Journal ArticleDOI
Trajectory Data Mining: An Overview
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TLDR
A systematic survey on the major research into trajectory data mining, providing a panorama of the field as well as the scope of its research topics, and introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors.Abstract:
The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles, and animals. Many techniques have been proposed for processing, managing, and mining trajectory data in the past decade, fostering a broad range of applications. In this article, we conduct a systematic survey on the major research into trajectory data mining, providing a panorama of the field as well as the scope of its research topics. Following a road map from the derivation of trajectory data, to trajectory data preprocessing, to trajectory data management, and to a variety of mining tasks (such as trajectory pattern mining, outlier detection, and trajectory classification), the survey explores the connections, correlations, and differences among these existing techniques. This survey also introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors, to which more data mining and machine learning techniques can be applied. Finally, some public trajectory datasets are presented. This survey can help shape the field of trajectory data mining, providing a quick understanding of this field to the community.read more
Citations
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Journal ArticleDOI
Trajectory data mining in distributed sensor networks
Proceedings ArticleDOI
Online Clustering of Trajectories in Road Networks
Ticiana L. Coelho da Silva,Francesco Lettich,José Antônio Fernandes de Macêdo,Karine Zeitouni,Marco A. Casanova +4 more
TL;DR: This work proposes NET-CUTiS, a novel approach that addresses the problem of discovering and monitor the evolution of clusters of trajectories over road networks from trajectory data streams, and conducts several experiments that demonstrate the validity of the proposal in terms of clustering quality and run-time performance.
Journal ArticleDOI
Local-entity resolution for building location-based social networks by using stay points
TL;DR: A novel approach that uses the coarsening stage of a multilevel optimization scheme to build LBSNs by using stay points is presented and has advantages compared to usual clustering methods to represent real-world features.
Book ChapterDOI
A Top-Down Algorithm with Free Distance Parameter for Mining Top-k Flock Patterns
Denis Evangelista Sanches,Luis Otavio Alvares,Vania Bogorny,Marcos R. Vieira,Daniel S. Kaster +4 more
TL;DR: This work introduces the concept of discovering of k-co-movement patterns, which is finding the top-k patterns, according to the desired raking criterion for the flock pattern, and proposes a top-down algorithm with free distance parameter for the aforementioned problem.
Journal ArticleDOI
Modeling Adversarial Behavior Against Mobility Data Privacy
TL;DR: Li et al. as mentioned in this paper proposed Simulated Privacy Annealing (SPA), a new adversarial behavior model for privacy risk assessment in mobility data, which models the behavior of an adversary as a mobility trajectory and introduces an optimization approach to find the most effective adversary trajectory in terms of privacy risk produced for the individuals represented in a mobility data set.
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Proceedings ArticleDOI
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