Scalable algorithms for nearest-neighbor joins on big trajectory data

Fang, Yixiang; Cheng, Reynold; Tang, Wenbin; Maniu, Silviu; Yang, Xuan

doi:10.1109/icde.2016.7498408

Cited by 22 publications

(28 citation statements)

References 2 publications

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“…Then, by performing binary search in the temporally sorted list, we can find the position of q j and can easily get q j−1 . Having that, we need to find if it "matches" with any point that belongs to p. Here, instead of scanning the whole 2 t window of q j−1 in order to check for "matches" with p, we perform a search in TrI in order to get the points of p that exist "close" to the time of q j−1 (lines 12,15). Then, if the spatial and temporal constraints are satisfied we have a "match" and the FindMatch I () method returns True.…”

Section: The Dtji Algorithmmentioning

confidence: 99%

“…Depending on the outcome of the duplicate avoidance technique, pairs ((D[j], D[i]), T rue) are also output. Second, it discovers points that belong to the candidate sN JP set by examining whether the previous trajectory point (getPrevTrPoint)) of D[j] (and D[i]), say D[k], is a N JP (FindMatch) with each point ∈ D[i].trajID (D[j].trajID, respectively) (lines[11][12][13][14][15][16][17]. In case such points are identified, they are output with a different flag ((D[i], D[k]), F alse) to differentiate them from JP .…”

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confidence: 99%

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Distributed Subtrajectory Join on Massive Datasets

Tampakis

Doulkeridis

Pelekis

et al. 2020

ACM Trans. Spatial Algorithms Syst.

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Joining trajectory datasets is a significant operation in mobility data analytics and the cornerstone of various methods that aim to extract knowledge out of them. In the era of Big Data, the production of mobility data has become massive and, consequently, performing such an operation in a centralized way is not feasible. In this paper, we address the problem of Distributed Subtrajectory Join processing by utilizing the MapReduce programming model. Compared to traditional trajectory join queries, this problem is even more challenging since the goal is to retrieve all the "maximal" portions of trajectories that are "similar". We propose three solutions: (i) a well-designed basic solution, coined DTJb, (ii) a solution that uses a preprocessing step that repartitions the data, labeled DTJr , and (iii) a solution that, additionally, employs an indexing scheme, named DTJi . In our experimental study, we utilize a 56GB dataset of real trajectories from the maritime domain, which, to the best of our knowledge, is the largest real dataset used for experimentation in the literature of trajectory data management. The results show that DTJi performs up to 16× faster compared with DTJb, 10× faster than DTJr and 3× faster than the closest related state of the art algorithm.

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Section: The Dtji Algorithmmentioning

confidence: 99%

mentioning

confidence: 99%

Distributed Subtrajectory Join on Massive Datasets

Tampakis

Doulkeridis

Pelekis

et al. 2020

ACM Trans. Spatial Algorithms Syst.

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show abstract

“…We demonstrate that d Q 's sketched representation of the trajectories in R |Q | allows for extremely efficient k-nearest neighbor search. We consider two representative methods [19,23] for comparison; but do all, e.g., [7]) which require timing information.…”

Section: Using D Q In Nearest Neighbor Searchmentioning

confidence: 99%

Simple Distances for Trajectories via Landmarks

Phillips

Tang

2019

Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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We develop a new class of distances for objects including lines, hyperplanes, and trajectories, based on the distance to a set of landmarks. These distances easily and interpretably map objects to a Euclidean space, are simple to compute, and perform well in data analysis tasks. For trajectories, they match and in some cases significantly out-perform all state-of-the-art other metrics, can effortlessly be used in k-means clustering, and directly plugged into approximate nearest neighbor approaches which immediately out-perform the best recent advances in trajectory similarity search by several orders of magnitude. These distances do not require a geometry distorting dual (common in the line or halfspace case) or complicated alignment (common in trajectory case). We show reasonable and often simple conditions under which these distances are metrics.

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“…With the emergence of geo-social networks, such as Twitter and Foursquare, the topic of geo-social networks has gained a lot of attention [1,30,26,12]. In these networks, a user is often associated with location information (e.g., positions of her hometown and check-ins).…”

Section: Introductionmentioning

confidence: 99%

Effective community search over large spatial graphs

et al. 2017

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Communities are prevalent in social networks, knowledge graphs, and biological networks. Recently, the topic of community search (CS) has received plenty of attention. Given a query vertex, CS looks for a dense subgraph that contains it. Existing CS solutions do not consider the spatial extent of a community. They can yield communities whose locations of vertices span large areas. In applications that facilitate the creation of social events (e.g., finding conference attendees to join a dinner), it is important to find groups of people who are physically close to each other. In this situation, it is desirable to have a spatial-aware community (or SAC), whose vertices are close structurally and spatially. Given a graph G and a query vertex q, we develop exact solutions for finding an SAC that contains q. Since these solutions cannot scale to large datasets, we have further designed three approximation algorithms to compute an SAC. We have performed an experimental evaluation for these solutions on both large real and synthetic datasets. Experimental results show that SAC is better than the communities returned by existing solutions. Moreover, our approximation solutions can find SACs accurately and efficiently.

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Scalable algorithms for nearest-neighbor joins on big trajectory data

Cited by 22 publications

References 2 publications

Distributed Subtrajectory Join on Massive Datasets

Distributed Subtrajectory Join on Massive Datasets

Simple Distances for Trajectories via Landmarks

Effective community search over large spatial graphs

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