A model for the prediction of R-tree performance
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Roads, codes, and spatiotemporal queries
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Efficient query processing on spatial networks
Proceedings of the 13th annual ACM international workshop on Geographic information systems
Grid-Clustering: An Efficient Hierarchical Clustering Method for Very Large Data Sets
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Query processing in spatial network databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Distributed clustering based on sampling local density estimates
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Node and edge selectivity estimation for range queries in spatial networks
Information Systems
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Modern applications requiring spatial network processing pose many interesting query optimization challenges. In many cases, query processing depends on the corresponding graph size (number of nodes and edges) and other graph parameters. In this paper, we present novel methods to estimate the number of nodes in regions of interest in spatial networks, towards predicting the space and time requirements of range queries. We examine all methods by using real-life and synthetic spatial networks. Experimental results show that the number of nodes can be estimated efficiently and accurately with small space requirements, thus providing useful information to the query optimizer.