04278 am 22006973u 450 991037274320332120230125225350.03-030-38081-510.1007/978-3-030-38081-6(CKB)4900000000505168(DE-He213)978-3-030-38081-6(MiAaPQ)EBC6111699(Au-PeEL)EBL6111699(OCoLC)1143625021(oapen)https://directory.doabooks.org/handle/20.500.12854/38740(PPN)242844871(EXLCZ)99490000000050516820200103d2020 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierMultiple-Aspect Analysis of Semantic Trajectories[electronic resource] First International Workshop, MASTER 2019, Held in Conjunction with ECML-PKDD 2019, Würzburg, Germany, September 16, 2019, Proceedings /edited by Konstantinos Tserpes, Chiara Renso, Stan Matwin1st ed. 2020.Springer Nature2020Cham :Springer International Publishing :Imprint: Springer,2020.1 online resource (IX, 133 p. 93 illus., 47 illus. in color.) Lecture Notes in Artificial Intelligence ;118893-030-38080-7 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.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.Lecture Notes in Artificial Intelligence ;11889Machine learningApplication softwareOptical data processingMachine Learninghttps://scigraph.springernature.com/ontologies/product-market-codes/I21010Computer Applicationshttps://scigraph.springernature.com/ontologies/product-market-codes/I23001Image Processing and Computer Visionhttps://scigraph.springernature.com/ontologies/product-market-codes/I22021Computer scienceMachine learningApplication softwareOptical data processingMachine learning.Application software.Optical data processing.Machine Learning.Computer Applications.Image Processing and Computer Vision.006.31Tserpes Konstantinosedt330033Tserpes Konstantinosedthttp://id.loc.gov/vocabulary/relators/edtRenso Chiaraedthttp://id.loc.gov/vocabulary/relators/edtMatwin Stanedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK9910372743203321Multiple-Aspect Analysis of Semantic Trajectories3358440UNINA