EuroVA16

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/15279
Groningen, the Netherlands, 6-10 June 2016
Table of Contents
Best Paper
Lessons on Combining Topology and Geography - Visual Analytics for Electrical Outage Management
[full paper Image] [meta data Image]
Alexander Jäger, Sebastian Mittelstädt, Daniela Oelke, Sonja Sander, Axel Platz, Gies Bouwman, and Daniel A. Keim
Multivariate Data Analysis
A Visual Analytics System for Mobile Telecommunication Marketing Analysis
[full paper Image] [meta data Image]
Marco Angelini, Rocco Corriero, Fabrizio Franceschi, Marina Geymonat, Mario Mirabelli, Chiara Remondino, Giuseppe Santucci, and Barbara Stabellini
Patent Retrieval: A Multi-Modal Visual Analytics Approach
[full paper Image] [meta data Image]
Daniel Seebacher, Manuel Stein, Halldór Janetzko, and Daniel A. Keim
Visualization of Latin Textual Variants using a Pixel-Based Text Analysis Tool
[full paper Image] [meta data Image]
Bharathi Asokarajan, Ronak Etemadpour, June Abbas, Sam Huskey, and Chris Weaver
An Art-based Approach to Visual Analytics
[full paper Image] [meta data Image]
Gunjan Sehgal and Geetika Sharma
Temporal Data Analysis
Visual-Interactive Segmentation of Multivariate Time Series
[full paper Image] [meta data Image]
Jürgen Bernard, Eduard Dobermann, Markus Bögl, Martin Röhlig, Anna Vögele, and Jörn Kohlhammer
Visual Analytics for Persistent Scatterer Interferometry: First Steps and Future Challenges
[full paper Image] [meta data Image]
Patrick Köthur, Daniel Eggert, Andreas Schenk, and Mike Sips
Visual Analytics in Process Mining: Classification of Process Mining Techniques
[full paper Image] [meta data Image]
Simone Kriglstein, Margit Pohl, Stefanie Rinderle-Ma, and Magdalena Stallinger
Visual-Interactive Exploration of Relations Between Time-Oriented Data and Multivariate Data
[full paper Image] [meta data Image]
Jürgen Bernard, David Sessler, Martin Steiger, Martin Spott, and Jörn Kohlhammer
Networks
MultiLayerMatrix: Visualizing Large Taxonomic Datasets
[full paper Image] [meta data Image]
Tuan Nhon Dang, Hong Cui, and Angus G. Forbes
Multigraph Visualization for Feature Classification of Brain Network Data
[full paper Image] [meta data Image]
Jiachen Wang, Shiaofen Fang, Huang Li, Joaquín Goñi, Andrew J. Saykin, and Li Shen

BibTeX (EuroVA16)
@inproceedings{
10.2312:eurova.20161117,

booktitle = {
EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {
Natalia Andrienko and Michael Sedlmair
},
title = {{
A Visual Analytics System for Mobile Telecommunication Marketing Analysis}},

author = {
Angelini, Marco
 and 
Corriero, Rocco
 and 
Franceschi, Fabrizio
 and 
Geymonat, Marina
 and 
Mirabelli, Mario
 and 
Remondino, Chiara
 and 
Santucci, Giuseppe
 and 
Stabellini, Barbara
},
year = {
2016},

publisher = {
The Eurographics Association},


ISSN = {-},
ISBN = {978-3-03868-016-1},

DOI = {
10.2312/eurova.20161117}

}
@inproceedings{
10.2312:eurova.20161116,

booktitle = {
EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {
Natalia Andrienko and Michael Sedlmair
},
title = {{
Lessons on Combining Topology and Geography - Visual Analytics for Electrical Outage Management}},

author = {
Jäger, Alexander
 and 
Mittelstädt, Sebastian
 and 
Oelke, Daniela
 and 
Sander, Sonja
 and 
Platz, Axel
 and 
Bouwman, Gies
 and 
Keim, Daniel A.
},
year = {
2016},

publisher = {
The Eurographics Association},


ISSN = {-},
ISBN = {978-3-03868-016-1},

DOI = {
10.2312/eurova.20161116}

}
@inproceedings{
10.2312:eurova.20161120,

booktitle = {
EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {
Natalia Andrienko and Michael Sedlmair
},
title = {{
An Art-based Approach to Visual Analytics}},

author = {
Sehgal, Gunjan
 and 
Sharma, Geetika
},
year = {
2016},

publisher = {
The Eurographics Association},


ISSN = {-},
ISBN = {978-3-03868-016-1},

DOI = {
10.2312/eurova.20161120}

}
@inproceedings{
10.2312:eurova.20161119,

booktitle = {
EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {
Natalia Andrienko and Michael Sedlmair
},
title = {{
Visualization of Latin Textual Variants using a Pixel-Based Text Analysis Tool}},

author = {
Asokarajan, Bharathi
 and 
Etemadpour, Ronak
 and 
Abbas, June
 and 
Huskey, Sam
 and 
Weaver, Chris
},
year = {
2016},

publisher = {
The Eurographics Association},


ISSN = {-},
ISBN = {978-3-03868-016-1},

DOI = {
10.2312/eurova.20161119}

}
@inproceedings{
10.2312:eurova.20161118,

booktitle = {
EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {
Natalia Andrienko and Michael Sedlmair
},
title = {{
Patent Retrieval: A Multi-Modal Visual Analytics Approach}},

author = {
Seebacher, Daniel
 and 
Stein, Manuel
 and 
Janetzko, Halldór
 and 
Keim, Daniel A.
},
year = {
2016},

publisher = {
The Eurographics Association},


ISSN = {-},
ISBN = {978-3-03868-016-1},

DOI = {
10.2312/eurova.20161118}

}
@inproceedings{
10.2312:eurova.20161122,

booktitle = {
EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {
Natalia Andrienko and Michael Sedlmair
},
title = {{
Visual Analytics for Persistent Scatterer Interferometry: First Steps and Future Challenges}},

author = {
Köthur, Patrick
 and 
Eggert, Daniel
 and 
Schenk, Andreas
 and 
Sips, Mike
},
year = {
2016},

publisher = {
The Eurographics Association},


ISSN = {-},
ISBN = {978-3-03868-016-1},

DOI = {
10.2312/eurova.20161122}

}
@inproceedings{
10.2312:eurova.20161121,

booktitle = {
EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {
Natalia Andrienko and Michael Sedlmair
},
title = {{
Visual-Interactive Segmentation of Multivariate Time Series}},

author = {
Bernard, Jürgen
 and 
Dobermann, Eduard
 and 
Bögl, Markus
 and 
Röhlig, Martin
 and 
Vögele, Anna
 and 
Kohlhammer, Jörn
},
year = {
2016},

publisher = {
The Eurographics Association},


ISSN = {-},
ISBN = {978-3-03868-016-1},

DOI = {
10.2312/eurova.20161121}

}
@inproceedings{
10.2312:eurova.20161123,

booktitle = {
EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {
Natalia Andrienko and Michael Sedlmair
},
title = {{
Visual Analytics in Process Mining: Classification of Process Mining Techniques}},

author = {
Kriglstein, Simone
 and 
Pohl, Margit
 and 
Rinderle-Ma, Stefanie
 and 
Stallinger, Magdalena
},
year = {
2016},

publisher = {
The Eurographics Association},


ISSN = {-},
ISBN = {978-3-03868-016-1},

DOI = {
10.2312/eurova.20161123}

}
@inproceedings{
10.2312:eurova.20161124,

booktitle = {
EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {
Natalia Andrienko and Michael Sedlmair
},
title = {{
Visual-Interactive Exploration of Relations Between Time-Oriented Data and Multivariate Data}},

author = {
Bernard, Jürgen
 and 
Sessler, David
 and 
Steiger, Martin
 and 
Spott, Martin
 and 
Kohlhammer, Jörn
},
year = {
2016},

publisher = {
The Eurographics Association},


ISSN = {-},
ISBN = {978-3-03868-016-1},

DOI = {
10.2312/eurova.20161124}

}
@inproceedings{
10.2312:eurova.20161126,

booktitle = {
EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {
Natalia Andrienko and Michael Sedlmair
},
title = {{
Multigraph Visualization for Feature Classification of Brain Network Data}},

author = {
Wang, Jiachen
 and 
Fang, Shiaofen
 and 
Li, Huang
 and 
Goñi, Joaquín
 and 
Saykin, Andrew J.
 and 
Shen, Li
},
year = {
2016},

publisher = {
The Eurographics Association},


ISSN = {-},
ISBN = {978-3-03868-016-1},

DOI = {
10.2312/eurova.20161126}

}
@inproceedings{
10.2312:eurova.20161125,

booktitle = {
EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {
Natalia Andrienko and Michael Sedlmair
},
title = {{
MultiLayerMatrix: Visualizing Large Taxonomic Datasets}},

author = {
Dang, Tuan Nhon
 and 
Cui, Hong
 and 
Forbes, Angus G.
},
year = {
2016},

publisher = {
The Eurographics Association},


ISSN = {-},
ISBN = {978-3-03868-016-1},

DOI = {
10.2312/eurova.20161125}

}

Browse

Recent Submissions

Now showing 1 - 12 of 12
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    EuroVA 2016: Frontmatter
    (Eurographics Association, 2016) Natalia Andrienko; Michael Sedlmair;
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    A Visual Analytics System for Mobile Telecommunication Marketing Analysis
    (The Eurographics Association, 2016) Angelini, Marco; Corriero, Rocco; Franceschi, Fabrizio; Geymonat, Marina; Mirabelli, Mario; Remondino, Chiara; Santucci, Giuseppe; Stabellini, Barbara; Natalia Andrienko and Michael Sedlmair
    The Mobile Operators European market is likely one of the most competitive arenas, and even big telecommunication companies are continuously tuning their marketing strategies to contrast the aggressive campaigns of old and new competitors. This paper presents a visual analytics solution developed for supporting one of the decision making processes of TIM (Telecom Italia Group), the biggest Italian provider of telecommunications services with over 30M active mobile subscribers (at September 2014). The proposed system uses information coming from both public open data and internal TIM data, mapping them on the Italian hierarchy of 20 regions and 110 provinces. The system has been designed together with TIM analysts and allows for selecting an optimal set of provinces on which to focus marketing campaigns, according to the campaign goals, the available market, the local economy, the actual traffic, and the actual TIM penetration.
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    Lessons on Combining Topology and Geography - Visual Analytics for Electrical Outage Management
    (The Eurographics Association, 2016) Jäger, Alexander; Mittelstädt, Sebastian; Oelke, Daniela; Sander, Sonja; Platz, Axel; Bouwman, Gies; Keim, Daniel A.; Natalia Andrienko and Michael Sedlmair
    Outage management in electrical networks is a complex task for operators and requires comprehensive overviews of the topology. At the same time valuable information for detecting the root cause may have geographical context such as digging activities or falling trees. Consequently, vendors of state-of-the-art SCADA systems started to integrate this valuable information source as well. However, in todays systems both views are separated, requiring operators to mentally connect the geographical and topological information. The wish of operators is to provide a comprehensive combination of both spaces in a single view. However, how to project geographical elements into the topology to support the workflow of real operators is yet unclear. In this paper, we present a design study for an interactive visualization system that provides a comprehensive overview for power grid operators. It provides full coverage of both spaces in order to measure how real operators make use of the geographical information. It bypasses the projection problem by interactive brushing-and-linking to support associative analysis. We extracted the mental-model of domain experts in real use cases and found a general bias source in sequential analysis of two spaces. We contribute our problem and task abstraction, lessons learned, and implications for future research.
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    An Art-based Approach to Visual Analytics
    (The Eurographics Association, 2016) Sehgal, Gunjan; Sharma, Geetika; Natalia Andrienko and Michael Sedlmair
    In this paper, we propose an art-based approach to visual analytics.We argue that while artistic data visualizations have mainly been designed to communicate the artist's message, certain artistic styles can be very effective in exploratory data analysis as well and data visualizations can benefit from more than just the aesthetics inspired by art. We use the ancient Warli style of tribal paintings, found in western India to demonstrate the use of artistic styles for visual analytics over open data provided by the Indian government.
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    Visualization of Latin Textual Variants using a Pixel-Based Text Analysis Tool
    (The Eurographics Association, 2016) Asokarajan, Bharathi; Etemadpour, Ronak; Abbas, June; Huskey, Sam; Weaver, Chris; Natalia Andrienko and Michael Sedlmair
    One of the most important activities of Latin scholars is to analyze fragmentary copies of a Classical text and assemble an annotated reconstruction as a conjecture about its original form. We have developed a pixel-based visual text analysis tool to help Latin scholars visualize the evolution of historic copies and analyze the details of alterations and errors introduced in transcription. Coordination of pixel-based visualizations with focus+context navigation across multiple views allows compact representation of text variation across scales of text structure. This approach helps scholars validate the accuracy of variations and assess subtle differences across fragmentary copies as well as past reconstructions. In this paper, we describe the central design features of the tool that help scholars analyze the density and distribution of variants by interacting with text at the granularities of words, lines, and pages simultaneously. We present the results of a user study on our initial multiple view focus+context design and discuss how the results motivate a more visually integrated focus+context design using tiered views.
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    Patent Retrieval: A Multi-Modal Visual Analytics Approach
    (The Eurographics Association, 2016) Seebacher, Daniel; Stein, Manuel; Janetzko, Halldór; Keim, Daniel A.; Natalia Andrienko and Michael Sedlmair
    Claiming intellectual property for an invention by patents is a common way to protect ideas and technological advancements. However, patents allow only the protection of new ideas. Assessing the novelty of filed patent applications is a very time-consuming, yet crucial manual task. Current patent retrieval systems do not make use of all available data and do not explain the similarity between patents. We support patent officials by an enhanced Visual Analytics multi-modal patent retrieval system. Including various similarity measurements and incorporating user feedback, we are able to achieve significantly better query results than state-of-the-art methods.
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    Visual Analytics for Persistent Scatterer Interferometry: First Steps and Future Challenges
    (The Eurographics Association, 2016) Köthur, Patrick; Eggert, Daniel; Schenk, Andreas; Sips, Mike; Natalia Andrienko and Michael Sedlmair
    In this paper, we introduce persistent scatterer interferometry (PSI) as a new and promising application domain for Visual Analytics (VA). PSI studies changes of the Earth's topography by analyzing large time-varying point clouds that easily comprise hundreds of millions of data points. We briefly outline the PSI analysis workflow and present a VA approach to the first step in this workflow based on a flexible and interactive filtering mechanism. We further describe challenges for VA in PSI analysis. We want to engage the VA community in a discussion about potential VA solutions because we expect these solutions to not only advance PSI analysis but also provide valuable insights and contributions for the VA community regarding exploration and analysis of spatiotemporal data.
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    Visual-Interactive Segmentation of Multivariate Time Series
    (The Eurographics Association, 2016) Bernard, Jürgen; Dobermann, Eduard; Bögl, Markus; Röhlig, Martin; Vögele, Anna; Kohlhammer, Jörn; Natalia Andrienko and Michael Sedlmair
    Choosing appropriate time series segmentation algorithms and relevant parameter values is a challenging problem. In order to choose meaningful candidates it is important that different segmentation results are comparable. We propose a Visual Analytics (VA) approach to address these challenges in the scope of human motion capture data, a special type of multivariate time series data. In our prototype, users can interactively select from a rich set of segmentation algorithm candidates. In an overview visualization, the results of these segmentations can be compared and adjusted with regard to visualizations of raw data. A similarity-preserving colormap further facilitates visual comparison and labeling of segments. We present our prototype and demonstrate how it can ease the choice of winning candidates from a set of results for the segmentation of human motion capture data.
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    Visual Analytics in Process Mining: Classification of Process Mining Techniques
    (The Eurographics Association, 2016) Kriglstein, Simone; Pohl, Margit; Rinderle-Ma, Stefanie; Stallinger, Magdalena; Natalia Andrienko and Michael Sedlmair
    The increasing interest from industry and academia has driven the development of process mining techniques over the last years. Since the process mining entails a strong explorative perspective, the combination of process mining and visual analytics methods is a fruitful multidisciplinary solution to enable the exploration and the understanding of large amounts of event log data. In this paper, we propose a first approach how process mining techniques can be categorized with respect to visual analytics aspects. Since ProM is a widely used open-source framework which includes most of the existing process mining techniques as plug-ins, we concentrate on the plugins of ProM as use case to show the applicability of our approach.
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    Visual-Interactive Exploration of Relations Between Time-Oriented Data and Multivariate Data
    (The Eurographics Association, 2016) Bernard, Jürgen; Sessler, David; Steiger, Martin; Spott, Martin; Kohlhammer, Jörn; Natalia Andrienko and Michael Sedlmair
    The analysis of large, multivariate data sets is challenging, especially when some of these data objects are timeoriented. Exploring relationships between multivariate and temporal information, e.g., to identify patterns that support decision making is an important industrial analysis task. The target group of this design study are data analysts aiming at detecting fault patterns in a telecommunications network in order to spend maintenance budget more effectively. We present a visual analytics tool that provides overviews of multivariate data sets and associated time series. Users can select data subsets of interest in both attribute data and clustered time series data. Linked views consequently support the identification of relations between the two spaces. To ensure usefulness, the tool was designed in an iterative way, based on a careful characterization of the data, users, and tasks. A usage scenario demonstrates the applicability of the approach.
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    Multigraph Visualization for Feature Classification of Brain Network Data
    (The Eurographics Association, 2016) Wang, Jiachen; Fang, Shiaofen; Li, Huang; Goñi, Joaquín; Saykin, Andrew J.; Shen, Li; Natalia Andrienko and Michael Sedlmair
    A Multigraph is a set of graphs with a common set of nodes but different sets of edges. Multigraph visualization has not received much attention so far. In this paper, we introduce a multigraph application in brain network data analysis that has a strong need for multigraph visualization. In this application, multigraph is used to represent brain connectome networks of multiple human subjects. A volumetric data set is constructed from the matrix representation of the multigraph. A volume visualization tool is then developed to assist the user to interactively and iteratively detect network features that may contribute to certain neurological conditions. We apply this technique to a brain connectome dataset for feature detection in the classification of Alzheimer's Disease (AD) patients. Preliminary results show significant improvements when interactively selected features are used.
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    MultiLayerMatrix: Visualizing Large Taxonomic Datasets
    (The Eurographics Association, 2016) Dang, Tuan Nhon; Cui, Hong; Forbes, Angus G.; Natalia Andrienko and Michael Sedlmair
    Adjacency matrices can be a useful way to visualize dense networks. However, they do not scale well as the network size increases due to limited screen space, especially when the number of rows and columns exceeds the pixel height and width of the screen. We introduce a new scalable technique, MultiLayerMatrix, to visualize very large matrices by breaking them into multiple layers. In our technique, the top layer shows the relationships between different groups of clustered data while each sub-layer shows the relationships between nodes in each group as needed. This process can be applied iteratively to create multiple sub-layers for very large datasets. We illustrate the usefulness of MultiLayerMatrix by applying it to a network representing similarity measures between 2,048 characters in the Asteraceae taxonomy, a rich dataset that describes characteristics of species of flowering plants.We also discuss the scalability of our technique by investigating its effectiveness on a large synthetic dataset with 20,000 columns by 20,000 rows.