Searching Trajectories by Regions of Interest

Shang, Shuo; Chen, Lisi; Jensen, Christian S.; Wen, Ji-Rong; Kalnis, Panos

doi:10.1109/tkde.2017.2685504

Cited by 89 publications

(37 citation statements)

References 23 publications

(93 reference statements)

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“…Notice that the modeling of spatial networks and trajectories align with previous studies [14,15,17,18].…”

Section: Definition: Trajectorysupporting

confidence: 80%

“…If two nodes n and n have the same parent node and their child nodes are not in H, we merge the results for n, n , and their parent node (e.g., n3, n4, and n14 in Figure 2) and store the qualified trajectory pairs in n.parent. Next, we add n.parent to H, and remove n and n from H (lines [12][13][14][15][16][17][18]. If H = 1, the root node is reached, and all qualified trajectory pairs are returned (lines 19-20).…”

mentioning

confidence: 99%

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Trajectory similarity join in spatial networks

Chen²,

et al. 2017

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The matching of similar pairs of objects, called similarity join, is fundamental functionality in data management. We consider the case of trajectory similarity join (TS-Join), where the objects are trajectories of vehicles moving in road networks. Thus, given two sets of trajectories and a threshold θ, the TS-Join returns all pairs of trajectories from the two sets with similarity above θ. This join targets applications such as trajectory near-duplicate detection, data cleaning, ridesharing recommendation, and traffic congestion prediction.With these applications in mind, we provide a purposeful definition of similarity. To enable efficient TS-Join processing on large sets of trajectories, we develop search space pruning techniques and take into account the parallel processing capabilities of modern processors. Specifically, we present a two-phase divideand-conquer algorithm. For each trajectory, the algorithm first finds similar trajectories. Then it merges the results to achieve a final result. The algorithm exploits an upper bound on the spatiotemporal similarity and a heuristic scheduling strategy for search space pruning. The algorithm's per-trajectory searches are independent of each other and can be performed in parallel, and the merging has constant cost. An empirical study with real data offers insight in the performance of the algorithm and demonstrates that is capable of outperforming a well-designed baseline algorithm by an order of magnitude.

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“…Notice that the modeling of spatial networks and trajectories align with previous studies [14,15,17,18].…”

Section: Definition: Trajectorysupporting

confidence: 80%

mentioning

confidence: 99%

Trajectory similarity join in spatial networks

Chen²,

et al. 2017

Self Cite

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show abstract

“…If h = 1, the root node n c is reached, and all (trajectory, location) pairs stored in P nc are returned. Otherwise, we repeat the procedure for the next level of the tree (line [15][16][17].…”

Section: Algorithm and Time Complexitymentioning

confidence: 99%

“…Then, we remove τ from the partly scanned trajectory set T p and update the value of UB S (lines 8-13). If τ is partly scanned (0 < τ.k < k τ ), we compute its spatial correlation upper bound C S (τ, o).ub, and we update the value of the global spatial upper bound UB S (lines [14][15][16][17]. If the value of UB S does not exceed that of θ, the expansion from o terminates (lines [18][19][20].…”

Section: Algorithm and Time Complexitymentioning

confidence: 99%

“…Existing trajectory-to-location matching studies typically consider (i) matching solely in the spatial domain [15], [18], [20]- [22], [25], [28] or (ii) use point-to-point matching [18], [19], [21], [28] in the spatial or temporal domain. For the first case, the matching results do not support time-aware applications, while for the second case, the matched (trajectory, location) pairs are unable to capture the continuous correlations between trajectories and locations in the spatial and temporal domains.…”

Section: Trajectory-to-location Matchingmentioning

confidence: 99%

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Parallel Trajectory-to-Location Join

Shang

Chen

Zheng

et al. 2019

IEEE Trans. Knowl. Data Eng.

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The matching between trajectories and locations, called Trajectory-to-Location join (TL-Join), is fundamental functionality in spatiotemporal data management. Given a set of trajectories, a set of locations, and a threshold θ, the TL-Join finds all (trajectory, location) pairs from the two sets with spatiotemporal correlation above θ. This join targets diverse applications, including location recommendation, event tracking, and trajectory activity analyses. We address three challenges in relation to the TL-Join: how to define the spatiotemporal correlation between trajectories and locations, how to prune the search space effectively when computing the join, and how to perform the computation in parallel. Specifically, we define new metrics to measure the spatiotemporal correlation between trajectories and locations. We develop a novel parallel collaborative (PCol) search method based on a divide-and-conquer strategy. For each location o, we retrieve the trajectories with high spatiotemporal correlation to o, and then we merge the results. An upper bound on the spatiotemporal correlation and a heuristic scheduling strategy are developed to prune the search space. The trajectory searches from different locations are independent and are performed in parallel, and the result merging cost is independent of the degree of parallelism. Studies of the performance of the developed algorithms using large spatiotemporal data sets are reported.

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Location-Based Top-k Term Querying over Sliding Window

Chen

Yao

et al. 2017

Lecture Notes in Computer Science

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In part due to the proliferation of GPS-equipped mobile devices, massive volumes of geo-tagged streaming text messages are becoming available on social media. It is of great interest to discover most frequent nearby terms from such tremendous stream data. In this paper, we present novel indexing, updating, and query processing techniques that are capable of discovering top-k locally popular nearby terms over a sliding window. Specifically, given a query location and a set of geo-tagged messages within a sliding window, we study the problem of searching for the top-k terms by considering both the term frequency and the proximities between the messages containing the term and the query location. We develop a novel and efficient mechanism to solve the problem, including a quad-tree based indexing structure, indexing update technique, and a best-first based searching algorithm. An empirical study is conducted to show that our proposed techniques are efficient and fit for users' requirements through varying a number of parameters.

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Searching Trajectories by Regions of Interest

Cited by 89 publications

References 23 publications

Trajectory similarity join in spatial networks

Trajectory similarity join in spatial networks

Parallel Trajectory-to-Location Join

Location-Based Top-k Term Querying over Sliding Window

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