types of queries that need to be answered effectively. While queries involving single data set have been studied extensively, join queries on multi-dimensional data like the k-closest pairs and the nearest neighbor joins have only recently received attention.In this paper, we propose a new index structure, the b-Rdnn tree, to solve different join queries. The structure is similar to the Rdnn-tree for the reverse nearest neighbor queries. Based on this new index structure, we give the algorithms for various join queries in spatial databases. It is especially effective for the k-closest pair queries, where earlier algorithms using R*-tree can be very inefficient in many real life circumstances. To this end we present experimental results on k-closest pair queries to support the fact that our index structure is a better alternative.