EuroVA14

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https://diglib.eg.org/handle/10.2312/7746

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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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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