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Cross domain fusion for spatiotemporal applications: taking interdisciplinary, holistic research to the next level

dc.contributor.authorRenz, Matthias
dc.contributor.authorKröger, Peer
dc.contributor.authorKoschmider, Agnes
dc.contributor.authorLandsiedel, Olaf
dc.contributor.authorTavares de Sousa, Nelson
dc.date.accessioned2023-01-13T13:58:12Z
dc.date.available2023-01-13T13:58:12Z
dc.date.issued2022
dc.description.abstractExploiting the power of collective use of complementing data sources for the discovery of new correlations and findings offers enormous additional value compared to the summed values of isolated analysis of the individual information sources. In this article, we will introduce the concept of “cross domain fusion” (CDF) as a machine learning and pattern mining driven and multi-disciplinary research approach for fusing data and knowledge from a variety of sources enabling the discovery of answers of the question to be examined from a more complete picture. The article will give a basic introduction in this emerging field and will highlight examples of basic CDF tasks in the field of marine science.de
dc.identifier.doi10.1007/s00287-022-01489-6
dc.identifier.pissn1432-122X
dc.identifier.urihttp://dx.doi.org/10.1007/s00287-022-01489-6
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/39989
dc.publisherSpringer
dc.relation.ispartofInformatik Spektrum: Vol. 45, No. 5
dc.relation.ispartofseriesInformatik Spektrum
dc.titleCross domain fusion for spatiotemporal applications: taking interdisciplinary, holistic research to the next levelde
dc.typeText/Journal Article
gi.citation.endPage277
gi.citation.startPage271

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