000 | 03796nam a22005895i 4500 | ||
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_a10.1007/978-3-030-38081-6 _2doi |
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_aMultiple-Aspect Analysis of Semantic Trajectories _h[electronic resource] : _bFirst International Workshop, MASTER 2019, Held in Conjunction with ECML-PKDD 2019, Würzburg, Germany, September 16, 2019, Proceedings / _cedited by Konstantinos Tserpes, Chiara Renso, Stan Matwin. |
250 | _a1st ed. 2020. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2020. |
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300 |
_aIX, 133 p. 93 illus., 47 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v11889 |
|
505 | 0 | _aLearning 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. | |
506 | 0 | _aOpen Access | |
520 | _aThis 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. | ||
650 | 0 | _aMachine learning. | |
650 | 0 | _aApplication software. | |
650 | 0 | _aComputer vision. | |
650 | 1 | 4 | _aMachine Learning. |
650 | 2 | 4 | _aComputer and Information Systems Applications. |
650 | 2 | 4 | _aComputer Vision. |
700 | 1 |
_aTserpes, Konstantinos. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
700 | 1 |
_aRenso, Chiara. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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700 | 1 |
_aMatwin, Stan. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783030380809 |
776 | 0 | 8 |
_iPrinted edition: _z9783030380823 |
830 | 0 |
_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v11889 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-38081-6 |
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912 | _aZDB-2-LNC | ||
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_c174409 _d174409 |