Efficient AKNN spatial network queries using the M-Tree

Ioup, Elias; Shaw, Kevin; Sample, John T.; Abdelguerfi, Mahdi

doi:10.1145/1341012.1341070

Cited by 14 publications

(13 citation statements)

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“…The remaining two algorithms were adaptations of the existing top-k algorithm, which finds the k nearest query objects based on their shortest-path distances. Ioup et al [2] proposed an algorithm for the ANN search in road networks using the M-tree [13]. However, this algorithm returns only approximate search results, and the error range of the search results is not known.…”

Section: Related Workmentioning

confidence: 99%

“…In this study, road network objects are mapped into those in a metric space as in [2]. The metric space is formally defined as a (D, d) pair, where D is a set of objects, and d ∶ D × D → ℝ is a distance function between two arbitrary objects in D satisfying the following three properties for any objects O a , O b , and O c (∈ D):…”

Section: Ier-lmds: Efficient Fann Algorithm In Road Networkmentioning

confidence: 99%

“…3 In the first experiment, stress values were calculated using the actual shortestpath distances d and the estimated Euclidean distances d between two objects in road networks. We compared the stress values that were separately calculated using d (k) and d (2) distances, where d (k) is the k-dimensional distance obtained after LMDS transformation, and d (2) is the distance obtained using 2-D coordinates of objects (refer to Sect. 3).…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…The search for the nearest neighbor (NN) in spatial database and road network applications is the most fundamental and important problem and has been studied extensively for many years [1][2][3][4]. With the recent advances and wide use of mobile devices and GPS systems, studies on various applications and services using the NN search are being actively conducted.…”

Section: Introductionmentioning

confidence: 99%

“…Aggregate nearest neighbor (ANN) search is an extension of the NN search with M (≥ 1) query objects Q = {q 0 , … , q M−1 } , where M = |Q| [2,4,6,7]. The ANN search finds the object p * that minimizes g(p, Q) among target objects, where g is an aggregate function (max or sum), and g(p, Q) is defined as g(p, Q) = g{d(p, q i ), q i ∈ Q} .…”

Section: Introductionmentioning

confidence: 99%

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α-Probabilistic flexible aggregate nearest neighbor search in road networks using landmark multidimensional scaling

Chung

Loh

2020

J Supercomput

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In spatial database and road network applications, the search for the nearest neighbor (NN) from a given query object q is the most fundamental and important problem. Aggregate nearest neighbor (ANN) search is an extension of the NN search with a set of query objects $$Q = \{ q_0, \dots , q_{M-1} \}$$ Q = { q 0 , ⋯ , q M - 1 } and finds the object $$p^*$$ p ∗ that minimizes $$g \{ d(p^*, q_i), q_i \in Q \}$$ g { d ( p ∗ , q i ) , q i ∈ Q } , where g (max or sum) is an aggregate function and d() is a distance function between two objects. Flexible aggregate nearest neighbor (FANN) search is an extension of the ANN search with the introduction of a flexibility factor $$\phi \, (0 < \phi \le 1)$$ ϕ ( 0 < ϕ ≤ 1 ) and finds the object $$p^*$$ p ∗ and the set of query objects $$Q^*_\phi $$ Q ϕ ∗ that minimize $$g \{ d(p^*, q_i), q_i \in Q^*_\phi \}$$ g { d ( p ∗ , q i ) , q i ∈ Q ϕ ∗ } , where $$Q^*_\phi $$ Q ϕ ∗ can be any subset of Q of size $$\phi |Q|$$ ϕ | Q | . This study proposes an efficient $$\alpha $$ α -probabilistic FANN search algorithm in road networks. The state-of-the-art FANN search algorithm in road networks, which is known as IER-$$k\hbox {NN}$$ k NN , used the Euclidean distance based on the two-dimensional coordinates of objects when choosing an R-tree node that most potentially contains $$p^*$$ p ∗ . However, since the Euclidean distance is significantly different from the actual shortest-path distance between objects, IER-$$k\hbox {NN}$$ k NN looks up many unnecessary nodes, thereby incurring many calculations of ‘expensive’ shortest-path distances and eventually performance degradation. The proposed algorithm transforms road network objects into k-dimensional Euclidean space objects while preserving the distances between them as much as possible using landmark multidimensional scaling (LMDS). Since the Euclidean distance after LMDS transformation is very close to the shortest-path distance, the lookup of unnecessary R-tree nodes and the calculation of expensive shortest-path distances are reduced significantly, thereby greatly improving the search performance. As a result of performance comparison experiments conducted for various real road networks and parameters, the proposed algorithm always achieved higher performance than IER-$$k\hbox {NN}$$ k NN ; the performance (execution time) of the proposed algorithm was improved by up to 10.87 times without loss of accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: Ier-lmds: Efficient Fann Algorithm In Road Networkmentioning

confidence: 99%