Extended Kalman Filter for Large Scale Vessels Trajectory Tracking in Distributed Stream Processing Systems-Reference-Cited by-同舟云学术

Extended Kalman Filter for Large Scale Vessels Trajectory Tracking in Distributed Stream Processing Systems

Published:2020 Issue: Volume: Page:151-166
ISSN:0302-9743
Container-title:Advanced Analytics and Learning on Temporal Data
language:
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Author:

Juraszek Katarzyna,Saini Nidhi,Charfuelan Marcela,Hemsen Holmer,Markl Volker

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-39098-3_12

Reference25 articles.

1. DEBS 2018 Grand Challenge. http://www.cs.otago.ac.nz/debs2018/calls/gc.html . Accessed 27 Nov 2018

2. Hadoop. http://hadoop.apache.org/ . Accessed 27 Nov 2018

3. Alessandrini, A., et al.: Mining vessel tracking data for maritime domain applications. In: 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), pp. 361–367. IEEE (2016)

4. Apache Flink: Application Development, Working with State. https://ci.apache.org/projects/flink/flink-docs-stable/dev/stream/state/state.html . Accessed 10 June 2019

5. Apache Flink: Configuration. https://ci.apache.org/projects/flink/flink-docs-release-1.8/ops/config.html#configuring-taskmanager-processing-slots . Accessed 10 June 2019

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