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Τίτλος: Mashup Tools for Big Data Analysis in Maritime Surveillance
Συγγραφείς: Melillos, George 
Themistocleous, Kyriacos 
Danezis, Chris 
Michaelides, Silas 
Hadjimitsis, Diofantos G. 
Jacobsen, Sven 
Tings, Björn 
Major Field of Science: Engineering and Technology
Field Category: Civil Engineering
Λέξεις-κλειδιά: Maritime Surveillance;Big Data;Mashup Tools;Python;Web Scraping;AIS
Ημερομηνία Έκδοσης: 20-Σεπ-2020
Πηγή: Proceedings Volume 11542, Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies IV
Volume: 11542
Issue: 1154208
Project: ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment 
Περιοδικό: Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies IV 
Conference: SPIE Security + Defence 
Περίληψη: The growth of big data and its popularity in maritime surveillance has increased at an exponential rate. The amount of maritime information being collected every minute around the world exceeds the capacity of traditional databases. The development of real-time, Geospatial Web Applications e.g., MarineTraffic and VesselFinder AIS vessel tracking web sites, provide us with huge sets of structured and unstructured data that are too complex for traditional data-processing software. The aim of this paper is to exploit the benefits of query and mashup amounts of maritime data using mashup tools as a result to create a single, unique visualization. The results show that using mashup techniques in maritime surveillance could be used to monitor, compare, combine, manipulate and analyse Big Maritime data. Therefore, research on Maritime Data offers a huge potential and an opportunity to benefit from the advantages.
URI: https://hdl.handle.net/20.500.14279/21976
DOI: 10.1117/12.2573749
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Type: Conference Papers
Affiliation: Cyprus University of Technology 
ERATOSTHENES Centre of Excellence 
DLR - German Aerospace Center 
Publication Type: Peer Reviewed
Εμφανίζεται στις συλλογές:EXCELSIOR H2020 Teaming Project Publications

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