Trajectory similarity join in spatial networks

Shang, Shuo; Chen, Lisi; Wei, Zhewei; Jensen, Christian S.; Zheng, Kai; Kalnis, Panos

doi:10.14778/3137628.3137630

Cited by 126 publications

(78 citation statements)

References 20 publications

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“…Aside from Hadoop, there are other parallel approaches. In the most recent work [26,27], Shang et al find similar trajectories and group them together in parallel. Compared to their work, which requires multiple passes over the data, our approach is capable of scanning the dataset only once and therefore further reducing the computation time and increasing utility.…”

Section: Processing Location Data In Parallelmentioning

confidence: 99%

A Parallel Algorithm For Anonymizing Large-scale Trajectory Data

Ward

Lin

Madria

2020

ACM/IMS Trans. Data Sci.

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With the proliferation of location-based services enabled by a large number of mobile devices and applications, the quantity of location data, such as trajectories collected by service providers, is gigantic. If these datasets could be published, then they would be valuable assets to various service providers to explore business opportunities, to study commuter behavior for better transport management, which in turn benefits the general public for day-today commuting. However, there are two major concerns that considerably limit the availability and the usage of these trajectory datasets. The first is the threat to individual privacy, as users' trajectories may be misused to discover sensitive information, such as home locations, their children's school locations, or social information like habits or relationships. The other concern is the ability to analyze the exabytes of location data in a timely manner. Although there have been trajectory anonymization approaches proposed in the past to mitigate privacy concerns. None of these prior works address the scalability issue, since it is a newly occurring problem brought by the significantly increasing adoption of location-based services. In this article, we conquer these two challenges by designing a novel parallel trajectory anonymization algorithm that achieves scalability, strong privacy protection, and high utility rate of the anonymized trajectory datasets. We have conducted extensive experiments using MapReduce and Spark on real maps with different topologies, and our results prove both effectiveness and efficiency when compared with the centralized approaches. CCS Concepts: • Security and privacy → Social aspects of security and privacy; Privacy protections; • Computing methodologies → MapReduce algorithms; Massively parallel algorithms;

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Section: Processing Location Data In Parallelmentioning

confidence: 99%

A Parallel Algorithm For Anonymizing Large-scale Trajectory Data

Ward

Lin

Madria

2020

ACM/IMS Trans. Data Sci.

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“…To the best of our knowledge, this is the first trajectoryto-location matching study that takes into account both the spatial and temporal ranges when computing spatial and temporal correlations. We use a linear method [16], [18], [19] to combine the spatial and temporal correlations into a spatiotemporal correlation metric. In contrast, existing studies typically perform (i) the matching solely in the spatial domain [18], [20], [21], [25], [28] or (ii) using point-to-point matching in the spatial domain or the temporal domain [18], [19], [21], [28].…”

Section: θmentioning

confidence: 99%

“…Next, the algorithm used for computing the TS-Join [16] cannot process the TL-Join because the query arguments are different (two trajectory sets vs. a trajectory and a location sets) and because the matching functions are different (pointto-point matching vs. range matching). The TL-Join needs its own specific solutions.…”

Section: θmentioning

confidence: 99%

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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“…Li et al [19] proposed a prefix tree index to join multi-attribute Data. Shang et al [31] applied PSJ in trajectory similarity join in spatial networks via some search space pruning techniques. Bohm et al [5] proposed a join approach for massive high-dimensional data, based on a particular order of data points via a grid.…”

Section: Related Workmentioning

confidence: 99%

Efficient Join Processing Over Incomplete Data Streams

Ren

Lian

Ghazinour

2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

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For decades, the join operator over fast data streams has always drawn much attention from the database community, due to its wide spectrum of real-world applications, such as online clustering, intrusion detection, sensor data monitoring, and so on. Existing works usually assume that the underlying streams to be joined are complete (without any missing values). However, this assumption may not always hold, since objects from streams may contain some missing attributes, due to various reasons such as packet losses, network congestion/failure, and so on. In this paper, we formalize an important problem, namely join over incomplete data streams (Join-iDS), which retrieves joining object pairs from incomplete data streams with high confidences. We tackle the Join-iDS problem in the style of "data imputation and query processing at the same time". To enable this style, we design an effective and efficient cost-modelbased imputation method via deferential dependency (DD), devise effective pruning strategies to reduce the Join-iDS search space, and propose efficient algorithms via our proposed cost-model-based data synopsis/indexes. Extensive experiments have been conducted to verify the efficiency and effectiveness of our proposed Join-iDS approach on both real and synthetic data sets. Figure 1 illustrates two critical routers, O and U , in an IP network, from which we collect statistical (log) attributes in a streaming manner, for example, No. of connections, the connection duration, and the transferred data size. In practice, due to packet losses, network congestion/delays, or hardware failure, we may not always obtain all attributes from each router. As an example in Table 1, the transferred data size of router o t is missing (denoted as "-") at timestamp t. As a result, stream data collected from each router may sometimes contain incomplete attributes. One critical, yet challenging, problem in the network is to monitor network traffic, and detect potential network intrusion. If one router (e.g., O) is under the attack of network intrusion, we should quickly identify potential attacks in other routers, like U , at close timestamps, to which we may take actions for protecting the network security. In

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Trajectory similarity join in spatial networks

Cited by 126 publications

References 20 publications

A Parallel Algorithm For Anonymizing Large-scale Trajectory Data

A Parallel Algorithm For Anonymizing Large-scale Trajectory Data

Parallel Trajectory-to-Location Join

Efficient Join Processing Over Incomplete Data Streams

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