Searching trajectories by locations

Chen, Zaiben; Shen, Heng Tao; Zhou, Xiaofang; Zheng, Yu; Xie, Xing

doi:10.1145/1807167.1807197

Cited by 194 publications

(180 citation statements)

References 17 publications

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“…K-BCT Query: Definition and Basic Implementation Procedure K-BCT aims to find the most similar trajectories to a given query trajectory (Q) from a set of reference trajectories (R). Definition 1 describes the calculation of the similarity value [25].…”

Section: Mbr and Mindist For Efficient Searchingmentioning

confidence: 99%

Efficient Parallel K Best Connected Trajectory (K-BCT) Query with GPGPU: A Combinatorial Min-Distance and Progressive Bounding Box Approach

Wang

Zhang

et al. 2018

IJGI

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Performing similarity analysis on trajectories consisting of massive numbers of tracking points is computationally challenging. We introduce a progressive minimum bounding rectangle (MBR) and minimum distance (MINDIST) approach to process the K Best Connected Trajectory (K-BCT) query, which aims to find the top K similarity trajectories to a given query trajectory. Our approach has three unique features to speed up the query. First, the approach builds a series of progressive MBRs from the query trajectory to determine the order of reference trajectories to identify the target top K reference trajectories at an earlier stage. Second, this method introduces a grid-based search method to speed up the matched point detection between two trajectories for similarity measures. Third, this approach further leverages the many-core computing power of Graphical Processing Unit (GPU) devices to perform the query in a parallel manner. We have conducted tests with ship tracking data and human movement data using GPU instances from Amazon Web Services. Preliminary results indicate that (a) parallel computing has greatly improved the efficiency of the query, and (b) our optimized approach can further speedup the computation compared to parallel implementations.

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Section: Mbr and Mindist For Efficient Searchingmentioning

confidence: 99%

Efficient Parallel K Best Connected Trajectory (K-BCT) Query with GPGPU: A Combinatorial Min-Distance and Progressive Bounding Box Approach

Wang

Zhang

et al. 2018

IJGI

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“…For each potential i-subset A, we compute and record its lower bound GT C(A).lb and upper bound GT C(A).ub (lines [3][4]. If the value of GT C(A).ub is less than that of UB , the value of UB is set to GT C(A).ub (lines [5][6]. If the value of GT C(A).lb is no greater than that of UB , A is put into a heap H sorted according to the value of GT C(A).lb (line 7-8).…”

Section: Algorithmmentioning

confidence: 99%

Collective Travel Planning in Spatial Networks

Shang

Chen

Wei

et al. 2017

2017 IEEE 33rd International Conference on Data Engineering (ICDE)

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“…Some researchers focus on searching/querying trajectories. Chen et al proposed an algorithm to find the k Best Connected Trajectories (k-BCT), query for searching trajectories by multiple geographical locations [11]. Generally, the k-BCT query is a set of locations indicated by coordinates like {latitude, longitude}, with those type of queries, a nameless beach or any arbitrary place approximated by the centre location.…”

Section: Related Workmentioning

confidence: 99%

Classification of Multi-Dimensional Trajectories Using Textual Approximation

Xuan¹,

Huang²,

Kawagoe³

2014

IJCCE

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Abstract-Classification and searching time series in the multidimensional space is a big challenge. There has been much research work on this field for many years. TAX (Textual ApproXimation) is a method for searching time series data such as stock or electrocardiogram data. The main idea behind TAX is extract a set of temporal-terms from time series data to approximate using document retrieval methods. The main problem of applying TAX to multidimensional data, such as moving object trajectory data is that how we extract temporal-terms from multidimensional data. In this paper, we propose a novel method to obtain temporal-terms by decomposing original multidimensional data into smaller segmentations, deploying a clustering method to group those segmentations into groups, and assigning a temporal-term for each group. Our research focuses on two-dimensional moving object, representing trajectories and on classification of a large set of the moving object trajectories. The experimental results confirm that our proposed method is effective in multi-multidimensional data classification.

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Searching trajectories by locations

Cited by 194 publications

References 17 publications

Efficient Parallel K Best Connected Trajectory (K-BCT) Query with GPGPU: A Combinatorial Min-Distance and Progressive Bounding Box Approach

Efficient Parallel K Best Connected Trajectory (K-BCT) Query with GPGPU: A Combinatorial Min-Distance and Progressive Bounding Box Approach

Collective Travel Planning in Spatial Networks

Classification of Multi-Dimensional Trajectories Using Textual Approximation

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