Density-based reverse nearest neighbourhood search in spatial databases

Allheeib, Nasser; Islam, Md. Saiful; Taniar, David; Shao, Zhou; Cheema, Muhammad Aamir

doi:10.1007/s12652-018-1103-x

Cited by 12 publications

(17 citation statements)

References 30 publications

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“…Next, [4] extended the reverse nearest neighbour (RNN) query by proposing reverse nearest neighbourhood query (RNNH) in spatial databases in a bichromatic manner. Instead of returning the dispersed RNN users that find the query as their closest facility, a method was proposed to find the location of the nearest group of users by considering the query point as the facility nearest to the group.…”

Section: Related Workmentioning

confidence: 99%

“…Based on Lemmas 2 and 4 we can conclude the following observations from the example displayed in Figure 10. Assume that dist(q , p 4 and f c is the nearest facility to NH , which is E. Hence, q can move to q position C d , where C d > F d , towards Secondclose and dismisses connection with server (see Figure 10). In the Basic SR, the centre point is the first point closest to the query, while the Enhanced SR needs to have two centre points, which are the two closest points to the query (firstclose, Secondclose).…”

Section: B Enhanced Safe Regionmentioning

confidence: 99%

“…Algorithm 2 is used to find the Basic SR where all the neighbourhood-points (RNNH result) listed in the safe-objects list (SOL) which are first found by RNNH algorithm [4]. In line 2, Algorithm 2 calls the calculating boundary function.…”

Section: ) Basic Sr Algorithmmentioning

confidence: 99%

“…[3]. These interconnected peers in a P2P system form a neighbourhood [4]. Figure 1b shows how a P2P environment can overcome the failure of a centralised base station issue by eliminating the need for such stations and allowing more points, such as p 5 (similarly p 6 , p 7 , p 8 and p 9 ), to communicate with the network environment.…”

Section: Introductionmentioning

confidence: 99%

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Safe Regions for Moving Reverse Neighbourhood Queries in a Peer-to-Peer Environment

et al. 2020

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The peer-to-peer (P2P) paradigm has become very popular for storing and sharing information. In most P2P systems, peers are connected by means of a limited range of uniform networks, leading to issues when some connected peers are isolated from the others. In order to address such issues, isolated peers rely on devices with long-range networks to relay their messages. However, since long-range devices can move freely, the set of connected peers may lose their connection. Hence, it is important not only to identify, but also to maximise the area known as the safe region (SR) where a long-range device can move freely while still maintaining connection with its peers. This paper illustrates an innovative and generic monitoring framework that addresses the issues related to frequent query location updating using a systematic approach. In our approach, we propose to apply the Reverse Nearest Neighbourhood (RNNH) concept in a P2P environment to efficiently identify and maximise the irregularly shaped area of the SR up to four times for the potential movement of the long-range devices. It was found that there is no need for costly re-computation when the query is retained within the SR. Monte-Carlo simulation was performed to calculate the area of the SR by weighing in shape irregularity. Experimental results demonstrate the effectiveness and efficiency of our approach. INDEX TERMS Moving query, query processing, reverse nearest neighbourhood, safe region.

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Section: Related Workmentioning

confidence: 99%

Section: B Enhanced Safe Regionmentioning

confidence: 99%

Section: ) Basic Sr Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Safe Regions for Moving Reverse Neighbourhood Queries in a Peer-to-Peer Environment

et al. 2020

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“…It takes a bit of time to load and pre-process raw datasets. In recent years, researchers have proposed a newer version of neighborhood algorithms with better performance [43,44].…”

Section: Clustering Based On Voxelsmentioning

confidence: 99%

Major Orientation Estimation-Based Rock Surface Extraction for 3D Rock-Mass Point Clouds

2019

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In the fields of 3D modeling, analysis of discontinuities and engineering calculation, surface extraction is of great importance. The rapid development of photogrammetry and Light Detection and Ranging (LiDAR) technology facilitates the study of surface extraction. Automatic extraction of rock surfaces from 3D rock-mass point clouds also becomes the basis of 3D modeling and engineering calculation of rock mass. This paper presents an automated and effective method for extracting rock surfaces from unorganized rock-mass point clouds. This method consists of three stages: (i) clustering based on voxels; (ii) estimating major orientations based on Gaussian Kernel and (iii) rock surface extraction. Firstly, the two-level spatial grid is used for fast voxelization and segmenting the point cloud into three types of voxels, including coplanar, non-coplanar and sparse voxels. Secondly, the coplanar voxels, rather than the scattered points, are employed to estimate major orientations by using a bivariate Gaussian Kernel. Finally, the seed voxels are selected on the basis of major orientations and the region growing method based on voxels is applied to extract rock surfaces, resulting in sets of surface clusters. The sub-surfaces of each cluster are coplanar or parallel. In this paper, artificial icosahedron point cloud and natural rock-mass point clouds are used for testing the proposed method, respectively. The experimental results show that, the proposed method can effectively and accurately extract rock surfaces in unorganized rock-mass point clouds.

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Neighborhood Query Processing and Surrounding Objects Retrieval in Spatial Databases: Applications and Algorithms

Islam

2021

Communications in Computer and Information Science

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A nearest neighbourhood query (NHQ) retrieves the closest group of collocated objects from a spatial database for a given query location. On the other hand, a reverse nearest neighborhood query (RNHQ) returns all groups of collocated objects that find the given query nearer than any other competitors. Both NHQ and RNHQ queries might have many practical applications on mobile social networks, demand facility placement and smart urban planning. This paper also introduces another query, called direction-based spatial skyline query (DSQ), for retrieving surrounding objects from a spatial database for a given user location. The retrieved objects are not dominated by other data objects in the same direction w.r.t. the query. Like NHQ and RNHQ queries, retrieval of surrounding objects also has many applications such as nearby pointof-interests retrieval surrounding a user and digital gaming. This paper presents the challenges, algorithms, data indexing and data pruning techniques for processing NHQ, RNHQ and DSQ queries in spatial databases. Finally, encouraging experimental results and future research directions in NHQ, RNHQ, DSQ queries and their variants are discussed.

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Density-based reverse nearest neighbourhood search in spatial databases

Cited by 12 publications

References 30 publications

Safe Regions for Moving Reverse Neighbourhood Queries in a Peer-to-Peer Environment

Safe Regions for Moving Reverse Neighbourhood Queries in a Peer-to-Peer Environment

Major Orientation Estimation-Based Rock Surface Extraction for 3D Rock-Mass Point Clouds

Neighborhood Query Processing and Surrounding Objects Retrieval in Spatial Databases: Applications and Algorithms

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