Show simple item record

dc.contributor.authorWartiainen, Pekka
dc.contributor.authorHeimbürger, Anneli
dc.contributor.authorKärkkäinen, Tommi
dc.contributor.editorThalheim, B.
dc.contributor.editorJaakkola, H.
dc.contributor.editorKiyoki, Y.
dc.date.accessioned2016-12-19T10:22:09Z
dc.date.available2016-12-19T10:22:09Z
dc.date.issued2014
dc.identifier.citationWartiainen, P., Heimbürger, A., & Kärkkäinen, T. (2014). Context-sensitive framework for visual analytics in energy production from biomass. In B. Thalheim, H. Jaakkola, & Y. Kiyoki (Eds.), <i>Proceedings of the 24th International Conference on Information Modelling and Knowledge Bases - EJC 2014</i> (pp. 508-515). Kiel University. Kiel Computer Science Series, 4/2014. <a href="https://www.numerik.uni-kiel.de/~discopt/kcss/kcss_2014_04_v1.0_print.pdf" target="_blank">https://www.numerik.uni-kiel.de/~discopt/kcss/kcss_2014_04_v1.0_print.pdf</a>
dc.identifier.otherCONVID_23708909
dc.identifier.otherTUTKAID_62055
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/52438
dc.description.abstractData masses require a lot of data processing. Data mining is the traditional way to convert data into knowledge. In visual analytics, humans are integrated into the process as there is continuous interaction between the analyst and the analysis software. Data mining methods can be utilized also in visual analytics where the priority is given to the visualization of the information and to dimension reduction. However, the provided data is not always enough. There is a large amount of background contextual information, which should be included into the automated process. This paper describes a context-sensitive approach, in which we utilize visual analytics by studying all phases in the process according to our ”sensing, processing and actuation” framework. Experimental studies show that our framework can be very useful in the process of analyzing causes for and relations between variable changes with laboratory-scale power plant data.
dc.format.extent528
dc.language.isoeng
dc.publisherKiel University
dc.relation.ispartofProceedings of the 24th International Conference on Information Modelling and Knowledge Bases - EJC 2014
dc.relation.ispartofseriesKiel Computer Science Series
dc.relation.urihttps://www.numerik.uni-kiel.de/~discopt/kcss/kcss_2014_04_v1.0_print.pdf
dc.subject.othervisual analytics
dc.subject.otherenergy production
dc.titleContext-sensitive framework for visual analytics in energy production from biomass
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201410032926
dc.contributor.laitosTietotekniikan laitosfi
dc.contributor.laitosDepartment of Mathematical Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2014-10-03T03:30:11Z
dc.type.coarconference paper
dc.description.reviewstatuspeerReviewed
dc.format.pagerange508-515
dc.relation.issn2193-6781
dc.type.versionacceptedVersion
dc.rights.copyright© the Authors, the Editors & Kiel University, 2014.
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceEuropean-Japanese conference on information modelling and knowledge bases
dc.subject.ysobiomassa (teollisuus)
jyx.subject.urihttp://www.yso.fi/onto/yso/p6170


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record