Multiple-Aspect Analysis of Semantic Trajectories

First International Workshop, MASTER 2019, Held in Conjunction with ECML-PKDD 2019, Würzburg, Germany, September 16, 2019, Proceedings
 
 
Springer (Verlag)
  • 1. Auflage
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  • erschienen am 4. Januar 2020
 
  • Buch
  • |
  • Softcover
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  • 144 Seiten
978-3-030-38080-9 (ISBN)
 
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 Würzburg, 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.
1st ed. 2020
  • Englisch
  • Cham
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  • Schweiz
Springer International Publishing
  • Für Beruf und Forschung
  • 47 farbige Abbildungen, 46 s/w Abbildungen
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  • 47 Illustrations, color; 46 Illustrations, black and white; IX, 133 p. 93 illus., 47 illus. in color.
  • Höhe: 235 mm
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  • Breite: 155 mm
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  • Dicke: 8 mm
  • 230 gr
978-3-030-38080-9 (9783030380809)
10.1007/978-3-030-38081-6
weitere Ausgaben werden ermittelt

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.


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