Node and edge selectivity estimation for range queries in spatial networks

Authors:
E. Tiakas;A. N. Papadopoulos;A. Nanopoulos;Y. Manolopoulos
Affiliations:
Department of Informatics, Aristotle University, 54124 Thessaloniki, Greece;Department of Informatics, Aristotle University, 54124 Thessaloniki, Greece;Department of Informatics, Aristotle University, 54124 Thessaloniki, Greece;Department of Informatics, Aristotle University, 54124 Thessaloniki, Greece
Venue:
Information Systems
Year:
2009

Citing 14
Cited 0

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
Spatial join selectivity using power laws

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Estimating the Selectivity of Spatial Queries Using the `Correlation' Fractal Dimension

VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Integer priority queues with decrease key in constant time and the single source shortest paths problem

Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
Nearest neighbor queries in road networks

GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information 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
Performance evaluation of spatio-temporal selectivity estimation techniques

SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management
Modeling and Predicting Future Trajectories of Moving Objects in a Constrained Network

MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Spatial Query Estimation without the Local Uniformity Assumption

Geoinformatica
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
Selectivity estimation in spatial networks

Proceedings of the 2008 ACM symposium on Applied computing
Distributed clustering based on sampling local density estimates

IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Modern applications requiring spatial network processing pose several interesting query optimization challenges. Spatial networks are usually represented as graphs, and therefore, queries involving a spatial network can be executed by using the corresponding graph representation. This means that the cost for executing a query is determined by graph properties such as the graph order and size (i.e., number of nodes and edges) and other graph parameters. In this paper, we present novel methods to estimate the number of nodes and edges in regions of interest in spatial networks, towards predicting the space and time requirements for range queries. The methods are evaluated by using real-life and synthetic data sets. Experimental results show that the number of nodes and edges can be estimated efficiently and accurately, with relatively small space requirements, thus providing useful information to the query optimizer.