Indexing Network Voronoi Diagrams

Demiryurek, Ugur; Shahabi, Cyrus

doi:10.1007/978-3-642-29038-1_38

Cited by 18 publications

(8 citation statements)

References 31 publications

(42 reference statements)

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“…The weighted network Voronoi diagrams have been extensively used for nearest neighbor queries in location-based services (Dickerson & Goodrich, 2008;Dickerson et al, 2011;Erwig, 2000;Kolahdouzan & Shahabi, 2004;Zhu et al, 2010) and market area analysis (Okabe & Kitamura, 1996). Indexing techniques were also investigated in the literature (Demiryurek & Shahabi, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

Weighted network Voronoi Diagrams for local spatial analysis

She

Zhu

et al. 2015

Computers, Environment and Urban Systems

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Section: Literature Reviewmentioning

confidence: 99%

Weighted network Voronoi Diagrams for local spatial analysis

She

Zhu

et al. 2015

Computers, Environment and Urban Systems

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“…The geodesic distance between nodes v i and v j is denoted by ρ(v i , v j ). We borrow the definition of a Voronoi diagram of a network from [11]. Definition 1.…”

Section: Preliminariesmentioning

confidence: 99%

Measuring event concentration in empirical networks with different types of degree distributions

Campos¹,

Finke

2020

PLoS ONE

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Measuring event concentration often involves identifying clusters of events at various scales of resolution and across different regions. In the context of a city, for example, clusters may be characterized by the proximity of events in the metric space. However, events may also occur over urban structures such as public transportation and infrastructure systems, which are naturally represented as networks. Our work provides a theoretical framework to determine whether events distributed over a set of interconnected nodes are concentrated on a particular subset. Our main analysis shows how the proposed or any other measure of event concentration on a network must explicitly take into account its degree distribution. We apply the framework to measure event concentration (i) on a street network (i.e., approximated as a regular network where events represent criminal activities); and (ii) on a social network (i.e., a power law network where events represent users who are dissatisfied after purchasing the same product).

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“…The Network Voronoi diagram [2] and its variants have been extensively used in the context of numerous applications in road networks, particularly to efficiently evaluate various spatial proximity queries such as K-NN and closest pair. Although the existing approaches successfully utilize the network Voronoi diagram as a way to partition the space for the specific problems, there is little emphasis on how to efficiently find and access the network Voronoi cell containing a particular point or edge of the network.…”

Section: Indexing Network Voronoi Diagramsmentioning

confidence: 99%

Survey on Confidentiality of the User and Query Processing on Spatial Network

Srilakshmi¹,

Dhamodharan²

2015

IJRET

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The administration of transhipment systems has become increasingly important in many applications such as position-based services, supply cycle management, travel control, and so on. These applications usually involve queries over spatial networks with vigorously changing and problematical travel conditions. There may be possibilities of user's privacy violated when they are querying about the location information on the third party servers where the location information about the users will be tracked. The malicious attackers may steal the location information about the users. The k nearest neighbour query verification with location points on Voronoi diagram increases the verification cost on mobile clients. The reverse nearest neighbour queries by assigning each object and query with a safe region is applied such that the expensive recomputation is not required as long as the query and objects remain in their respective safe regions. The proposed system reduces the communication cost in client-server architectures because an object does not report its location to the server unless it leaves its safe region or the server sends a location update request. Hilbert curve is used here for the capability of partially retaining the neighbouring adjacency of the original data. The user data is protected by applying Hilbert transform over the original values and storing the transformed values in the Hilbert curve.

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Indexing Network Voronoi Diagrams

Cited by 18 publications

References 31 publications

Weighted network Voronoi Diagrams for local spatial analysis

Weighted network Voronoi Diagrams for local spatial analysis

Measuring event concentration in empirical networks with different types of degree distributions

Survey on Confidentiality of the User and Query Processing on Spatial Network

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