{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T06:48:16Z","timestamp":1743144496721,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030451639"},{"type":"electronic","value":"9783030451646"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-45164-6_9","type":"book-chapter","created":{"date-parts":[[2020,6,23]],"date-time":"2020-06-23T14:03:06Z","timestamp":1592920986000},"page":"255-274","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Event Processing for Maritime Situational Awareness"],"prefix":"10.1007","author":[{"given":"Manolis","family":"Pitsikalis","sequence":"first","affiliation":[]},{"given":"Konstantina","family":"Bereta","sequence":"additional","affiliation":[]},{"given":"Marios","family":"Vodas","sequence":"additional","affiliation":[]},{"given":"Dimitris","family":"Zissis","sequence":"additional","affiliation":[]},{"given":"Alexander","family":"Artikis","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,6,24]]},"reference":[{"issue":"4","key":"9_CR1","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1109\/TKDE.2014.2356476","volume":"27","author":"A Artikis","year":"2015","unstructured":"Artikis, A., Sergot, M.J., Paliouras, G.: An event calculus for event recognition. IEEE Trans. Knowl. Data Eng. 27(4), 895\u2013908 (2015)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"9_CR2","first-page":"1","volume":"2019","author":"K Chatzikokolakis","year":"2019","unstructured":"Chatzikokolakis, K., Zissis, D., Vodas, M., Spiliopoulos, G., Kontopoulos, I.: A distributed lightning fast maritime anomaly detection service. In: OCEANS 2019 - Marseille, June 2019, pp. 1\u20138 (2019)","journal-title":"June"},{"key":"9_CR3","first-page":"293","volume-title":"Logic and Databases","author":"KL Clark","year":"1978","unstructured":"Clark, K.L.: Negation as failure. In: Gallaire, H., Minker, J. (eds.) Logic and Databases, pp. 293\u2013322. Plenum Press, New York (1978)"},{"key":"9_CR4","unstructured":"FAO. VMS for fishery vessels. http:\/\/www.fao.org\/fishery\/topic\/18103\/en. Accessed 15 May 2019"},{"issue":"3","key":"9_CR5","doi-asserted-by":"publisher","first-page":"246","DOI":"10.3390\/rs9030246","volume":"9","author":"H Greidanus","year":"2017","unstructured":"Greidanus, H., Alvarez, M., Santamaria, C., Thoorens, F.-X., Kourti, N., Argentieri, P.: The sumo ship detector algorithm for satellite radar images. Remote Sens. 9(3), 246 (2017)","journal-title":"Remote Sens."},{"key":"9_CR6","unstructured":"IMO. Technical characteristics for an automatic identification system using time division multiple access in the vhf maritime mobile frequency band. Tech. rep., ITU (2017)"},{"key":"9_CR7","unstructured":"IMO. Long-range identification and tracking system. Tech. rep., IMO (2018)"},{"key":"9_CR8","unstructured":"Improving maritime situational awareness through big data analytics, machine learning and artificial intelligence. Anomaly detection white paper, MarineTraffic Research (2019). https:\/\/www.marinetraffic.com\/research\/publication\/anomaly-detection-white-paper\/"},{"key":"9_CR9","unstructured":"Jousselme, A.-L., Ray, C., Camossi, E., Hadzagic, M., Claramunt, C., Bryan, K., Reardon, E., Ilteris, M.: Maritime use case description, h2020 datACRON project deliverable d5.1. http:\/\/datacron-project.eu\/ (2016)"},{"issue":"5\u20136","key":"9_CR10","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1017\/S1471068416000260","volume":"16","author":"N Katzouris","year":"2016","unstructured":"Katzouris, N., Artikis, A., Paliouras, G.: Online learning of event definitions. Theory Pract. Log. Program. 16(5\u20136), 817\u2013833 (2016)","journal-title":"Theory Pract. Log. Program."},{"key":"9_CR11","volume-title":"Big Data: Principles and Best Practices of Scalable Realtime Data Systems","author":"N Marz","year":"2015","unstructured":"Marz, N., Warren, J.: Big Data: Principles and Best Practices of Scalable Realtime Data Systems, 1st edn. Manning Publications Co., Shelter Island (2015)","edition":"1"},{"issue":"7","key":"9_CR12","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.1007\/s10994-019-05794-2","volume":"108","author":"E Michelioudakis","year":"2019","unstructured":"Michelioudakis, E., Artikis, A., Paliouras, G.: Semi-supervised online structure learning for composite event recognition. Mach. Learn. 108(7) , 1085\u20131110 (2019)","journal-title":"Mach. Learn."},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Millefiori, L.M., Zissis, D., Cazzanti, L., Arcieri, G.: A distributed approach to estimating sea port operational regions from lots of AIS data. In: 2016 IEEE International Conference on Big Data, BigData 2016, Washington DC, 5\u20138 December 2016, pp. 1627\u20131632 (2016)","DOI":"10.1109\/BigData.2016.7840774"},{"issue":"2","key":"9_CR14","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1093\/icesjms\/fsl026","volume":"64","author":"CM Mills","year":"2007","unstructured":"Mills, C.M., Townsend, S.E., Jennings, S., Eastwood, P.D., Houghton, C.A.: Estimating high resolution trawl fishing effort from satellite-based vessel monitoring system data. ICES J. Mar. Sci. 64(2), 248\u2013255 (2007)","journal-title":"ICES J. Mar. Sci."},{"issue":"2","key":"9_CR15","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1007\/s10707-016-0266-x","volume":"21","author":"K Patroumpas","year":"2017","unstructured":"Patroumpas, K., Alevizos, E., Artikis, A., Vodas, M., Pelekis, N., Theodoridis, Y.: Online event recognition from moving vessel trajectories. GeoInformatica 21(2), 389\u2013427 (2017)","journal-title":"GeoInformatica"},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"Pitsikalis, M., Artikis, A., Dreo, R., Ray, C., Camossi, E., Jousselme, A.: Composite event recognition for maritime monitoring. In: Proceedings of the 13th ACM International Conference on Distributed and Event-based Systems, DEBS 2019, Darmstadt, 24\u201328 June 2019, pp. 163\u2013174 (2019)","DOI":"10.1145\/3328905.3329762"},{"key":"9_CR17","unstructured":"Santipantakis, G.M., Vlachou, A., Doulkeridis, C., Artikis, A., Kontopoulos, I., Vouros, G.A.: A stream reasoning system for maritime monitoring. In: 25th International Symposium on Temporal Representation and Reasoning, TIME 2018, Warsaw, 15\u201317 October 2018, pp. 20:1\u201320:17 (2018)"},{"issue":"4","key":"9_CR18","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1007\/s10796-015-9560-7","volume":"18","author":"F Terroso-Saenz","year":"2016","unstructured":"Terroso-Saenz, F., Valdes-Vela, M., Skarmeta-Gomez, A.F.: A complex event processing approach to detect abnormal behaviours in the marine environment. Inf. Syst. Front. 18(4), 765\u2013780 (2016)","journal-title":"Inf. Syst. Front."},{"key":"9_CR19","unstructured":"van Laere, J., Nilsson, M.: Evaluation of a workshop to capture knowledge from subject matter experts in maritime surveillance. In: Proceedings of FUSION, pp. 171\u2013178 (2009)"},{"key":"9_CR20","unstructured":"Vouros, G.A., Vlachou, A., Santipantakis, G.M., Doulkeridis, C., Pelekis, N., Georgiou, H.V., Theodoridis, Y., Patroumpas, K., Alevizos, E., Artikis, A., Claramunt, C., Ray, C., Scarlatti, D., Fuchs, G., Andrienko, G.L., Andrienko, N.V., Mock, M., Camossi, E., Jousselme, A., Garcia, J.M.C.: Big data analytics for time critical mobility forecasting: recent progress and research challenges. In: Proceedings of the 21th International Conference on Extending Database Technology, EDBT 2018, Vienna, 26\u201329 March 2018, pp. 612\u2013623 (2018)"},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"Vouros, G.A., Vlachou, A., Santipantakis, G.M., Doulkeridis, C., Pelekis, N., Georgiou, H.V., Theodoridis, Y., Patroumpas, K., Alevizos, E., Artikis, A., Fuchs, G., Mock, M., Andrienko, G.L., Andrienko, N.V., Claramunt, C., Ray, C., Camossi, E., Jousselme, A.: Increasing maritime situation awareness via trajectory detection, enrichment and recognition of events. In: Web and Wireless Geographical Information Systems - Proceedings of 16th International Symposium, W2GIS 2018, A Coru\u00f1a, 21\u201322 May 2018, pp. 130\u2013140 (2018)","DOI":"10.1007\/978-3-319-90053-7_13"}],"container-title":["Big Data Analytics for Time-Critical Mobility Forecasting"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-45164-6_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,3]],"date-time":"2022-09-03T12:20:58Z","timestamp":1662207658000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-45164-6_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030451639","9783030451646"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-45164-6_9","relation":{},"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"24 June 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}