Efficient reverse spatial and textual k nearest neighbor queries on road networks

Luo, Changyin; Li, Junlin; Li, Guohui; Wei, Wei; Li, Yanhong; Li, Jianjun

doi:10.1016/j.knosys.2015.11.009

Cited by 18 publications

(15 citation statements)

References 21 publications

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“…Yang et al [11] proposed to extend half-space-based pruning technique to solve the spatial reverse top-k queries and introduced a novel regions-based pruning algorithm according to SLICE [72] that is a regions-based pruning algorithm for reverse k nearest neighbors queries to improve the efficiency. Instead in the Euclidean space, Luo et al [73] investigated reverse spatial and textual k nearest neighbor queries on road networks. Besides, they proposed several spatial keyword pruning techniques to speed up the search.…”

Section: Reverse Query Processingmentioning

confidence: 99%

Reverse Spatial Visual Top-$k$ Query

Zhu

Song

et al. 2020

IEEE Access

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With the wide application of mobile Internet techniques an location-based services (LBS), massive multimedia data with geo-tags has been generated and collected. In this paper, we investigate a novel type of spatial query problem, named reverse spatial visual top-k query (RSVQ k) that aims to retrieve a set of geo-images that have the query as one of the most relevant geo-images in both geographical proximity and visual similarity. Existing approaches for reverse top-k queries are not suitable to address this problem because they cannot effectively process unstructured data, such as image. To this end, firstly we propose the definition of RSVQ k problem and introduce the similarity measurement. A novel hybrid index, named VR 2-Tree is designed, which is a combination of visual representation of geo-image and R-Tree. Besides, an extension of VR 2-Tree, called CVR 2-Tree is introduced and then we discuss the calculation of lower/upper bound, and then propose the optimization technique via CVR 2-Tree for further pruning. In addition, a search algorithm named RSVQ k algorithm is developed to support the efficient RSVQ k query. Comprehensive experiments are conducted on four geo-image datasets, and the results illustrate that our approach can address the RSVQ k problem effectively and efficiently. INDEX TERMS Geo-image, reverse top-k query, spatial visual query, hybrid index.

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Section: Reverse Query Processingmentioning

confidence: 99%

Reverse Spatial Visual Top-$k$ Query

Zhu

Song

et al. 2020

IEEE Access

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“…Note that we still use the Euclidean method to calculate the distance between the GPS data points. L-Similarity: In fact, there exist several methods to measure the similarity of line segments, such as those that are reported in References [13,33,34], as well as cosine similarity. Similar results between line segments are usually used to achieve trajectory clustering and to produce sub-trajectories.…”

Section: Sub-trajectorymentioning

confidence: 99%

A Trajectory Regression Clustering Technique Combining a Novel Fuzzy C-Means Clustering Algorithm with the Least Squares Method

Zhou

Miao

et al. 2018

IJGI

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Rapidly growing GPS (Global Positioning System) trajectories hide much valuable information, such as city road planning, urban travel demand, and population migration. In order to mine the hidden information and to capture better clustering results, a trajectory regression clustering method (an unsupervised trajectory clustering method) is proposed to reduce local information loss of the trajectory and to avoid getting stuck in the local optimum. Using this method, we first define our new concept of trajectory clustering and construct a novel partitioning (angle-based partitioning) method of line segments; second, the Lagrange-based method and Hausdorff-based K-means++ are integrated in fuzzy C-means (FCM) clustering, which are used to maintain the stability and the robustness of the clustering process; finally, least squares regression model is employed to achieve regression clustering of the trajectory. In our experiment, the performance and effectiveness of our method is validated against real-world taxi GPS data. When comparing our clustering algorithm with the partition-based clustering algorithms (K-means, K-median, and FCM), our experimental results demonstrate that the presented method is more effective and generates a more reasonable trajectory.

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“…A lot of other Spatial Keyword Queries variants are also based on TkNN queries, in which the works on these variants try to improve the TkNN queries to be able to process moving objects [44], continuous objects [23], reverse top-k query [35], [22], joint queries [43], [26], or interactive TkNN queries [52]. Besides the works on Spatial Keyword Queries that focus on TkNN queries, some variants of Spatial Keyword Queries have also been proposed, such as the collective Spatial Keyword querying [15], [34], [51], diversified Spatial Keyword search [48], regionbased query [16], scalable continual top-k query [46], reverse spatial and textual k nearest neighbor query [36], spatio-textual data clustering [19], fuzzy keyword search [8], and m-closest keyword queries [50]. However, none of these queries can be classified as route planning queries.…”

Section: Spatial Abstract Queriesmentioning

confidence: 99%

IG-Tree: an efficient spatial keyword index for planning best path queries on road networks

et al. 2018

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Due to the popularity of Spatial Databases, many search engine providers have started to expand their text searching capability to include geographical information. Because of this reason, many new queries on spatial objects affiliated with textual information, known as the Spatial Keyword Queries, have taken significant research interest in the past years. Unfortunately, most of existing works on Spatial Keyword Queries only focus on objects retrieval. There is barely any work on route planning queries, even though route planning is often needed in our daily life. In this research, we propose the Best Path Query, which we find the best optimum route from two different spatial locations that visits or avoids the objects that are specified by the textual data given by the user. We show that Best Path Query is an NP-Hard problem. We propose an efficient indexing technique, namely IG-Tree, and three different algorithms with different trade-offs to process the Best Path Queries on Road Networks. Our extensive experimental study demonstrates the efficiency and accuracy of our proposed approach.

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Efficient reverse spatial and textual k nearest neighbor queries on road networks

Cited by 18 publications

References 21 publications

Reverse Spatial Visual Top-$k$ Query

Reverse Spatial Visual Top-$k$ Query

A Trajectory Regression Clustering Technique Combining a Novel Fuzzy C-Means Clustering Algorithm with the Least Squares Method

IG-Tree: an efficient spatial keyword index for planning best path queries on road networks

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