Optimizing All-Nearest-Neighbor Queries with Trigonometric Pruning

Emrich, Tobias; Graf, Franz; Kriegel, Hans‐Peter; Schubert, Matthias; Thoma, Marisa

doi:10.1007/978-3-642-13818-8_35

Cited by 22 publications

(25 citation statements)

References 17 publications

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“…A particular case of kneighborhood join is all-nearest-neighbor query, where the parameter k equals to 1. They have been extensively studied in free space [35], [36], [37], [38], whereas our problem focuses on the constrained space.…”

Section: Related Workmentioning

confidence: 99%

Distance-Aware Join for Indoor Moving Objects

Xie

Pedersen

2015

IEEE Trans. Knowl. Data Eng.

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Indoor spaces accommodate large parts of people's lives. Relevant techniques are thus needed to efficiently manage indoor moving objects, whose positions are detected by technologies, such as Assisted GPS, Wi-Fi, RFID, and Bluetooth. Among such techniques, the distance-aware join processing is of importance in practice for indoor spatial databases. Such join operators leverage a series of applications, such as indoor mobile service and facility monitoring. However, distance-aware joining over indoor moving objects is challenging because: (1) indoor spaces are characterized by many special entities and thus render distance calculation very complex; (2) the limitations of indoor positioning technologies create inherent uncertainties in indoor moving objects data. In this paper, we study two representative join predicates in indoor settings, semi-range join and semi-neighborhood join. To implement them, we define and categorize the indoor distances between indoor uncertain objects, and derive different distance bounds that can facilitate the join processing. We design a composite index scheme that integrates indoor geometries, indoor topologies, as well as indoor uncertain objects, and thus supports the join processing efficiently. The results of extensive experimental studies demonstrate that our proposals are efficient and scalable in evaluating distance-aware join over indoor moving objects.

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Section: Related Workmentioning

confidence: 99%

Distance-Aware Join for Indoor Moving Objects

Xie

Pedersen

2015

IEEE Trans. Knowl. Data Eng.

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“…Index-based algorithms assume that at least the data set is organized in an index structure. Index-based algorithms for computing all k -nearest neighbor are provided in [2,8,12,35,[42][43][44] . Algorithms for solving all k -nearest neighbor queries without using index structures are found in [40,45] .…”

Section: Related Workmentioning

confidence: 99%

Solving multiple kth smallest dissimilarity queries for non-metric dissimilarities with the GPU

Fort

Sellarès

2016

Information Sciences

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“…The work of Emrich et al [13] improves the performance of AkNNQ (All k-nearest neighbor queries), developing a technique to prune branches that exploits trigonometric properties to reduce the search space, based on spherical regions. That technique was also employed by Jacox [14] to improve similarity join algorithms over high dimensional datasets, leading to the method called QuickJoin.…”

Section: Related Workmentioning

confidence: 99%