Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Marios Vodas"'
Autor:
Alexandros Troupiotis-Kapeliaris, Dimitris Zissis, Konstantina Bereta, Marios Vodas, Giannis Spiliopoulos, Giannis Karantaidis
Publikováno v:
Remote Sensing, Vol 15, Iss 21, p 5080 (2023)
Density maps support a bird’s eye view of vessel traffic, through providing an overview of vessel behavior, either at a regional or global scale in a given timeframe. However, any inaccuracies in the underlying data, due to sensor noise or other fa
Externí odkaz:
https://doaj.org/article/992b3b69c9f04c15add3713242af839a
Publikováno v:
IEEE Access, Vol 8, Pp 47556-47568 (2020)
In this work we propose a novel spatial knowledge discovery pipeline capable of automatically unravelling the “roads of the sea” and maritime traffic patterns by analysing voluminous vessel tracking data, as collected through the Automatic Identi
Externí odkaz:
https://doaj.org/article/36c3ad400573484693c75daa53f58a0b
Autor:
Gabriele Ferri, Raffaele Grasso, Alessandro Faggiani, Francesca de Rosa, Elena Camossi, Alberto Grati, Pietro Stinco, Alessandra Tesei, Robert Been, Kevin D. LePage, Konstantina Bereta, Marios Vodas, Dimitris Zissis
Publikováno v:
OCEANS 2022, Hampton Roads.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::279e34fde285b05e7e10b839c3c61284
https://doi.org/10.1109/oceans47191.2022.9977142
https://doi.org/10.1109/oceans47191.2022.9977142
Publikováno v:
Proceedings of the 12th Hellenic Conference on Artificial Intelligence.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::520bbdf2fe7812735d8652235893d74c
https://doi.org/10.1145/3549737.3549801
https://doi.org/10.1145/3549737.3549801
Autor:
Marios Vodas, Konstantina Bereta, Dimitris Kladis, Dimitris Zissis, Emmanouil Ntoulias, Elias Alevizos, Alexander Artikis, David Arnu, Edwin Yaqub, Fabian Temme, Mate Torok, Ralf Klinkenberg
We present a Maritime Situational Awareness (MSA) framework for detecting and forecasting maritime events (e.g., illegal fishing) over streams of Big maritime Data. The architecture of the MSA framework relies on the following state-of-the-art compon
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3325ccdfe1ccdbf0ce04735db941a5bd
Autor:
Marios Vodas, Konstantina Bereta, Dimitris Kladis, Dimitris Zissis, Elias Alevizos, Emmanouil Ntoulias, Alexander Artikis, Antonios Deligiannakis, Antonios Kontaxakis, Nikos Giatrakos, David Arnu, Edwin Yaqub, Fabian Temme, Mate Torok, Ralf Klinkenberg
Publikováno v:
2021 IEEE International Conference on Big Data (Big Data).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5038bd3515b6ae96ce152775512d889a
https://doi.org/10.1109/bigdata52589.2021.9671732
https://doi.org/10.1109/bigdata52589.2021.9671732
Publikováno v:
Big Data Analytics for Time-Critical Mobility Forecasting ISBN: 9783030451639
Big Data Analytics for Time-Critical Mobility Forecasting
Big Data Analytics for Time-Critical Mobility Forecasting
Numerous illegal and dangerous activities take place at sea, including violations of ship emission rules, illegal fishing, illegal discharges of oil and garbage, smuggling, piracy and more. We present our efforts to combine two stream reasoning techn
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::29050a9d54b1a0d25d4473d3e9eb17ba
https://doi.org/10.1007/978-3-030-45164-6_9
https://doi.org/10.1007/978-3-030-45164-6_9
Autor:
Ioannis Kontopoulos, Marios Vodas, Giannis Spiliopoulos, Dimitris Zissis, Konstantinos Chatzikokolakis
Publikováno v:
OCEANS 2019 - Marseille.
For applications such as navigation and others, timeliness is a top priority; making the right decision steering a vessel away from danger, is only useful if it is a decision made in due time. Effectiveness of such time critical computing systems is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::399aaf1f5af29320041ddf4a67b60dd1
https://doi.org/10.1109/oceanse.2019.8867269
https://doi.org/10.1109/oceanse.2019.8867269
In this paper, we propose an efficient in-DBMS solution for the problem of sub-trajectory clustering and outlier detection in large moving object datasets. The method relies on a two-phase process: a voting-and-segmentation phase that segments trajec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2659::3448b076ed1567fda342f8e12daf74f3
https://zenodo.org/record/832306
https://zenodo.org/record/832306
Publikováno v:
Proceedings of the 20th International Conference on Extending Database Technology, EDBT 2017
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=r3c4b2081b22::ecfc56eeaa4d37a6a504f260526f3bb5