{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T19:04:16Z","timestamp":1767035056720,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,9,4]],"date-time":"2019-09-04T00:00:00Z","timestamp":1567555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010665","name":"H2020 Marie Sk\u0142odowska-Curie Actions","doi-asserted-by":"publisher","award":["777695"],"award-info":[{"award-number":["777695"]}],"id":[{"id":"10.13039\/100010665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>The problem of unmanned supervision of maritime areas has attracted the interest of researchers for the last few years, mainly thanks to the advances in vessel monitoring that the Automatic Identification System (AIS) has brought. Several frameworks and algorithms have been proposed for the management of vessel trajectory data, which focus on data compression, data clustering, classification and visualization, offering a wide variety of solutions from vessel monitoring to automatic detection of complex events. This work builds on our previous work in the topic of automatic detection of Search and Rescue (SAR) missions, by developing and evaluating a methodology for classifying the trajectories of vessels that possibly participate in such missions. The proposed solution takes advantage of a synthetic trajectory generator and a classifier that combines a genetic algorithm (GENDIS) for the extraction of informative shapelets from training data and a transformation to the shapelets\u2019 feature space. Using the generator and several SAR patterns that are formally described in naval operations bibliography, it generates a synthetic dataset that is used to train the classifier. Evaluation on both synthetic and real data has very promising results and helped us to identify vessel SAR maneuvers without putting any effort into manual annotation.<\/jats:p>","DOI":"10.3390\/fi11090192","type":"journal-article","created":{"date-parts":[[2019,9,5]],"date-time":"2019-09-05T03:22:36Z","timestamp":1567653756000},"page":"192","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Framework for the Detection of Search and Rescue Patterns Using Shapelet Classification"],"prefix":"10.3390","volume":"11","author":[{"given":"Konstantinos","family":"Kapadais","sequence":"first","affiliation":[{"name":"Department of Informatics &amp; Telematics, Harokopio University of Athens, 17778 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0876-8167","authenticated-orcid":false,"given":"Iraklis","family":"Varlamis","sequence":"additional","affiliation":[{"name":"Department of Informatics &amp; Telematics, Harokopio University of Athens, 17778 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7262-7310","authenticated-orcid":false,"given":"Christos","family":"Sardianos","sequence":"additional","affiliation":[{"name":"Department of Informatics &amp; Telematics, Harokopio University of Athens, 17778 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5183-1443","authenticated-orcid":false,"given":"Konstantinos","family":"Tserpes","sequence":"additional","affiliation":[{"name":"Department of Informatics &amp; Telematics, Harokopio University of Athens, 17778 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kontopoulos, I., Spiliopoulos, G., Zissis, D., Chatzikokolakis, K., and Artikis, A. (2018, January 12\u201315). Countering Real-Time Stream Poisoning: An architecture for detecting vessel spoofing in streams of AIS data. Proceedings of the 2018 IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, 16th International Conference on Pervasive Intelligence and Computing, 4th International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC\/PiCom\/DataCom\/CyberSciTech), Athens, Greece.","DOI":"10.1109\/DASC\/PiCom\/DataCom\/CyberSciTec.2018.00139"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Pitsikalis, M., Artikis, A., Dreo, R., Ray, C., Camossi, E., and Jousselme, A.L. (2019, January 24\u201328). Composite Event Recognition for Maritime Monitoring. 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