Aggregate nearest neighbor queries in spatial databases

Papadias, Dimitris; Tao, Yufei; Mouratidis, Kyriakos; Hui, Chun Kit

doi:10.1145/1071610.1071616

Cited by 249 publications

(196 citation statements)

References 36 publications

(42 reference statements)

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“…similarity queries that retrieve the top-k objects according to a given similarity (distance) aggregate w.r.t. a given set of query points [9,8]. However, the problem addressed by group queries generally differs from the problem addressed in this paper.…”

Section: Related Workmentioning

confidence: 99%

Inverse Queries for Multidimensional Spaces

Bernecker

Emrich

Kriegel

et al. 2011

Advances in Spatial and Temporal Databases

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Abstract. Traditional spatial queries return, for a given query object q, all database objects that satisfy a given predicate, such as epsilon range and k-nearest neighbors. This paper defines and studies inverse spatial queries, which, given a subset of database objects Q and a query predicate, return all objects which, if used as query objects with the predicate, contain Q in their result. We first show a straightforward solution for answering inverse spatial queries for any query predicate. Then, we propose a filter-and-refinement framework that can be used to improve efficiency. We show how to apply this framework on a variety of inverse queries, using appropriate space pruning strategies. In particular, we propose solutions for inverse epsilon range queries, inverse k-nearest neighbor queries, and inverse skyline queries. Our experiments show that our framework is significantly more efficient than naive approaches.

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Section: Related Workmentioning

confidence: 99%

Inverse Queries for Multidimensional Spaces

Bernecker

Emrich

Kriegel

et al. 2011

Advances in Spatial and Temporal Databases

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“…It is important to note that we consider Euclidean distance and a 2D point database server [6] for the GLP protocol. Based on this notation and the above model, we present the GLP protocol in the next subsection.…”

Section: Modelmentioning

confidence: 99%

“…As an example, consider a scenario in which a group of users (a working group) needs to urgently meet. They can use an LBS provider to find the nearest meeting place that minimizes their aggregate distances [6]. To get the desired result, each user sends a nearest neighbor (NN) query along with her location to the LBS.…”

Section: Introductionmentioning

confidence: 99%

“…To get the desired result, each user sends a nearest neighbor (NN) query along with her location to the LBS. The LBS evaluates the set of NN queries (a group of NN queries or an aggregate NN query [6]) and retrieves the points of interest (POIs) with the smallest aggregate distances from them. Here, the aggregate distance is the total distance of all group members from the meeting location [6].…”

Section: Introductionmentioning

confidence: 99%

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GLP: A cryptographic approach for group location privacy

Ashouri-Talouki

Baraani-Dastjerdi

Selçuk

2012

Computer Communications

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a b s t r a c tRecently, location privacy during the use of location-based services (LBSs) has raised considerable concerns. There is a wide literature on location privacy from the individual point of view; however, there exist only a few works to support location privacy for a group of users. In this paper, we consider location privacy issues for a group of users who may ask an LBS for a meeting place that minimizes their aggregate distance. The proposed solution, which we call the Group Location Privacy (GLP) protocol, is based on the Anonymous Veto network (AV-net) and homomorphic encryption. It preserves the location privacy of all users even in the case of collusion. Our solution also tries to minimize the LBS overhead for nearest neighbor (NN) queries and communication, i.e., to decrease the number of NN queries sent to an LBS and the number of points of interest (POIs) it returns. Furthermore, GLP greatly decreases the bandwidth usage to a high extent and protects the LBS provider from excessive disclosure of POIs. We discuss the performance and security analysis of the GLP protocol and show that the proposed protocol is secure against partial collusion in a malicious model.

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“…The proposed algorithms and techniques mainly focus on pruning the site set P to identify kNN. Roussopoulos et al [1] propose an R-tree based kNN algorithm that prunes in a branch-and-bound manner; Korn et al [2] study the influence set (reverse nearest neighbors) to the sites; Tao et al focus on continuous kNN with moving query points [3] and aggregate kNN on multiple query points [4].…”

Section: Knn Query Processing Techniquesmentioning

confidence: 99%

Indexing land surface for efficient kNN query

2008

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The class of k Nearest Neighbor (kNN) queries is frequently used in geospatial applications. Many studies focus on processing kNN in Euclidean and road network spaces. Meanwhile, with the recent advances in remote sensory devices that can acquire detailed elevation data, the new geospatial applications heavily operate on this third dimension, i.e., land surface. Hence, for the field of databases to stay relevant, it should be able to efficiently process spatial queries given this constrained third dimension. However, online processing of the surface k Nearest Neighbor (skNN) queries is quite challenging due to the huge size of land surface models which renders any accurate distance computation on the surface extremely slow. In this paper, for the first time, we propose an index structure on land surface that enables exact and fast responses to skNN queries. Two complementary indexing schemes, namely Tight Surface Index (TSI) and Loose Surface Index (LSI), are constructed and stored collectively on a single novel data structure called Surface Index R-tree (SIR-tree). With those indexes, we can process skNN query efficiently by localizing the search and minimizing the invocation of the costly surface distance computation and hence incurring low I/O and computation costs. Our algorithm does not need to know the value of k a priori and can incrementally expand the search region using SIR-tree and report the query result progressively. It also reports the exact shortest surface paths to the query results. We show through experiments with real world data sets that our algorithm has better performance than the competitors in both efficiency and accuracy.

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Aggregate nearest neighbor queries in spatial databases

Cited by 249 publications

References 36 publications

Inverse Queries for Multidimensional Spaces

Inverse Queries for Multidimensional Spaces

GLP: A cryptographic approach for group location privacy

Indexing land surface for efficient kNN query

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