EuroVA13

Permanent URI for this collection

https://diglib7.eg.org/handle/10.2312/986

Visual Comparison of Orderings and Rankings

[meta data] [files:
007-011.pdf
]
Behrisch, Michael
; 
Davey, James
; 
Simon, Svenja
; 
Schreck, Tobias
; 
Keim, Daniel
; 
Kohlhammer, Jörn

Visual Grouping - Follow the Leader!

[meta data] [files:
001-005.pdf ,
07_0112-file1.avi ,
07_0112-file2.avi
]
Lammers, Thomas
; 
Vliegen, Roel
; 
Linden, Erik-Jan van der
; 
Wetering, Huub van de

Modeling Incremental Visualizations

[meta data] [files:
013-017.pdf
]
Angelini, Marco
; 
Santucci, Giuseppe

Towards Privacy-Preserving Semantic Mobility Analysis

[meta data] [files:
019-023.pdf
]
Andrienko, N.
; 
Andrienko, G.
; 
Fuchs, G.

Feature-Based Visual Analytics for Studying Simulations of Dynamic Bi-Stable Spatial Systems

[meta data] [files:
025-029.pdf ,
05_0110-file1.mp4
]
Eichner, C.
; 
Bittig, A.
; 
Schumann, H.
; 
Tominski, C.

Mind the Time: Unleashing the Temporal Aspects in Pattern Discovery

[meta data] [files:
031-035.pdf
]
Lammarsch, T.
; 
Aigner, W.
; 
Bertone, A.
; 
Miksch, S.
; 
Rind, A.

Retrospective Analysis of Surveillance Data: A Case Study for Road Tunnels

[meta data] [files:
049-053.pdf ,
09_0114-file1.avi ,
09_0114-file2.mp4
]
Piringer, H.
; 
Buchetics, M.

Visual Analysis of Tracts of Homozygosity in Human Genome

[meta data] [files:
037-041.pdf
]
Reber, Sean
; 
Zhao, Ye
; 
Zhang, Li
; 
Orloff, Mohammed
; 
Eng, Charis

Visual Analysis of Expert Systems for Smart Grid Monitoring

[meta data] [files:
043-047.pdf
]
Steiger, M.
; 
May, T.
; 
Davey, J.
; 
Kohlhammer, J.

Dataflow-based Visual Analysis for Fault Diagnosis and Predictive Maintenance in Manufacturing

[meta data] [files:
055-059.pdf
]
Wörner, M.
; 
Metzger, M.
; 
T.Ertl,

Visual Analytics of Microblog Data for Public Behavior Analysis in Disaster Events

[meta data] [files:
067-071.pdf
]
Chae, Junghoon
; 
Thom, Dennis
; 
Jang, Yun
; 
Kim, Sung Ye
; 
Ertl, Thomas
; 
Ebert, David S.

Interactive Visual Analysis in the Concept Stage of a Hybrid-Vehicle Design

[meta data] [files:
061-065.pdf
]
Matkovic, Kresimir
; 
Duras, Mario
; 
Gracanin, Denis
; 
Splechtna, Rainer
; 
Stehno, Benedikt
; 
Hauser, Helwig

BibTeX (EuroVA13)
@inproceedings{
10.2312:PE.EuroVAST.EuroVA13.007-011,

booktitle = {
EuroVis Workshop on Visual Analytics},

editor = {
M. Pohl and H. Schumann
},
title = {{
Visual Comparison of Orderings and Rankings}},

author = {
Behrisch, Michael
 and 
Davey, James
 and 
Simon, Svenja
 and 
Schreck, Tobias
 and 
Keim, Daniel
 and 
Kohlhammer, Jörn
},
year = {
2013},

publisher = {
The Eurographics Association},


ISBN = {978-3-905674-55-2},

DOI = {
10.2312/PE.EuroVAST.EuroVA13.007-011}

}
@inproceedings{
10.2312:PE.EuroVAST.EuroVA13.001-005,

booktitle = {
EuroVis Workshop on Visual Analytics},

editor = {
M. Pohl and H. Schumann
},
title = {{
Visual Grouping - Follow the Leader!}},

author = {
Lammers, Thomas
 and 
Vliegen, Roel
 and 
Linden, Erik-Jan van der
 and 
Wetering, Huub van de
},
year = {
2013},

publisher = {
The Eurographics Association},


ISBN = {978-3-905674-55-2},

DOI = {
10.2312/PE.EuroVAST.EuroVA13.001-005}

}
@inproceedings{
10.2312:PE.EuroVAST.EuroVA13.013-017,

booktitle = {
EuroVis Workshop on Visual Analytics},

editor = {
M. Pohl and H. Schumann
},
title = {{
Modeling Incremental Visualizations}},

author = {
Angelini, Marco
 and 
Santucci, Giuseppe
},
year = {
2013},

publisher = {
The Eurographics Association},


ISBN = {978-3-905674-55-2},

DOI = {
10.2312/PE.EuroVAST.EuroVA13.013-017}

}
@inproceedings{
10.2312:PE.EuroVAST.EuroVA13.019-023,

booktitle = {
EuroVis Workshop on Visual Analytics},

editor = {
M. Pohl and H. Schumann
},
title = {{
Towards Privacy-Preserving Semantic Mobility Analysis}},

author = {
Andrienko, N.
 and 
Andrienko, G.
 and 
Fuchs, G.
},
year = {
2013},

publisher = {
The Eurographics Association},


ISBN = {978-3-905674-55-2},

DOI = {
10.2312/PE.EuroVAST.EuroVA13.019-023}

}
@inproceedings{
10.2312:PE.EuroVAST.EuroVA13.025-029,

booktitle = {
EuroVis Workshop on Visual Analytics},

editor = {
M. Pohl and H. Schumann
},
title = {{
Feature-Based Visual Analytics for Studying Simulations of Dynamic Bi-Stable Spatial Systems}},

author = {
Eichner, C.
 and 
Bittig, A.
 and 
Schumann, H.
 and 
Tominski, C.
},
year = {
2013},

publisher = {
The Eurographics Association},


ISBN = {978-3-905674-55-2},

DOI = {
10.2312/PE.EuroVAST.EuroVA13.025-029}

}
@inproceedings{
10.2312:PE.EuroVAST.EuroVA13.031-035,

booktitle = {
EuroVis Workshop on Visual Analytics},

editor = {
M. Pohl and H. Schumann
},
title = {{
Mind the Time: Unleashing the Temporal Aspects in Pattern Discovery}},

author = {
Lammarsch, T.
 and 
Aigner, W.
 and 
Bertone, A.
 and 
Miksch, S.
 and 
Rind, A.
},
year = {
2013},

publisher = {
The Eurographics Association},


ISBN = {978-3-905674-55-2},

DOI = {
10.2312/PE.EuroVAST.EuroVA13.031-035}

}
@inproceedings{
10.2312:PE.EuroVAST.EuroVA13.049-053,

booktitle = {
EuroVis Workshop on Visual Analytics},

editor = {
M. Pohl and H. Schumann
},
title = {{
Retrospective Analysis of Surveillance Data: A Case Study for Road Tunnels}},

author = {
Piringer, H.
 and 
Buchetics, M.
},
year = {
2013},

publisher = {
The Eurographics Association},


ISBN = {978-3-905674-55-2},

DOI = {
10.2312/PE.EuroVAST.EuroVA13.049-053}

}
@inproceedings{
10.2312:PE.EuroVAST.EuroVA13.037-041,

booktitle = {
EuroVis Workshop on Visual Analytics},

editor = {
M. Pohl and H. Schumann
},
title = {{
Visual Analysis of Tracts of Homozygosity in Human Genome}},

author = {
Reber, Sean
 and 
Zhao, Ye
 and 
Zhang, Li
 and 
Orloff, Mohammed
 and 
Eng, Charis
},
year = {
2013},

publisher = {
The Eurographics Association},


ISBN = {978-3-905674-55-2},

DOI = {
10.2312/PE.EuroVAST.EuroVA13.037-041}

}
@inproceedings{
10.2312:PE.EuroVAST.EuroVA13.043-047,

booktitle = {
EuroVis Workshop on Visual Analytics},

editor = {
M. Pohl and H. Schumann
},
title = {{
Visual Analysis of Expert Systems for Smart Grid Monitoring}},

author = {
Steiger, M.
 and 
May, T.
 and 
Davey, J.
 and 
Kohlhammer, J.
},
year = {
2013},

publisher = {
The Eurographics Association},


ISBN = {978-3-905674-55-2},

DOI = {
10.2312/PE.EuroVAST.EuroVA13.043-047}

}
@inproceedings{
10.2312:PE.EuroVAST.EuroVA13.055-059,

booktitle = {
EuroVis Workshop on Visual Analytics},

editor = {
M. Pohl and H. Schumann
},
title = {{
Dataflow-based Visual Analysis for Fault Diagnosis and Predictive Maintenance in Manufacturing}},

author = {
Wörner, M.
 and 
Metzger, M.
 and 
T.Ertl,
},
year = {
2013},

publisher = {
The Eurographics Association},


ISBN = {978-3-905674-55-2},

DOI = {
10.2312/PE.EuroVAST.EuroVA13.055-059}

}
@inproceedings{
10.2312:PE.EuroVAST.EuroVA13.067-071,

booktitle = {
EuroVis Workshop on Visual Analytics},

editor = {
M. Pohl and H. Schumann
},
title = {{
Visual Analytics of Microblog Data for Public Behavior Analysis in Disaster Events}},

author = {
Chae, Junghoon
 and 
Thom, Dennis
 and 
Jang, Yun
 and 
Kim, Sung Ye
 and 
Ertl, Thomas
 and 
Ebert, David S.
},
year = {
2013},

publisher = {
The Eurographics Association},


ISBN = {978-3-905674-55-2},

DOI = {
10.2312/PE.EuroVAST.EuroVA13.067-071}

}
@inproceedings{
10.2312:PE.EuroVAST.EuroVA13.061-065,

booktitle = {
EuroVis Workshop on Visual Analytics},

editor = {
M. Pohl and H. Schumann
},
title = {{
Interactive Visual Analysis in the Concept Stage of a Hybrid-Vehicle Design}},

author = {
Matkovic, Kresimir
 and 
Duras, Mario
 and 
Gracanin, Denis
 and 
Splechtna, Rainer
 and 
Stehno, Benedikt
 and 
Hauser, Helwig
},
year = {
2013},

publisher = {
The Eurographics Association},


ISBN = {978-3-905674-55-2},

DOI = {
10.2312/PE.EuroVAST.EuroVA13.061-065}

}

Browse

Recent Submissions

Now showing 1 - 12 of 12
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    Visual Comparison of Orderings and Rankings
    (The Eurographics Association, 2013) Behrisch, Michael; Davey, James; Simon, Svenja; Schreck, Tobias; Keim, Daniel; Kohlhammer, Jörn; M. Pohl and H. Schumann
    In many data analysis problems, sequentially ordered (or ranked) data occurs that needs to be understood and compared. Ranking information is essential in applications such as multimedia search where retrieval rankings need to be inspected; alignments of gene sequences in bio-molecular applications; or for a more abstract example, considering the permutations of rows and columns for purpose of matrix visualization. In each of these examples, often many different orderings of a given data set are possible. E.g., a search engine may produce, based on different user parameterizations, different rankings. A relevant problem then is to understand the commonalities and differences of a potentially large set of rankings. E.g., finding global or partial orderings in which different ranking or sorting algorithms agree can support the certainty in the respective ranking by the user. We consider the problem of comparing sets of rankings with these questions in mind. We present an approach for a visual comparison of sets of rankings that effectively allows to spot commonalities and differences among rankings. The approach relies on a small-multiple view of glyphs each of which visually contrasts a pair of rankings. The glyph in turn is defined on a radial node-link representation which allows effective perception of agreements and differences in pairs of rankings. We apply our approach on different use cases and demonstrate its effectiveness in spotting patterns of similarity and differences in sets of rankings.
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    Visual Grouping - Follow the Leader!
    (The Eurographics Association, 2013) Lammers, Thomas; Vliegen, Roel; Linden, Erik-Jan van der; Wetering, Huub van de; M. Pohl and H. Schumann
    We present an interactive system that provides users with automated techniques for grouping data, while shielding them from the technical aspects of these techniques. In our system users create visual data representations, called views, and choose a dataset for visualization in such views. From the resulting visualization and user actions on this visualization, the system derives the information that is used to automatically steer a grouping engine. Knowledge of data mining is not necessary; parameters and distance functions are automatically derived. In this easy to use system the user can efficiently create any grouping by incrementally manipulating groups with intuitive user actions. These actions allow the user to create, remove, and manipulate groups using a leader and follower metaphor. An implementation of the system has been created in a commercial data visualization tool.
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    Modeling Incremental Visualizations
    (The Eurographics Association, 2013) Angelini, Marco; Santucci, Giuseppe; M. Pohl and H. Schumann
    An increasing number of applications call for the incremental/iterative drawing of a visualization. That is an obvious requirement when dealing with continuously changing data, like the emerging field of data streams or scientific visualizations that have the burden of rendering complex and evolving physical phenomena. This paper postulates that the same need is rising in the field of Visual Analytics and cloud based applications and, in order to provide a support for such processes, it presents a formal model for characterizing the iterative drawing of a visualization, describing the practical issues and outlining the main parameters that can be used to drive and evaluate the whole process. The proposed model is general enough to capture all of the above presented scenarios. Two examples are presented, showing the role that such a model can play in designing iterative visualizations.
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    Towards Privacy-Preserving Semantic Mobility Analysis
    (The Eurographics Association, 2013) Andrienko, N.; Andrienko, G.; Fuchs, G.; M. Pohl and H. Schumann
    By analyzing data reflecting human mobility, one can derive patterns and knowledge that are tightly linked to the underlying geography and therefore cannot be applied to another territory or even compared with patterns obtained for another territory. Another problem of mobility analysis is compromising personal privacy, since person identities can be determined based on the regularly visited geographical locations.We here propose an idea for novel approach based on transformation of the spatial component of movement data from the geographic space to an abstract semantic space, inspired by the concept of cartographic chorems. We demonstrate that many visual analytics procedures developed for geographic movement data can be adapted for privacy-preserving mobility analysis based on semantic spaces.
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    Feature-Based Visual Analytics for Studying Simulations of Dynamic Bi-Stable Spatial Systems
    (The Eurographics Association, 2013) Eichner, C.; Bittig, A.; Schumann, H.; Tominski, C.; M. Pohl and H. Schumann
    Simulations of dynamic bi-stable spatial systems usually generate large and complex data that are hard to evaluate. In this paper, we describe how visual analytics technology can help in analyzing such simulation data. The idea behind our approach is to utilize concepts of feature-based visualization. Consequently, we consider (1) interactive specification of meaningful features, (2) analytic extraction and tracking of features as well as detection of events in the features' evolution, and (3) visual representation of features with their spatial, temporal, and structural aspects. Our solution has been used by simulation experts to analyze spatio-temporal distributions of multiple types of particles in reaction-diffusion simulation data. With the help of the feature-based approach the scientists were able to understand how the spatial separation of proteins develops over time.
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    Mind the Time: Unleashing the Temporal Aspects in Pattern Discovery
    (The Eurographics Association, 2013) Lammarsch, T.; Aigner, W.; Bertone, A.; Miksch, S.; Rind, A.; M. Pohl and H. Schumann
    Temporal Data Mining is a core concept of Knowledge Discovery in Databases handling time-oriented data. Stateof- the-art methods are capable of preserving the temporal order of events as well as the information in between. The temporal nature of the events themselves, however, can likely be misinterpreted by current algorithms. We present a new definition of the temporal aspects of events and extend related work for pattern finding not only by making use of intervals between events but also by utilizing temporal relations like meets, starts, or during. The result is a new algorithm for Temporal Data Mining that preserves and mines additional time-oriented information.
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    Retrospective Analysis of Surveillance Data: A Case Study for Road Tunnels
    (The Eurographics Association, 2013) Piringer, H.; Buchetics, M.; M. Pohl and H. Schumann
    The surveillance of a particular infrastructure is a multi-faceted activity. In addition to tasks which must be performed in real-time, a retrospective analysis of surveillance data is of equal importance for ensuring the quality and plausibility of surveillance activities as well as for drawing conclusions. Based on insights gained from the design of AlVis, a system for the surveillance of road tunnels, the main contribution of this paper is a problem characterization of retrospective analysis tasks in the context of spatio-temporal surveillance data. We also describe concepts for supporting a retrospective analysis in AlVis and we report feedback from a field study.
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    Visual Analysis of Tracts of Homozygosity in Human Genome
    (The Eurographics Association, 2013) Reber, Sean; Zhao, Ye; Zhang, Li; Orloff, Mohammed; Eng, Charis; M. Pohl and H. Schumann
    We propose a new visual analytics system designed for genetic researchers to study genome-wide homozygosity regions. Finding significant tracts of homozygosity (TOH) using single nucleotide polymorphisms (SNPs) from a large-scale genome data set can contribute to the discovery of genetic factors related to human diseases. Our system helps users to visually examine TOH clusters computed from the underlying patient data, lending itself a convenient and powerful tool for knowledge discovery. We illustrate the usability and performance of the system with a clinical data set of human cancers.
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    Visual Analysis of Expert Systems for Smart Grid Monitoring
    (The Eurographics Association, 2013) Steiger, M.; May, T.; Davey, J.; Kohlhammer, J.; M. Pohl and H. Schumann
    This application paper introduces the combination of an expert system with a visualization system, specifically designed for SmartGrid control rooms. It supports operators in the efficient monitoring of electric grids with a focus on distributed, small-scale power generation. A rule-based expert system filters the stream of a large amount of incoming events, searching for potential problems. A multiple, coordinated view environment provides the required situational awareness for the user and presents the analysis results in a details-on-demand manner. Being a critical infrastructure, the electric grid is highly sensitive and any modification must be well justified. Our visualization system therefore also provides insight into the expert system enabling the user to validate and verify the expert system's analysis process. This provides the required decision support to assist the operator in keeping the grid in stable operating condition.
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    Dataflow-based Visual Analysis for Fault Diagnosis and Predictive Maintenance in Manufacturing
    (The Eurographics Association, 2013) Wörner, M.; Metzger, M.; T.Ertl,; M. Pohl and H. Schumann
    Predictive machine maintenance, which monitors the current condition of a machine, can be much more efficient than maintaining it on a strict schedule or only as a reaction to actual breakdowns. Although sophisticated theoretical models exist, these are not always employed in practice, presumably in part due to their abstract nature. Introducing interactive visualization into the analysis process may facilitate the adoption of predictive maintenance. We apply a dataflow-based visual analytics approach to the analysis of diagnostic machine data on a real-world dataset and collect feedback from domain experts.
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    Visual Analytics of Microblog Data for Public Behavior Analysis in Disaster Events
    (The Eurographics Association, 2013) Chae, Junghoon; Thom, Dennis; Jang, Yun; Kim, Sung Ye; Ertl, Thomas; Ebert, David S.; M. Pohl and H. Schumann
    In disaster management, analysis of public behavior plays an important role for evacuation planning. Unfortunately, finding meaningful information for analysis is challenging and collecting relevant data can be very costly. However, the growing dataset of Location-based Social Networks services with its time-stamped, geo-located data offers a new opportunity. Such spatiotemporal data has substantial potential to increase the situational awareness of local events and provide for both planning and investigation. In this paper, we present a visual analytics tool that provides users with interactive social media data analysis and investigation in order to help evacuation planning, analysis, and response. We demonstrate how to improve investigation by analyzing the extracted public behavior responses before and after the evacuation order during the natural disaster event, such as Hurricane Sandy.
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    Interactive Visual Analysis in the Concept Stage of a Hybrid-Vehicle Design
    (The Eurographics Association, 2013) Matkovic, Kresimir; Duras, Mario; Gracanin, Denis; Splechtna, Rainer; Stehno, Benedikt; Hauser, Helwig; M. Pohl and H. Schumann
    The design of modern, hybrid vehicles is an active area of research. As the whole field is new, engineers need intuitive and powerful support tools. In this application paper, we illustrate an application of interactive visual analysis in the concept phase of a hybrid-vehicle design. We exploit coordinated multiple views to explore and analyze a simulation ensemble - a set of simulation runs of the same simulation model. Once we reduce the ensemble to a single run we use a detailed view, including an energy flow graph and a vehicle drive animation. Very positive feedback from domain experts and opportunities for additional improvements encourage further research.