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        Multiple-Aspect Analysis of Semantic Trajectories

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

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        Contributor(s)
        Tserpes, Konstantinos (editor)
        Renso, Chiara (editor)
        Matwin, Stan (editor)
        Language
        English
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        Abstract
        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.
        URI
        http://library.oapen.org/handle/20.500.12657/23319
        Keywords
        Computer science; Machine learning; Application software; Optical data processing
        DOI
        10.1007/978-3-030-38081-6
        Publisher
        Springer Nature
        Publisher website
        https://www.springernature.com/gp/products/books
        Publication date and place
        2020
        Series
        Lecture Notes in Computer Science,
        Classification
        Information technology: general topics
        Machine learning
        Image processing
        Pages
        133
        Rights
        https://creativecommons.org/licenses/by/4.0
        • Imported or submitted locally

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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