Title
Clustering spatial networks for aggregate query processing: A hypergraph approach
Abstract
In spatial networks, clustering adjacent data to disk pages is highly likely to reduce the number of disk page accesses made by the aggregate network operations during query processing. For this purpose, different techniques based on the clustering graph model are proposed in the literature. In this work, we show that the state-of-the-art clustering graph model is not able to correctly capture the disk access costs of aggregate network operations. Moreover, we propose a novel clustering hypergraph model that correctly captures the disk access costs of these operations. The proposed model aims to minimize the total number of disk page accesses in aggregate network operations. Based on this model, we further propose two adaptive recursive bipartitioning schemes to reduce the number of allocated disk pages while trying to minimize the number of disk page accesses. We evaluate our clustering hypergraph model and recursive bipartitioning schemes on a wide range of road network datasets. The results of the conducted experiments show that the proposed model is quite effective in reducing the number of disk accesses incurred by the network operations.
Year
DOI
Venue
2008
10.1016/j.is.2007.04.001
Inf. Syst.
Keywords
Field
DocType
spatial networks,record-to-page allocation,state-of-the-art clustering graph model,clustering graph model,hypergraph approach,clustering hypergraph model,hypergraph model,network operation,aggregate query processing,spatial network,hypergraph partitioning,aggregate network operation,disk access cost,disk page,disk page access,clustering
Data mining,Correlation clustering,Computer science,Hypergraph,Theoretical computer science,Network operations center,Cluster analysis,Database,Recursion,Graph model
Journal
Volume
Issue
ISSN
33
1
Information Systems
Citations 
PageRank 
References 
16
0.67
30
Authors
3
Name
Order
Citations
PageRank
Engin Demir134314.59
Cevdet Aykanat299684.08
B. Barla Cambazoglu373538.87