Progressive Top-K Nearest Neighbors Search in Large Road Networks

Ouyang, Dian; Wen, Dong; Qin, Lu; Chang, Lijun; Zhang, Ying; Lin, Xuemin

doi:10.1145/3318464.3389746

Cited by 26 publications

(11 citation statements)

References 16 publications

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“…Giri et al applied the Knn algorithm to a position prediction system [47]. Dian et al proposed a progressive top-KNN search algorithm [48]. This method optimizes the index structure, thereby improving the efficiency of index maintenance in large networks.…”

Section: Neighbor Searchmentioning

confidence: 99%

KNN Classification with One-step Computation

Zhang¹,

Li²

2021

IEEE Trans. Knowl. Data Eng.

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KNN classification is an improvisational learning mode, in which they are carried out only when a test data is predicted that set a suitable K value and search the K nearest neighbors from the whole training sample space, referred them to the lazy part of KNN classification. This lazy part has been the bottleneck problem of applying KNN classification due to the complete search of K nearest neighbors. In this paper, a one-step computation is proposed to replace the lazy part of KNN classification. The one-step computation actually transforms the lazy part to a matrix computation as follows. Given a test data, training samples are first applied to fit the test data with the least squares loss function. And then, a relationship matrix is generated by weighting all training samples according to their influence on the test data. Finally, a group lasso is employed to perform sparse learning of the relationship matrix. In this way, setting K value and searching K nearest neighbors are both integrated to a unified computation. In addition, a new classification rule is proposed for improving the performance of one-step KNN classification. The proposed approach is experimentally evaluated, and demonstrated that the one-step KNN classification is efficient and promising.

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Section: Neighbor Searchmentioning

confidence: 99%

KNN Classification with One-step Computation

Zhang¹,

Li²

2021

IEEE Trans. Knowl. Data Eng.

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“…(Spatial network [ 3 , 9 , 11 , 25 , 26 , 41 , 50 , 51 ]) . A dynamic spatial network can be described as a dynamic weighted graph

, where V, E, and W indicate the vertex set, edge set, and edge distance matrix, respectively.…”

Section: Preliminariesmentioning

confidence: 99%

“…NN queries retrieve points of interest (POI), such as taxis and restaurants, closest to a query user [ 1 , 2 ], and RN queries retrieve POIs within a query distance [ 3 , 4 , 5 ]. Typically, location-based services (LBS), such as taxi-booking and ride-sharing services, use real-time spatial data to locate POIs close to the query user [ 6 , 7 , 8 , 9 , 10 ]. When multiple SP queries reach an LBS server simultaneously at peak times, if the SP queries are processed sequentially, it may not be possible to provide prompt responses to the query users.…”

Section: Introductionmentioning

confidence: 99%

A Unified Approach to Spatial Proximity Query Processing in Dynamic Spatial Networks

Cho¹

2021

Sensors

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Nearest neighbor (NN) and range (RN) queries are basic query types in spatial databases. In this study, we refer to collections of NN and RN queries as spatial proximity (SP) queries. At peak times, location-based services (LBS) need to quickly process SP queries that arrive simultaneously. Timely processing can be achieved by increasing the number of LBS servers; however, this also increases service costs. Existing solutions evaluate SP queries sequentially; thus, such solutions involve unnecessary distance calculations. This study proposes a unified batch algorithm (UBA) that can effectively process SP queries in dynamic spatial networks. With the proposed UBA, the distance between two points is indicated by the travel time on the shortest path connecting them. The shortest travel time changes frequently depending on traffic conditions. The goal of the proposed UBA is to avoid unnecessary distance calculations for nearby SP queries. Thus, the UBA clusters nearby SP queries and exploits shared distance calculations for query clusters. Extensive evaluations using real-world roadmaps demonstrated the superiority and scalability of UBA compared with state-of-the-art sequential solutions.

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“…Giri et al applied the Knn algorithm to a position prediction system [46]. Dian et al proposed a progressive top-KNN search algorithm [47]. This method optimizes the index structure, thereby improving the efficiency of index maintenance in large networks.…”

Section: Neighbor Searchmentioning

confidence: 99%

KNN Classification with One-step Computation

Zhang,

2020

Preprint

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KNN classification is a query triggered yet improvisational learning mode, in which they are carried out only when a test data is predicted that set a suitable K value and search the K nearest neighbors from the whole training sample space, referred them to the lazy part of KNN classification. This lazy part has been the bottleneck problem of applying KNN classification. In this paper, a one-step computation is proposed to replace the lazy part of KNN classification. The one-step computation actually transforms the lazy part to a matrix computation as follows. Given a test data, training samples are first applied to fit the test data with the least squares loss function. And then, a relationship matrix is generated by weighting all training samples according to their influence on the test data. Finally, a group lasso is employed to perform sparse learning of the relationship matrix. In this way, setting K value and searching K nearest neighbors are both integrated to a unified computation. In addition, a new classification rule is proposed for improving the performance of one-step KNN classification. The proposed approach is experimentally evaluated, and demonstrated that the one-step KNN classification is efficient and promising.

show abstract

Progressive Top-K Nearest Neighbors Search in Large Road Networks

Cited by 26 publications

References 16 publications

KNN Classification with One-step Computation

KNN Classification with One-step Computation

A Unified Approach to Spatial Proximity Query Processing in Dynamic Spatial Networks

KNN Classification with One-step Computation

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