LNCS 2798 Distance Join Queries of Multiple Inputs in Spatial Databases 1st Edition by Antonio Corral, Yannis Manolopoulos, Yannis Theodoridis, Michael Vassilakopoulos  ISBN 3540394036 9783540394037

LNCS 2798 – Distance Join Queries of Multiple Inputs in Spatial Databases 1st Edition by Antonio Corral, Yannis Manolopoulos, Yannis Theodoridis, Michael Vassilakopoulos  – Ebook PDF Instant Download/Delivery. 3540394036, 9783540394037

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Product details:

ISBN 10: 3540394036

ISBN 13: 9783540394037

Author:  Antonio Corral, Yannis Manolopoulos, Yannis Theodoridis, Michael Vassilakopoulos  

LNCS 2798 – Distance Join Queries of Multiple Inputs in Spatial Databases 1st Edition: 

Let a tuple of n objects obeying a query graph (QG) be called the n-tuple. The “D distance -value” of this n-tuple is the value of a linear function of distances of the n objects that make up this n-tuple, according to the edges of the QG. This paper addresses the problem of finding the Kn-tuples between n spatial datasets that have the smallest D distance -values, the so-called K-Multi-Way Distance Join Query (K-MWDJQ), where each set is indexed by an R-tree-based structure. This query can be viewed as an extension of K-Closest-Pairs Query (K-CPQ) [4] for n inputs. In addition, a recursive non-incremental branch-and-bound algorithm following a Depth-First search for processing synchronously all inputs without producing any intermediate result is proposed. Enhanced pruning techniques are also applied to the n R-trees nodes in order to reduce the total response time of the query, and a global LRU buffer is used to reduce the number of disk accesses. Finally, an experimental study of the proposed algorithm using real spatial datasets is presented.

 

LNCS 2798 – Distance Join Queries of Multiple Inputs in Spatial Databases 1st Edition Table of contents:

1 Introduction

2 Related Work and Motivation

3 K-Multi-Way Distance Join Queries Using R-Trees

3.1 The Query Graph and the Ddistance Function

3.2 Definition of the K-Multi-way Distance Join Query

3.3 MBR-Based Distance Function and Pruning Heuristic

4 An Algorithm for K-Multi-way Distance Join Queries

4.1 Enhancing the Pruning Process

4.2 A Recursive Branch-and-Bound Algorithm for K-MWDJQ

5 Experimental Results

6 Conclusions and Future Work

 

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