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oapen-20.500.12657-233192024-03-22T19:23:42Z Multiple-Aspect Analysis of Semantic Trajectories Tserpes, Konstantinos Renso, Chiara Matwin, Stan 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 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. 2020-03-18 13:36:15 2020-04-01T09:11:21Z 2020-04-01T09:11:21Z 2020 book 1006836 http://library.oapen.org/handle/20.500.12657/23319 eng Lecture Notes in Computer Science application/pdf n/a 1006836.pdf https://www.springer.com/9783030380816 Springer Nature 10.1007/978-3-030-38081-6 10.1007/978-3-030-38081-6 6c6992af-b843-4f46-859c-f6e9998e40d5 133 open access
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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.
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