TY  - BOOK
AU  - Tserpes,Konstantinos
AU  - Renso,Chiara
AU  - Matwin,Stan
ED  - SpringerLink (Online service)
TI  - Multiple-Aspect Analysis of Semantic Trajectories: First International Workshop, MASTER 2019, Held in Conjunction with ECML-PKDD 2019, Würzburg, Germany, September 16, 2019, Proceedings 
T2  - Lecture Notes in Artificial Intelligence
SN  - 9783030380816
AV  - Q325.5-.7 
U1  - 006.31 23
PY  - 2020///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Machine learning
KW  - Application software
KW  - Optical data processing
KW  - Machine Learning
KW  - Computer Applications
KW  - Image Processing and Computer Vision
N1  - 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; Open Access
N2  - 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
UR  - https://doi.org/10.1007/978-3-030-38081-6
ER  -