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00000cam a2200000 i 4500 |
| 001 |
on1137158171 |
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OCoLC |
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20240909213021.0 |
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m o d |
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200121s2020 sz a o 101 0 eng d |
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|b eng
|e rda
|e pn
|c GW5XE
|d OCLCF
|d UKMGB
|d ESU
|d OCLCQ
|d OCLCA
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|a 019668390
|2 Uk
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|a 1135205428
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|a 9783030380816
|q (electronic bk.)
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|a 3030380815
|q (electronic bk.)
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|z 9783030380809
|q (print)
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|z 3030380807
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7 |
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|a 10.1007/978-3-030-38081-6
|2 doi
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|a (OCoLC)1137158171
|z (OCoLC)1135205428
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| 037 |
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|a com.springer.onix.9783030380816
|b Springer Nature
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| 050 |
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4 |
|a QA76.9.D343
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| 049 |
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|a HCDD
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| 111 |
2 |
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|a MASTER (Workshop)
|n (1st :
|d 2019 :
|c Würzburg, Germany)
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| 245 |
1 |
0 |
|a Multiple-aspect analysis of semantic trajectories :
|b first International Workshop, MASTER 2019, held in conjunction with ECML-PKDD 2019, Würzburg, Germany, September 16, 2019, Proceedings /
|c Konstantinos Tserpes, Chiara Renso, Stan Matwin (eds.).
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| 246 |
3 |
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|a MASTER 2019
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| 264 |
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1 |
|a Cham :
|b Springer Open,
|c 2020.
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| 300 |
|
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|a 1 online resource (ix, 133 pages) :
|b illustrations (some color)
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| 336 |
|
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Lecture notes in artificial intelligence
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| 490 |
1 |
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|a Lecture notes in computer science ;
|v 11889
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| 490 |
1 |
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|a LNCS sublibrary. SL 7, Artificial intelligence
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| 506 |
0 |
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|a Open access.
|5 GW5XE
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| 500 |
|
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|a International conference proceedings.
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| 500 |
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|a Includes author index.
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| 588 |
0 |
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|a Online resource; title from PDF title page (SpringerLink, viewed January 21, 2020).
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| 505 |
0 |
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|a Learning 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.
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| 520 |
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|a 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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| 506 |
0 |
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|a Open Access
|5 EbpS
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| 650 |
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0 |
|a Data mining
|v Congresses.
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| 650 |
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0 |
|a Semantic computing
|v Congresses.
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| 650 |
|
7 |
|a Data mining
|2 fast
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| 650 |
|
7 |
|a Semantic computing
|2 fast
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| 655 |
|
7 |
|a proceedings (reports)
|2 aat
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| 655 |
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7 |
|a Conference papers and proceedings
|2 fast
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| 655 |
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7 |
|a Conference papers and proceedings.
|2 lcgft
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| 655 |
|
7 |
|a Actes de congrès.
|2 rvmgf
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| 700 |
1 |
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|a Tserpes, Konstantinos,
|e editor.
|
| 700 |
1 |
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|a Renso, Chiara,
|d 1968-
|e editor.
|1 https://id.oclc.org/worldcat/entity/E39PBJwhv33QfvXJkGMM3X6qcP
|
| 700 |
1 |
|
|a Matwin, Stan,
|e editor.
|
| 711 |
2 |
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|a ECML PKDD (Conference)
|d (2019 :
|c Würzburg, Germany)
|
| 758 |
|
|
|i has work:
|a Multiple-aspect analysis of semantic trajectories (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCGv8DBpPQGh6HPK7kJkvH3
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
| 776 |
0 |
8 |
|i Print version:
|z 3030380807
|z 9783030380809
|w (OCoLC)1128885135
|
| 830 |
|
0 |
|a Lecture notes in computer science.
|p Lecture notes in artificial intelligence.
|
| 830 |
|
0 |
|a Lecture notes in computer science ;
|v 11889.
|
| 830 |
|
0 |
|a LNCS sublibrary.
|n SL 7,
|p Artificial intelligence.
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-030-38081-6
|y Click for online access
|
| 903 |
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|a SPRING-COMP2020
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| 994 |
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|a 92
|b HCD
|