Paper Info
Title
Optimal network location queries
Abstract
Given a set S of sites and a set O of weighted objects, an optimal location query finds the location(s) where introducing a new site maximizes the total weight of the objects that are closer to the new site than to any other site. With such a query, for instance, a franchise corporation (e.g., McDonald's) can find a location to open a new store such that the number of potential store customers (i.e., people living close to the store) is maximized. Optimal location queries are computationally complex to compute and require efficient solutions that scale with large datasets. Previously, two specific approaches have been proposed for efficient computation of optimal location queries. However, they both assume p-norm distance (namely, L1 and L2/Euclidean); hence, they are not applicable where sites and objects are located on spatial networks. In this paper, we focus on optimal network location (ONL) queries, i.e., optimal location queries with which objects and sites reside on a spatial network. We introduce an approach, namely EONL (short for Expansion-based ONL), which enables efficient computation of ONL queries. Moreover, with an extensive experimental study we verify and compare the efficiency of our proposed approach with real datasets, and we demonstrate the importance of considering network distance (rather than p-norm distance) with ONL queries.
Year
DOI
Venue
2010
10.1145/1869790.1869866
GIS
Keywords
Field
DocType
optimal network location query,optimal location query,efficient solution,onl query,new site,spatial network,expansion-based onl,p-norm distance,network distance,optimal network location,efficient computation
Data mining,Spatial network,Computer science,Franchise,Spatial query,Euclidean geometry,Computation
Conference
Citations 
PageRank 
References 
9
0.55
3
Authors
4
Name
Order
Citations
PageRank
Parisa Ghaemi1343.03
Kaveh Shahabi2312.35
John P. Wilson36911.71
Farnoush Banaei-Kashani433629.47