Reverse k-nearest neighbor search in dynamic and general metric databases

Achtert, Elke; Kriegel, Hans‐Peter; Kröger, Peer; Renz, Matthias; Züfle, Andreas

doi:10.1145/1516360.1516462

Cited by 49 publications

(45 citation statements)

References 13 publications

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“…The RkNN query has been extensively studied [3,14,15,2,7,5,16,17,4,8,9,18,19,10] ever since it was introduced in [1]. Below, we briefly describe two widely used pruning strategies.…”

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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“…The RkNN query has been extensively studied [3,14,15,2,7,5,16,17,4,8,9,18,19,10] ever since it was introduced in [1]. Below, we briefly describe two widely used pruning strategies.…”

Section: Related Workmentioning

confidence: 99%

Relaxed Reverse Nearest Neighbors Queries

Hidayat

Cheema

Taniar

2015

Advances in Spatial and Temporal Databases

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“…2) The second class of solutions is based on ℓp norm metric space [Stanoi et al 2000;Stanoi et al 2001;Wu et al 2008b;Cheema et al 2011;Achtert et al 2009]. Stanoi et al [Stanoi et al 2000] propose an algorithm for processing an RNN query that does not require the pre-computation of the nearest neighbor circles.…”

Section: Reverse K Nearest Neighbor Queriesmentioning

confidence: 99%

Efficient Algorithms and Cost Models for Reverse Spatial-Keyword k -Nearest Neighbor Search

Lü

Cong

et al. 2014

ACM Trans. Database Syst.

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Geographic objects associated with descriptive texts are becoming prevalent, justifying the need for spatial keyword queries that consider both locations and textual descriptions of the objects. Specifically, the relevance of an object to a query is measured by spatial-textual similarity that is based on both spatial proximity and textual similarity. In this article, we introduce the Reverse Spatial-Keyword k-Nearest Neighbor (RSKk NN) query, which finds those objects that have the query as one of their k nearest spatial-textual objects. The RSKk NN queries have numerous applications in online maps and GIS decision support systems.To answer RSKk NN queries efficiently, we propose a hybrid index tree, called IUR-tree (IntersectionUnion R-Tree) that effectively combines location proximity with textual similarity. Subsequently, we design a branch-and-bound search algorithm based on the IUR-tree. To accelerate the query processing, we improve IUR-tree by leveraging the distribution of textual description, leading to some variants of the IUR-tree called clustered IUR-tree (CIUR-tree) and combined clustered IUR-tree (C 2 IUR-tree), for each of which we develop optimized algorithms. We also provide a theoretical cost model to analyze the efficiency of our algorithms. Our empirical studies show that the proposed algorithms are efficient and scalable.

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“…Metric spaces enable reasoning about the distance between a pair of objects using their distances to a third object using the triangle inequality. Such reasoning can be done at an aggregate level to prune multiple objects in a single operation [3]. Euclidean spaces, being a specific type of metric spaces, have been exploited to affect other types of optimizations in RkNN search [9,18,20].…”

Section: A Rknn Search In Metric Spacesmentioning

confidence: 99%

Efficient RkNN retrieval with arbitrary non-metric similarity measures

Deepak

Deshpande

2010

Proc. VLDB Endow.

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A RkNN query returns all objects whose nearest k neighbors contain the query object. In this paper, we consider RkNN query processing in the case where the distances between attribute values are not necessarily metric. Dissimilarities between objects could then be a monotonic aggregate of dissimilarities between their values, such aggregation functions being specified at query time. We outline real world cases that motivate RkNN processing in such scenarios. We consider the AL-Tree index and its applicability in RkNN query processing. We develop an approach that exploits the group level reasoning enabled by the AL-Tree in RkNN processing. We evaluate our approach against a Naive approach that performs sequential scans on contiguous data and an improved block-based approach that we provide. We use real-world datasets and synthetic data with varying characteristics for our experiments. This extensive empirical evaluation shows that our approach is better than existing methods in terms of computational and disk access costs, leading to significantly better response times.

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Reverse k-nearest neighbor search in dynamic and general metric databases

Cited by 49 publications

References 13 publications

Relaxed Reverse Nearest Neighbors Queries

Relaxed Reverse Nearest Neighbors Queries

Efficient Algorithms and Cost Models for Reverse Spatial-Keyword k -Nearest Neighbor Search

Efficient RkNN retrieval with arbitrary non-metric similarity measures

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