A taxonomy for nearest neighbour queries in spatial databases

Taniar, David; Rahayu, J. Wenny

doi:10.1016/j.jcss.2013.01.017

Cited by 62 publications

(32 citation statements)

References 17 publications

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“…If these are swapped, we will have a reverse region query, where the inputs are the objects, and the output of the query is a region. Therefore, a Reverse Region Query selects several objects as the query objects, and the query will find a region where the sum distance between all objects and the region is the shortest [22]. Fig.…”

Section: Reverse Region Queriesmentioning

confidence: 99%

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A taxonomy for region queries in spatial databases

Taniar

Rahayu

2015

Journal of Computer and System Sciences

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In spatial databases, there are two basic types of queries, namely nearest neighbour queries (kNN) and range queries. Spatial range queries are not only finding objects of interest within a certain range or radius, but feature a wide spectrum: from finding objects of interest to forming the range (or region). Therefore, in this paper, we coin a term "Region Queries" to indicate a broad category of spatial range queries. It is imperative to understand the full capabilities of region queries, before starting to work on processing and optimising such queries. The aim of this paper is to show a complete picture of region queries. In this study, we present taxonomy of region queries, comprising of three categories: (i) finding objects of interest, (ii) forming regions, and (iii) determining centroids. These three query types form a comprehensive view of what region queries are about.

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Section: Reverse Region Queriesmentioning

confidence: 99%

“…We have previously published a similar work in this journal, but with a focus on nearest neighbour (kNN) [22], in which we presented a comprehensive view of nearest neighbour queries in spatial databases. In this paper, we study region queries.…”

Section: Introductionmentioning

confidence: 99%

A taxonomy for region queries in spatial databases

Taniar

Rahayu

2015

Journal of Computer and System Sciences

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“…For each element in R, it consists of the interesting objects P i and one of the intersection points S i . This is quite useful when it comes into some Put these two points together with their distance into a set S D 10 b ←− next element in P b end 11 A set C storing adjacent generator points, together with the distances 12 Sort C in an ascending order based on D N (S i , P i ) 13 n ←− number of elements in R 14 while n < k − 1 do 15 Put P i along with S i of the first element in C in R 16 Replace the first element in C with all its neighbouring NVDs' generator points, re-calculate the distances 17 Sort C in an ascending order end 18 Return R end Algorithm 5: Search Algorithm.…”

Section: Search Algorithmmentioning

confidence: 99%

“…However, for both of these two queries, the user location is necessary, which hazards the privacy of users' personal information. Hence, we proposed Range-kNN queries [15], which uses the LT to hide mobile users' locations, in order to protect the user information privacy for query processing. LT is the hierarchy structure of the landmarks, which can be used to replace the user location with a landmark for query processing.…”

Section: Introductionmentioning

confidence: 99%

Range-kNN queries with privacy protection in a mobile environment

Shao

Taniar

Adhinugraha

2015

Pervasive and Mobile Computing

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“…The idea of relative distances has been discussed in [22] in the context of k nearest neighbors queries. However, this is a survey study and a solution was not proposed.…”

Section: Related Workmentioning

confidence: 99%

Relaxed Reverse Nearest Neighbors Queries

Hidayat

Cheema

Taniar

2015

Advances in Spatial and Temporal Databases

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Abstract. Given a set of users U , a set of facilities F , and a query facility q, a reverse nearest neighbors (RNN) query retrieves every user u for which q is its closest facility. Since q is the closest facility of u, the user u is said to be influenced by q. In this paper, we propose a relaxed definition of influence where a user u is said to be influenced by not only its closest facility but also every other facility that is almost as close to u as its closest facility is. Based on this definition of influence, we propose relaxed reverse nearest neighbors (RRNN) queries. Formally, given a value of x > 1, an RRNN query q returns every user u for which dist(u, q) ≤ x × N N Dist (u) where N N Dist(u) denotes the distance between a user u and its nearest facility. Based on effective pruning techniques and several non-trivial observations, we propose an efficient RRNN query processing algorithm. Our extensive experimental study conducted on several real and synthetic data sets demonstrates that our algorithm is several orders of magnitude better than a naïve algorithm as well as a significantly improved version of the naïve algorithm.

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A taxonomy for nearest neighbour queries in spatial databases

Cited by 62 publications

References 17 publications

A taxonomy for region queries in spatial databases

A taxonomy for region queries in spatial databases

Range-kNN queries with privacy protection in a mobile environment

Relaxed Reverse Nearest Neighbors Queries

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