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020 _a9783030380816
_9978-3-030-38081-6
024 7 _a10.1007/978-3-030-38081-6
_2doi
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072 7 _aUYQM
_2bicssc
072 7 _aMAT029000
_2bisacsh
072 7 _aUYQM
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082 0 4 _a006.31
_223
245 1 0 _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.
300 _aIX, 133 p. 93 illus., 47 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
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
700 1 _aMatwin, Stan.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
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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