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DC Element
Wert
Sprache
dc.contributor.author
Bors, Christian
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dc.contributor.author
Bögl, Markus
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dc.contributor.author
Bernard, Jürgen
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dc.contributor.author
Gschwandtner, Theresia
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dc.contributor.author
Miksch, Silvia
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dc.date.accessioned
2022-10-20T11:48:11Z
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dc.date.available
2022-10-20T11:48:11Z
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dc.date.issued
2018
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dc.identifier.citation
<div class="csl-bib-body">
<div class="csl-entry">Bors, C., Bögl, M., Bernard, J., Gschwandtner, T., & Miksch, S. (2018). <i>Quantifying Uncertainty in Time Series Data Processing</i>. VisInPractice Mini-Symposium on Visualizing Uncertainty, Berlin, Germany. http://hdl.handle.net/20.500.12708/86740</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/86740
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dc.description.abstract
Uncertainty visualization has become an integral part of many data analysis applications, aiding practitioners in making informed decisions, particularly when uncertain aspects are involved.
However, the assessment and quantification of uncertainty introduced by data processing methods is still neglected in application scenarios.
Using human motion and activity recognition as an example, different machine learning and data mining routines can be applied for data processing and analysis, which change the value domain of the underlying multivariate time series.
We identify value uncertainty as the information of changes done to the value domain, e.g., by cleansing/wrangling data to make them suited for further analysis.
For domain experts to appropriately account for uncertainties in their decision making process, they need to be quantified and externalized in the visualizations.
To accomplish this, we implemented a quantification of value uncertainties from
commonly used data processing routines for time series data (e.g., smoothing operations).
We also provide different aggregation methods of value uncertainties over consecutive routines in data processing pipelines by employing various uncertainty quantification techniques (e.g., statistical, bayesian, probabilistic).
This allows developers of data processing pipelines as well as users of the resulting visualization to consider the results with appropriate knowledge of value uncertainty that influenced the analysis outcome.
In our visual interactive environment different processing and segmentation routine results are juxtaposed, the most appropriate motion and activity recognition pipeline can be selected by the domain expert under the consideration of (a) segmentation accuracy, (b) value uncertainties introduced into the data, and (c) overall uncertainty of the result.
en
dc.description.sponsorship
Fonds zur Förderung der wissenschaftlichen Forschung (FWF)
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dc.title
Quantifying Uncertainty in Time Series Data Processing
en
dc.type
Präsentation
de
dc.type
Presentation
en
dc.relation.grantno
2850-N31
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dc.type.category
Conference Presentation
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tuw.peerreviewed
false
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tuw.publication.invited
invited
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tuw.project.title
Visual Segmentation and Labeling of Multivariate Time Series
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tuw.publication.orgunit
E193-07 - Forschungsbereich Visual Analytics
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tuw.event.name
VisInPractice Mini-Symposium on Visualizing Uncertainty
en
tuw.event.startdate
22-10-2018
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tuw.event.enddate
22-10-2018
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tuw.event.online
On Site
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tuw.event.type
Event for scientific audience
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tuw.event.place
Berlin
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tuw.event.country
DE
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tuw.event.presenter
Bors, Christian
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wb.sciencebranch
Informatik
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wb.sciencebranch.oefos
1020
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wb.facultyfocus
Visual Computing and Human-Centered Technology (VC + HCT)
de
wb.facultyfocus
Visual Computing and Human-Centered Technology (VC + HCT)
en
wb.facultyfocus.faculty
E180
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item.grantfulltext
none
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item.fulltext
no Fulltext
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item.openairetype
conference paper not in proceedings
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item.openairecristype
http://purl.org/coar/resource_type/c_18cp
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item.cerifentitytype
Publications
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crisitem.project.funder
FWF Fonds zur Förderung der wissenschaftlichen Forschung (FWF)
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crisitem.project.grantno
2850-N31
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crisitem.author.dept
E193-07 - Forschungsbereich Visual Analytics
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crisitem.author.dept
E193-07 - Forschungsbereich Visual Analytics
-
crisitem.author.dept
E193-07 - Forschungsbereich Visual Analytics
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crisitem.author.dept
E193-07 - Forschungsbereich Visual Analytics
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crisitem.author.orcid
0000-0001-8119-7025
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crisitem.author.orcid
0000-0002-8337-4774
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crisitem.author.orcid
0000-0003-4427-5703
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crisitem.author.parentorg
E193 - Institut für Visual Computing and Human-Centered Technology
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crisitem.author.parentorg
E193 - Institut für Visual Computing and Human-Centered Technology
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crisitem.author.parentorg
E193 - Institut für Visual Computing and Human-Centered Technology
-
crisitem.author.parentorg
E193 - Institut für Visual Computing and Human-Centered Technology
