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AuthorHoppenstedt, Burkharddc.contributor.author
AuthorReichert, Manfreddc.contributor.author
AuthorKammerer, Klausdc.contributor.author
AuthorProbst, Thomasdc.contributor.author
AuthorSchlee, Winfrieddc.contributor.author
AuthorSpiliopoulou, Myradc.contributor.author
AuthorPryss, Rüdigerdc.contributor.author
Date of accession2020-12-03T13:19:29Zdc.date.accessioned
Available in OPARU since2020-12-03T13:19:29Zdc.date.available
Date of first publication2019-09-10dc.date.issued
AbstractVisual analytics are becoming increasingly important in the light of big data and related scenarios. Along this trend, the field of immersive analytics has been variously furthered as it is able to provide sophisticated visual data analytics on one hand, while preserving user-friendliness on the other. Furthermore, recent hardware developments such as smart glasses, as well as achievements in virtual-reality applications, have fanned immersive analytic solutions. Notably, such solutions can be very effective when they are applied to high-dimensional datasets. Taking this advantage into account, the work at hand applies immersive analytics to a high-dimensional production dataset to improve the digital support of daily work tasks. More specifically, a mixed-reality implementation is presented that will support manufacturers as well as data scientists to comprehensively analyze machine data. As a particular goal, the prototype will simplify the analysis of manufacturing data through the usage of dimensionality reduction effects. Therefore, five aspects are mainly reported in this paper. First, it is shown how dimensionality reduction effects can be represented by clusters. Second, it is presented how the resulting information loss of the reduction is addressed. Third, the graphical interface of the developed prototype is illustrated as it provides (1) a correlation coefficient graph, (2) a plot for the information loss, and (3) a 3D particle system. In addition, an implemented voice recognition feature of the prototype is shown, which was considered to be being promising to select or deselect data variables users are interested in when analyzing the data. Fourth, based on a machine learning library, it is shown how the prototype reduces computational resources using smart glasses. The main idea is based on a recommendation approach as well as the use of subspace clustering. Fifth, results from a practical setting are presented, in which the prototype was shown to domain experts. The latter reported that such a tool is actually helpful to analyze machine data daily. Moreover, it was reported that such a system can be used to educate machine operators more properly. As a general outcome of this work, the presented approach may constitute a helpful solution for the industry as well as other domains such as medicine.dc.description.abstract
Languageendc.language.iso
PublisherUniversität Ulmdc.publisher
LicenseCC BY 4.0 Internationaldc.rights
Link to license texthttps://creativecommons.org/licenses/by/4.0/dc.rights.uri
KeywordImmersive analyticsdc.subject
KeywordDimensionality reductiondc.subject
KeywordCovariance graphdc.subject
KeywordSubspace clusteringdc.subject
Dewey Decimal GroupDDC 530 / Physicsdc.subject.ddc
LCSHDimension reduction (Statistics)dc.subject.lcsh
LCSHMixed realitydc.subject.lcsh
TitleDimensionality reduction and subspace clustering in mixed reality for condition monitoring of high-dimensional production datadc.title
Resource typeWissenschaftlicher Artikeldc.type
SWORD Date2020-01-28T13:24:52Zdc.date.updated
VersionpublishedVersiondc.description.version
DOIhttp://dx.doi.org/10.18725/OPARU-33928dc.identifier.doi
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-oparu-33990-9dc.identifier.urn
GNDAnalysedc.subject.gnd
GNDDimension (Physik)dc.subject.gnd
GNDImmersion (Virtuelle Realität)dc.subject.gnd
GNDGraphdc.subject.gnd
GNDCluster-Analysedc.subject.gnd
FacultyFakultät für Ingenieurwissenschaften, Informatik und Psychologieuulm.affiliationGeneral
InstitutionInstitut für Datenbanken und Informationssystemeuulm.affiliationSpecific
Peer reviewjauulm.peerReview
DCMI TypeTextuulm.typeDCMI
CategoryPublikationenuulm.category
DOI of original publication10.3390/s19183903dc.relation1.doi
Source - Title of sourceSensorssource.title
Source - Place of publicationMDPIsource.publisher
Source - Volume19source.volume
Source - Issue18source.issue
Source - Year2019source.year
Source - From page1source.fromPage
Source - To page18source.toPage
Source - Article number3903source.articleNumber
Source - eISSN1424-8220source.identifier.eissn
Open AccessDOAJ Golduulm.OA
WoS000489187800089uulm.identifier.wos
Bibliographyuulmuulm.bibliographie


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