EuroVA14

Permanent URI for this collection

https://diglib7.eg.org/handle/10.2312/7746
Swansea, UK

BibTeX (EuroVA14)
@inproceedings{
10.2312:eurova.20141137,

booktitle = {
EuroVis Workshop on Visual Analytics},

editor = {
M. Pohl and J. Roberts
},
title = {{
Visual Analytics for Risk-based Decision Making, Long-Term Planning, and Assessment Process}},

author = {
Oliveros, Silvia
 and 
Yang, Yang
 and 
Jang, Yun
 and 
Maule, Ben
 and 
Ebert, David
},
year = {
2014},

publisher = {
The Eurographics Association},


ISBN = {978-3-905674-68-2},

DOI = {
10.2312/eurova.20141137}

}
@inproceedings{
10.2312:eurova.20141138,

booktitle = {
EuroVis Workshop on Visual Analytics},

editor = {
M. Pohl and J. Roberts
},
title = {{
From Ill-defined Problems to Informed Decisions}},

author = {
Roberts, Jonathan
 and 
Keim, Daniel
 and 
Hanratty, Timothy
 and 
Rowlingson, Robert
 and 
Walker, Rick
 and 
Hall, Mark
 and 
Jackobson, Zack
 and 
Lavigne, Valerie
 and 
Rooney, Chris
 and 
Varga, Margaret
},
year = {
2014},

publisher = {
The Eurographics Association},


ISBN = {978-3-905674-68-2},

DOI = {
10.2312/eurova.20141138}

}
@inproceedings{
10.2312:eurova.20141140,

booktitle = {
EuroVis Workshop on Visual Analytics},

editor = {
M. Pohl and J. Roberts
},
title = {{
Guided Sketching for Visual Search and Exploration in Large Scatter Plot Spaces}},

author = {
Shao, Lin
 and 
Behrisch, Michael
 and 
Schreck, Tobias
 and 
Landesberger, Tatiana von
 and 
Scherer, Maximilian
 and 
Bremm, Sebastian
 and 
Keim, Daniel
},
year = {
2014},

publisher = {
The Eurographics Association},


ISBN = {978-3-905674-68-2},

DOI = {
10.2312/eurova.20141140}

}
@inproceedings{
10.2312:eurova.20141139,

booktitle = {
EuroVis Workshop on Visual Analytics},

editor = {
M. Pohl and J. Roberts
},
title = {{
Ribbons: Enabling the Effective Use of HPC Utilization Data for System Support Staff}},

author = {
Sisneros, Robert
 and 
Fullop, Joshi
 and 
Semeraro, B. David
 and 
Bauer, Greg
},
year = {
2014},

publisher = {
The Eurographics Association},


ISBN = {978-3-905674-68-2},

DOI = {
10.2312/eurova.20141139}

}
@inproceedings{
10.2312:eurova.20141142,

booktitle = {
EuroVis Workshop on Visual Analytics},

editor = {
M. Pohl and J. Roberts
},
title = {{
A Visual Analytics Approach to Segmenting and Labeling Multivariate Time Series Data}},

author = {
Alsallakh, Bilal
 and 
Bögl, Markus
 and 
Gschwandtner, Theresia
 and 
Miksch, Silvia
 and 
Esmael, Bilal
 and 
Arnaout, Arghad
 and 
Thonhauser, Gerhard
 and 
Zöllner, Philipp
},
year = {
2014},

publisher = {
The Eurographics Association},


ISBN = {978-3-905674-68-2},

DOI = {
10.2312/eurova.20141142}

}
@inproceedings{
10.2312:eurova.20141143,

booktitle = {
EuroVis Workshop on Visual Analytics},

editor = {
M. Pohl and J. Roberts
},
title = {{
What's In a Name? Data Linkage, Demography and Visual Analytics}},

author = {
Wang, Feng
 and 
Ibarra, Jose
 and 
Muhammed, Adnan
 and 
Longley, Paul
 and 
Maciejewski, Ross
},
year = {
2014},

publisher = {
The Eurographics Association},


ISBN = {978-3-905674-68-2},

DOI = {
10.2312/eurova.20141143}

}
@inproceedings{
10.2312:eurova.20141144,

booktitle = {
EuroVis Workshop on Visual Analytics},

editor = {
M. Pohl and J. Roberts
},
title = {{
A Visual Analytics Field Experiment to Evaluate Alternative Visualizations for Cyber Security Applications}},

author = {
Fischer, Fabian
 and 
Davey, James
 and 
Fuchs, Johannes
 and 
Thonnard, Olivier
 and 
Kohlhammer, Jörn
 and 
Keim, Daniel A.
},
year = {
2014},

publisher = {
The Eurographics Association},


ISBN = {978-3-905674-68-2},

DOI = {
10.2312/eurova.20141144}

}
@inproceedings{
10.2312:eurova.20141141,

booktitle = {
EuroVis Workshop on Visual Analytics},

editor = {
M. Pohl and J. Roberts
},
title = {{
Integrated Visualization and Analysis of a Multi-scale Biomedical Knowledge Space}},

author = {
Agibetov, Asan
 and 
Vaquero, Ricardo Manuel Millan
 and 
Friese, Karl-Ingo
 and 
Patane, Giuseppe
 and 
Spagnuolo, Michela
 and 
Wolter, Franz-Erich
},
year = {
2014},

publisher = {
The Eurographics Association},


ISBN = {978-3-905674-68-2},

DOI = {
10.2312/eurova.20141141}

}
@inproceedings{
10.2312:eurova.20141147,

booktitle = {
EuroVis Workshop on Visual Analytics},

editor = {
M. Pohl and J. Roberts
},
title = {{
Towards more Visual Analytics in Learning Analytics}},

author = {
Ritsos, Panagiotis D.
 and 
Roberts, Jonathan C.
},
year = {
2014},

publisher = {
The Eurographics Association},


ISBN = {978-3-905674-68-2},

DOI = {
10.2312/eurova.20141147}

}
@inproceedings{
10.2312:eurova.20141146,

booktitle = {
EuroVis Workshop on Visual Analytics},

editor = {
M. Pohl and J. Roberts
},
title = {{
Interactively Visualizing Summaries of Rules and Exceptions}},

author = {
Sharma, Geetika
 and 
Shroff, Gautam
 and 
Pandey, Aditeya
 and 
Agarwal, Puneet
 and 
Srinivasan, Ashwin
},
year = {
2014},

publisher = {
The Eurographics Association},


ISBN = {978-3-905674-68-2},

DOI = {
10.2312/eurova.20141146}

}
@inproceedings{
10.2312:eurova.20141145,

booktitle = {
EuroVis Workshop on Visual Analytics},

editor = {
M. Pohl and J. Roberts
},
title = {{
Supporting an Early Detection of Diabetic Neuropathy by Visual Analytics}},

author = {
Luboschik, Martin
 and 
Röhlig, Martin
 and 
Kundt, Günther
 and 
Stachs, Oliver
 and 
Peschel, Sabine
 and 
Zhivov, Andrey
 and 
Guthoff, Rudolf F.
 and 
Winter, Karsten
 and 
Schumann, Heidrun
},
year = {
2014},

publisher = {
The Eurographics Association},


ISBN = {978-3-905674-68-2},

DOI = {
10.2312/eurova.20141145}

}

Browse

Recent Submissions

Now showing 1 - 11 of 11
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    Visual Analytics for Risk-based Decision Making, Long-Term Planning, and Assessment Process
    (The Eurographics Association, 2014) Oliveros, Silvia; Yang, Yang; Jang, Yun; Maule, Ben; Ebert, David; M. Pohl and J. Roberts
    Risk-based decision making is a data-driven process used to gather data about outcomes, analyze different scenarios, and deliver informed decisions to mitigate risk. We describe the design and application of integrated visual analytics techniques and components to support risk-based decision making following a structured risk management process in the US Coast Guard domain. The components proposed perform the following interactive tasks: the identification of risk priority areas, the distribution of pre-computed risk values, and the analysis of coverage versus risk, all of which equip analysts with the tools to examine the different decision factors and assist course of action development in the long-term planning and assessment process.
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    From Ill-defined Problems to Informed Decisions
    (The Eurographics Association, 2014) Roberts, Jonathan; Keim, Daniel; Hanratty, Timothy; Rowlingson, Robert; Walker, Rick; Hall, Mark; Jackobson, Zack; Lavigne, Valerie; Rooney, Chris; Varga, Margaret; M. Pohl and J. Roberts
    Decision makers such as military leaders and security analysts are increasingly being asked to make decisions on ill-defined problems. These problems may contain uncertain or incomplete data, and are often complex to piece together. Consequently, decision makers rely heavily on intuition, knowledge and experience. We argue for rich narratives that encapsulate both explicit data and implicit knowledge, supported by three levels of provenance: data, analytical and reasoning. Our hypotheses is that visual analytics tools and methods can help to provide a valuable means to make sense of these complex data, and to help make this tacit knowledge explicit, to support the construction and presentation of the decision.
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    Guided Sketching for Visual Search and Exploration in Large Scatter Plot Spaces
    (The Eurographics Association, 2014) Shao, Lin; Behrisch, Michael; Schreck, Tobias; Landesberger, Tatiana von; Scherer, Maximilian; Bremm, Sebastian; Keim, Daniel; M. Pohl and J. Roberts
    Recently, there has been an interest in methods for filtering large scatter plot spaces for interesting patterns. However, user interaction remains crucial in starting an explorative analysis in a large scatter plot space. We introduce an approach for explorative search and navigation in large sets of scatter plot diagrams. By means of a sketch-based query interface, users can start the exploration process by providing a visual example of the pattern they are interested in. A shadow-drawing approach provides suggestions for possibly relevant patterns while query drawing takes place, supporting the visual search process. We apply the approach on a large real-world data set, demonstrating the principal functionality and usefulness of our technique.
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    Ribbons: Enabling the Effective Use of HPC Utilization Data for System Support Staff
    (The Eurographics Association, 2014) Sisneros, Robert; Fullop, Joshi; Semeraro, B. David; Bauer, Greg; M. Pohl and J. Roberts
    Beyond raw computational power, a supercomputer offers the capability of generating and logging a significant amount of diagnostic data. While adding to the burden of maintenance, this data nevertheless represents compelling opportunities for development directed toward improved evaluations, diagnostics, analytics, etc. We have developed such a utility, a visual analytics tool for the support staff of the Blue Waters supercomputer. Our initial goal was broad: provide an informative illustration of current running jobs on the machine for the purpose of system monitoring. Additionally, we were able to collect diverse utilization data to the extent that both minimizing exclusion of as well as intuitively coordinating information were equally challenging. Our primary visual element is an extension of a stacked bar chart to increase horizontal continuity; resulting visualizations show system utilization as a series of concurrent job ''ribbons''. The remaining elements are common visual/interactive techniques offering expansive functionality. Together these components were deployed as a web application, which is referred to as the ''ribbon viewer'' by its regular users. In this paper we will highlight the design nuances and development complexities that are belied by the ribbon viewer's apparent simplicity. We will also discuss use-case scenarios in terms of both typical usage and specific examples.
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    A Visual Analytics Approach to Segmenting and Labeling Multivariate Time Series Data
    (The Eurographics Association, 2014) Alsallakh, Bilal; Bögl, Markus; Gschwandtner, Theresia; Miksch, Silvia; Esmael, Bilal; Arnaout, Arghad; Thonhauser, Gerhard; Zöllner, Philipp; M. Pohl and J. Roberts
    Many natural and industrial processes such as oil well construction are composed of a sequence of recurring activities. Such processes can often be monitored via multiple sensors that record physical measurements over time. Using these measurements, it is sometimes possible to reconstruct the processes by segmenting the respective time series data into intervals that correspond to the constituent activities. While automated algorithms can compute this segmentation rapidly, they cannot always achieve the required accuracy rate e.g. due to process variations that need human judgment to account for. We propose a Visual Analytics approach that intertwines interactive time series visualization with automated algorithms for segmenting and labeling multivariate time series data. Our approach helps domain experts to inspect the results, identify segmentation problems, and correct mislabeled segments accordingly. We demonstrate how our approach is applied in the drilling industry and discuss its applicability to other domains having similar requirements.
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    What's In a Name? Data Linkage, Demography and Visual Analytics
    (The Eurographics Association, 2014) Wang, Feng; Ibarra, Jose; Muhammed, Adnan; Longley, Paul; Maciejewski, Ross; M. Pohl and J. Roberts
    This work explores the development of a visual analytics tool for geodemographic exploration in an online environment. We mine 78 million records from the United States public telephone directories, link the location data to demographic data (specifically income) from the United States Census Bureau, and allow users to interactively compare distributions of names with regards to spatial location similarity and income. In order to enable interactive similarity exploration, we explore methods of pre-processing the data as well as on-the-fly lookups. As data becomes larger and more complex, the development of appropriate data storage and analytics solutions has become even more critical when enabling online visualization. We discuss problems faced in implementation, design decisions and directions for future work.
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    A Visual Analytics Field Experiment to Evaluate Alternative Visualizations for Cyber Security Applications
    (The Eurographics Association, 2014) Fischer, Fabian; Davey, James; Fuchs, Johannes; Thonnard, Olivier; Kohlhammer, Jörn; Keim, Daniel A.; M. Pohl and J. Roberts
    The analysis and exploration of emerging threats in the Internet is important to better understand the behaviour of attackers and develop new methods to enhance cyber security. Fully automated algorithms alone are often not capable of providing actionable insights about the threat landscape. We therefore combine a multi-criteria clustering algorithm, tailor-made for the identification of such attack campaigns with three interactive visualizations, namely treemap representations, interactive node-link diagrams, and chord diagrams, to allow the analysts to visually explore and make sense of the resulting multi-dimensional clusters. To demonstrate the potential of the system, we share our lessons learned in conducting a field experiment with experts in a security response team and show how it helped them to gain new insights into various threat landscapes.
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    Integrated Visualization and Analysis of a Multi-scale Biomedical Knowledge Space
    (The Eurographics Association, 2014) Agibetov, Asan; Vaquero, Ricardo Manuel Millan; Friese, Karl-Ingo; Patane, Giuseppe; Spagnuolo, Michela; Wolter, Franz-Erich; M. Pohl and J. Roberts
    The study and analysis of relationships in a complex and multi-scale data set is a challenge of information and scientific visualization. This work proposes an integrated visualization to capture all the important aspects of multi-scale data into the same view by leveraging the multi-scale biomedical knowledge encoded into an underlying ontology. Ontology supports visualization by providing semantic means to identify relevant items that must be presented to the user. The study and analysis of relationships across the scales are presented as results of queries to the multi-scale biomedical knowledge space. We demonstrate the prototype of the graphical interface of an integrated visualization framework and the knowledge formalization support in an example scenario related to the musculoskeletal diseases.
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    Towards more Visual Analytics in Learning Analytics
    (The Eurographics Association, 2014) Ritsos, Panagiotis D.; Roberts, Jonathan C.; M. Pohl and J. Roberts
    Learning Analytics is the collection, management and analysis of students' learning. It is used to enable teachers to understand how their students are progressing and for learners to ascertain how well they are performing. Often the data is displayed through dashboards. However, there is a huge opportunity to include more comprehensive and interactive visualizations that provide visual depictions and analysis throughout the lifetime of the learner, monitoring their progress from novices to experts. We therefore encourage researchers to take a comprehensive approach and re-think how visual analytics can be applied to the learning environment, and develop more interactive and exploratory interfaces for the learner and teacher.
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    Interactively Visualizing Summaries of Rules and Exceptions
    (The Eurographics Association, 2014) Sharma, Geetika; Shroff, Gautam; Pandey, Aditeya; Agarwal, Puneet; Srinivasan, Ashwin; M. Pohl and J. Roberts
    Rules along with their exceptions have been used to explain large data sets in a comprehensible manner. In this paper we describe an interactive visualization scheme for rules and their exceptions. Our visual encoding is based on principles for creating perceptually effective visualizations from literature. Our visualization scheme presents an overview first, allows semantic zooming and then shows details on demand using established principles of interactive visualization. We assume that rules and exceptions have been mined and summarized using available techniques; however our visualization is applicable for more general rule hierarchies as well. We illustrate our visualization using rules and exceptions extracted from real customer surveys as well as on rule sets derived from past literature.
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    Supporting an Early Detection of Diabetic Neuropathy by Visual Analytics
    (The Eurographics Association, 2014) Luboschik, Martin; Röhlig, Martin; Kundt, Günther; Stachs, Oliver; Peschel, Sabine; Zhivov, Andrey; Guthoff, Rudolf F.; Winter, Karsten; Schumann, Heidrun; M. Pohl and J. Roberts
    In this paper, we describe a step-wise approach to utilize ophthalmic markers for detecting early diabetic neuropathy (DN), the most common long-term complication of diabetes mellitus. Our approach is based on the Visual Analytics Mantra: First, we statistically analyze the data to identify those variables that separate DN patients from a control group. Afterwards, we show the important separating variables individually, but also in the context of all variables regarding a pre-defined classification. By doing so, we support the understanding of the categorization in respect of the value distribution of variables. This allows for zooming, filtering and further analysis like deleting non-relevant variables that do not contribute to the definition of markers as well as deleting data records with false data values or false classifications. Finally, outliers are observed and investigated in detail. So, a third group of potential DN patients can be introduced. In this way, the detection of early DN can be effectively supported.