ACM Transactions on Intelligent Systems and Technology· 2015Q1· Review
Trajectory Data Mining
- 1,631citations
- Q1SCImago
- 2015year
Short summary
This survey systematically reviews trajectory data mining techniques, covering data derivation, preprocessing, management, and diverse mining tasks like pattern mining and outlier detection.
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Key points
- Reviews techniques for processing, managing, and mining massive spatial trajectory data.
- Covers the entire pipeline from trajectory data derivation to preprocessing and management.
- Explores mining tasks such as trajectory pattern mining, outlier detection, and classification.
- Discusses data transformation methods (graphs, matrices, tensors) for applying advanced ML techniques.
- Presents public trajectory datasets for research use.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. ACM Transactions on Intelligent Systems and Technology, 2015 · DOI ↗
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Field: Signal Processing
Signal ProcessingComputer Science