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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
            https://doab-dev.siscern.org/handle/20.500.12854/195826
            Keywords
            Computer science; Machine learning; Application software; Optical data processing; thema EDItEUR::U Computing and Information Technology::UB Information technology: general topics; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYT Image processing
            DOI
            10.1007/978-3-030-38081-6
            Publisher
            Springer Nature
            Publisher website
            http://www.springernature.com/oabooks
            Publication date and place
            2020
            Series
            Lecture Notes in Computer Science,
            Pages
            133
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            Credits


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            • logo EUEuropean Union
              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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