Multiple-Aspect Analysis of Semantic Trajectories (häftad)
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Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
133
Utgivningsdatum
2020-01-04
Upplaga
1st ed. 2020
Förlag
Springer Nature Switzerland AG
Medarbetare
Renso, Chiara / Matwin, Stan
Illustrationer
47 Illustrations, color; 46 Illustrations, black and white; IX, 133 p. 93 illus., 47 illus. in color
Dimensioner
234 x 156 x 8 mm
Vikt
213 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783030380809
Multiple-Aspect Analysis of Semantic Trajectories (häftad)

Multiple-Aspect Analysis of Semantic Trajectories

First International Workshop, MASTER 2019, Held in Conjunction with ECML-PKDD 2019, Wurzburg, Germany, September 16, 2019, Proceedings

Häftad Engelska, 2020-01-04
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This open access book constitutes the refereed post-conference proceedings of the First International Workshop on Multiple-Aspect Analysis of Semantic Trajectories, MASTER 2019, held in conjunction with the 19th European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, in Wurzburg, Germany, in September 2019. The 8 full papers presented were carefully reviewed and selected from 12 submissions. They represent an interesting mix of techniques to solve recurrent as well as new problems in the semantic trajectory domain, such as data representation models, data management systems, machine learning approaches for anomaly detection, and common pathways identification.
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Innehållsförteckning

Learning from our Movements - The Mobility Data Analytics Era.- Uncovering hidden concepts from AIS data: A network abstraction of maritime traffic for anomaly detection.- Nowcasting Unemployment Rates with Smartphone GPS data.- Online long-term trajectory prediction based on mined route patterns.- EvolvingClusters: Online Discovery of Group Patterns in Enriched Maritime Data.- Prospective Data Model and Distributed Query Processing for Mobile Sensing Data Streams.- Predicting Fishing Effort and Catch Using Semantic Trajectories and Machine Learning.- A Neighborhood-augmented LSTM Model for Taxi-Passenger Demand Prediction.- Multi-Channel Convolutional Neural Networks for Handling Multi-Dimensional Semantic Trajectories and Predicting Future Semantic Locations.