Multiple-Aspect Analysis of Semantic Trajectories: Lecture Notes in Computer Science, cartea 11889
Editat de Konstantinos Tserpes, Chiara Renso, Stan Matwinen Limba Engleză Paperback – 4 ian 2020
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.
| Toate formatele și edițiile | Preț | Express |
|---|---|---|
| Paperback (2) | 206.26 lei 6-8 săpt. | |
| Saint Philip Street Press – 7 oct 2020 | 206.26 lei 6-8 săpt. | |
| Springer – 4 ian 2020 | 398.91 lei 6-8 săpt. | |
| Hardback (1) | 278.68 lei 6-8 săpt. | |
| Saint Philip Street Press – 7 oct 2020 | 278.68 lei 6-8 săpt. |
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Specificații
ISBN-13: 9783030380809
ISBN-10: 3030380807
Pagini: 144
Ilustrații: IX, 133 p. 93 illus., 47 illus. in color.
Dimensiuni: 155 x 235 x 9 mm
Greutate: 0.23 kg
Ediția:1st ed. 2020
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science
Locul publicării:Cham, Switzerland
ISBN-10: 3030380807
Pagini: 144
Ilustrații: IX, 133 p. 93 illus., 47 illus. in color.
Dimensiuni: 155 x 235 x 9 mm
Greutate: 0.23 kg
Ediția:1st ed. 2020
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science
Locul publicării:Cham, Switzerland
Cuprins
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.