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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Proceedings ArticleDOI
Inferring Semantically Enriched Representative Trajectories
Jana Seep,Jan Vahrenhold +1 more
TL;DR: This work constructs an extended finite state machine describing the spatial and non-spatial properties of the data trajectories in a given cluster and uses this machine to generate a representative trajectory exhibiting characteristic changes in spatial andNon-Spatial properties.
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Multi-Aspect Embedding for Attribute-Aware Trajectories
TL;DR: MAEAT is built upon a sentence embedding algorithm and directly learns whole trajectory embedding via predicting the context aspect tokens when given a trajectory, a representation learning approach for trajectories that simultaneously models the similarities according to their multiple aspects.
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TL;DR: A contact tracing-based stochastic compartment model is implemented, to further modify the existing setup to include the spatial risk, and it is highlighted that the new spatio-SIR model can act as a tool to empower the analyst with a capability to explore disease dynamics from a spatial perspective.
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Visual Exploration of Relationships and Structure in Low-Dimensional Embeddings
TL;DR: In this article , an interactive visual approach for the exploration and formation of structural relationships in embeddings of high-dimensional data is proposed, where users explore enriched scatterplots of the embedding, in which relationships between items and/or groups are visually highlighted.
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