Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.2503
DC FieldValueLanguage
dc.contributor.authorScheidweiler, Tina-
dc.contributor.authorJahn, Carlos-
dc.date.accessioned2019-11-14T15:00:15Z-
dc.date.available2019-11-14T15:00:15Z-
dc.date.issued2019-09-26-
dc.identifier.citationHamburg International Conference of Logistics (HICL): 341-368 (2019)de_DE
dc.identifier.isbn978-3-750249-49-3de_DE
dc.identifier.urihttp://hdl.handle.net/11420/3787-
dc.description.abstractPurpose: As maritime digitalization progresses, great opportunities for maritime transport arise: The introduction of the AIS opened up a number of possibilities and perspectives for increasing efficiency, automation and cost reduction using business analytics and machine learning in the supply chain and maritime sector. Methodology: Various analysis and forecast techniques of machine learning as well as interactive visualizations are presented for the automated analysis of ship movement patterns, risk assessments of encounter situations of two or more ships as well as anomaly detections or performance indicators to quickly extract key figures of certain ships, routes or areas. Findings: In addition to a comprehensive representation of relevant potentials and business analytics areas of AIS data, the feasibility and associated accuracy of the data mining and machine learning methods used are described. In addition, limitations will be shown and perspectives especially on autonomous surface ships will be discussed. Originality: At present, there is no information platform that bundles the areas described in the previous sections in a central source. Previous work has either been limited to the visualization of historical and current ship movements or deals with narrowly limited individual questions of isolated applications.en
dc.language.isoende_DE
dc.publisherepubli GmbHde_DE
dc.relation.ispartofProceedings of the Hamburg International Conference of Logistics (HICL)de_DE
dc.rightsCC-BY-SA 4.0de_DE
dc.rights.urihttps://creativecommons.org/licenses/by-sa/4.0/
dc.subjectBusinessde_DE
dc.subjectAnalyticsde_DE
dc.subjectMaritimede_DE
dc.subjectTrafficde_DE
dc.subject.ddc330: Wirtschaftde_DE
dc.subject.ddc380: Handel, Kommunikation, Verkehrde_DE
dc.titleBusiness analytics on ais data: potentials, limitations and perspectivesde_DE
dc.typeinProceedingsde_DE
dc.identifier.urnurn:nbn:de:gbv:830-882.054922-
dc.identifier.doi10.15480/882.2503-
dc.type.dinicontributionToPeriodical-
dcterms.DCMITypeText-
tuhh.identifier.urnurn:nbn:de:gbv:830-882.054922-
tuhh.oai.showtruede_DE
tuhh.abstract.englishPurpose: As maritime digitalization progresses, great opportunities for maritime transport arise: The introduction of the AIS opened up a number of possibilities and perspectives for increasing efficiency, automation and cost reduction using business analytics and machine learning in the supply chain and maritime sector. Methodology: Various analysis and forecast techniques of machine learning as well as interactive visualizations are presented for the automated analysis of ship movement patterns, risk assessments of encounter situations of two or more ships as well as anomaly detections or performance indicators to quickly extract key figures of certain ships, routes or areas. Findings: In addition to a comprehensive representation of relevant potentials and business analytics areas of AIS data, the feasibility and associated accuracy of the data mining and machine learning methods used are described. In addition, limitations will be shown and perspectives especially on autonomous surface ships will be discussed. Originality: At present, there is no information platform that bundles the areas described in the previous sections in a central source. Previous work has either been limited to the visualization of historical and current ship movements or deals with narrowly limited individual questions of isolated applications.de_DE
tuhh.publisher.urlhttps://www.epubli.de/shop/buch/Digital-Transformation-in-Maritime-and-City-Logistics-Christian-M-Ringle-Wolfgang-Kersten-Carlos-Jahn-9783750249493/92097-
tuhh.publication.instituteMaritime Logistik W-12de_DE
tuhh.identifier.doi10.15480/882.2503-
tuhh.type.opusInProceedings (Aufsatz / Paper einer Konferenz etc.)-
tuhh.gvk.hasppnfalse-
tuhh.hasurnfalse-
dc.type.drivercontributionToPeriodical-
dc.type.casraiConference Paper-
tuhh.container.startpage341de_DE
tuhh.container.endpage368de_DE
dc.relation.conferenceHamburg International Conference of Logistics (HICL) 2019de_DE
dc.rights.nationallicensefalsede_DE
tuhh.relation.ispartofseriesProceedings of the Hamburg International Conference of Logistics (HICL)de_DE
tuhh.relation.ispartofseriesnumber28de_DE
item.cerifentitytypePublications-
item.fulltextWith Fulltext-
item.creatorGNDScheidweiler, Tina-
item.creatorGNDJahn, Carlos-
item.seriesrefProceedings of the Hamburg International Conference of Logistics (HICL);28-
item.openairecristypehttp://purl.org/coar/resource_type/c_5794-
item.openairetypeinProceedings-
item.languageiso639-1en-
item.creatorOrcidScheidweiler, Tina-
item.creatorOrcidJahn, Carlos-
item.tuhhseriesidProceedings of the Hamburg International Conference of Logistics (HICL)-
item.grantfulltextopen-
crisitem.author.deptMaritime Logistik W-12-
crisitem.author.deptMaritime Logistik W-12-
crisitem.author.orcid0000-0002-5409-0748-
crisitem.author.parentorgStudiendekanat Management-Wissenschaften und Technologie-
crisitem.author.parentorgStudiendekanat Management-Wissenschaften und Technologie-
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