EuroVA: International Workshop on Visual Analytics

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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.
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.
polimaps: Supporting Predictive Policing with Visual Analytics
(The Eurographics Association, 2018) Stoffel, Florian; Post, Hanna; Stewen, Marcus; Keim, Daniel A.; Christian Tominski and Tatiana von Landesberger
Recently, predictive policing has gained a lot of attention, as the benefits, e.g., better crime prevention or an optimized resource planning are essential goals for law enforcement agencies. Commercial predictive policing systems commonly visualize predictions on maps but provide only little support for human analysts in the technical and methodological processes that constitute corresponding implementations. In this paper, we report on a project of bringing visual analytics to the field of predictive policing. We introduce a process model that includes machine learning as well as visualization and has been developed together with experts from a law enforcement agency. We also showcase a visual analytics tool, called polimaps, that is part of a real-world predictive policing project and implements elements of the proposed process.
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.
Subpopulation Discovery and Validation in Epidemiological Data
(The Eurographics Association, 2017) Alemzadeh, Shiva; Hielscher, Tommy; Niemann, Uli; Cibulski, Lena; Ittermann, Till; Völzke, Henry; Spiliopoulou, Myra; Preim, Bernhard; Michael Sedlmair and Christian Tominski
Motivated by identifying subpopulations that share common characteristics (e.g. alcohol consumption) to explain risk factors of diseases in cohort study data, we used subspace clustering to discover such subpopulations. In this paper, we describe our interactive coordinated multiple view system Visual Analytics framework S-ADVIsED for SubpopulAtion Discovery and Validation In Epidemiological Data. S-ADVIsED enables epidemiologists to explore and validate findings derived from subspace clustering. We investigated the replication of a selected subpopulation in an independent population.
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.
SurviVIS: Visual Analytics for Interactive Survival Analysis
(The Eurographics Association, 2019) Corvò, Alberto; Garcia Caballero, Humberto; Westenberg, Michel; Landesberger, Tatiana von and Turkay, Cagatay
The increasing quantity of data in biomedical informatics is leading towards better patient profiling and personalized medicine. Lab tests, medical images, and clinical data represent extraordinary sources for patient characterization. While retrospective studies focus on finding correlations in this sheer volume of data, potential new biomarkers are difficult to identify. A common approach is to observe patient mortality with respect to different clinical variables in what is called survival analysis. Kaplan-Meier plots, also known as survival curves, are generally used to examine patient survival in retrospective and prognostic studies. The plot is very intuitive and hence very popular in the medical domain to disclose evidence of poor or good prognosis. However, the Kaplan-Meier plots are mostly static and the data exploration of the plotted cohorts can be performed only with additional analysis. There is a need to make survival plots interactive and to integrate potential prognostic data that may reveal correlations with disease progression. We introduce SurviVIS, a visual analytics approach for interactive survival analysis and data integration on Kaplan-Meier plots. We demonstrate our work on a melanoma dataset and in the perspective of a potential use case in precision imaging.
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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