EuroVA: International Workshop on Visual Analytics

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https://diglib.eg.org/handle/10.2312/982

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Interactive Visual Analysis of Patient-Reported Outcomes for Improved Cancer Aftercare
(The Eurographics Association, 2019) Müller, Juliane; Zebralla, Veit; Wiegand, Susanne; Oeltze-Jafra, Steffen; Landesberger, Tatiana von and Turkay, Cagatay
The monitoring and planning of cancer aftercare are commonly based on clinical, physiological and caregiver-reported outcome measures. More recently, patient-reported outcome (PRO) measures, capturing social, psychological, and financial aspects, are gaining attention in the course of establishing a patient-centered healthcare system. PROs are acquired during regular aftercare consultations where patients are asked to fill in questionnaires. We present an interactive visual analysis (IVA) approach to investigating PROs. The approach is applied in clinical routine during the aftercare consultation to assess the development of the particular patient, to compare this development to those of similar patients, and to detect trends that may require an adaptation of the aftercare strategy. Furthermore, the approach is employed in clinical research to identify groups of similarly developing patients and risk factors for poor outcomes, as well as to visually compare patient groups. We demonstrate the IVA approach in analyzing PROs of 1025 head and neck cancer patients. In an evaluation with 20 clinicians, we assessed the usefulness and usability of a prototypical implementation.
Progressive Parameter Space Visualization for Task-Driven SAX Configuration
(The Eurographics Association, 2020) Loeschcke, Sebastian; Hogräfer, Marius; Schulz, Hans-Jörg; Turkay, Cagatay and Vrotsou, Katerina
As time series datasets are growing in size, data reduction approaches like PAA and SAX are used to keep them storable and analyzable. Yet, finding the right trade-off between data reduction and remaining utility of the data is a challenging problem. So far, it is either done in a user-driven way and offloaded to the analyst, or it is determined in a purely data-driven, automated way. None of these approaches take the analytic task to be performed on the reduced data into account. Hence, we propose a task-driven parametrization of PAA and SAX through a parameter space visualization that shows the difference of progressively running a given analytic computation on the original and on the reduced data for a representative set of data samples. We illustrate our approach in the context of climate analysis on weather data and exoplanet detection on light curve data.
Quality Metrics to Guide Visual Analysis of High Dimensional Genomics Data
(The Eurographics Association, 2020) Fernstad, Sara Johansson; Macquisten, Alexander; Berrington, Janet; Embleton, Nicholas; Stewart, Christopher; Turkay, Cagatay and Vrotsou, Katerina
Studies of genome sequenced data are increasingly common in many domains. Technological advances enable detection of hundreds of thousands of biological entities in samples, resulting in extremely high dimensional data. To enable exploration and understanding of such data, efficient visual analysis approaches are needed that take domain and data specific requirements into account. Based on a survey with bioscience experts, this paper suggests a categorisation and a set of quality metrics to identify patterns of interest, which can be used as guidance in visual analysis, as demonstrated in the paper.
Toward Disease Diagnosis Visual Support Bridging Classic and Precision Medicine
(The Eurographics Association, 2022) Palleschi, Alessia; Petti, Manuela; Tieri, Paolo; Angelini, Marco; Bernard, Jürgen; Angelini, Marco
The traditional approach in medicine starts with investigating patients' symptoms to make a diagnosis. While with the advent of precision medicine, a diagnosis results from several factors that interact and need to be analyzed together. This added complexity asks for increased support for medical personnel in analyzing these data altogether. Our objective is to merge the traditional approach with network medicine to offer a tool to investigate together symptoms, anatomies, diseases, and genes to establish a diagnosis from different points of view. This paper aims to help the clinician with the typical workflow of disease analysis, proposing a Visual Analytics tool to ease this task. A use case demonstrates the benefits of the proposed solution.
Towards the Detection and Visual Analysis of COVID-19 Infection Clusters
(The Eurographics Association, 2021) Antweiler, Dario; Sessler, David; Ginzel, Sebastian; Kohlhammer, Jörn; Vrotsou, Katerina and Bernard, Jürgen
A major challenge for departments of public health (DPHs) in dealing with the ongoing COVID-19 pandemic is tracing contacts in exponentially growing SARS-CoV2 infection clusters. Prevention of further disease spread requires a comprehensive registration of the connections between individuals and clusters. Due to the high number of infections with unknown origin, the healthcare analysts need to identify connected cases and clusters through accumulated epidemiological knowledge and the metadata of the infections in their database. Here we contribute a visual analytics framework to identify, assess and visualize clusters in COVID-19 contact tracing networks. Additionally, we demonstrate how graph-based machine learning methods can be used to find missing links between infection clusters and thus support the mission to get a comprehensive view on infection events. This work was developed through close collaboration with DPHs in Germany. We argue how our systems supports the identification of clusters by public health experts and discuss ongoing developments and possible extensions.
Towards Visual Cyber Security Analytics for the Masses
(The Eurographics Association, 2018) Ulmer, Alex; Schufrin, Marija; Lücke-Tieke, Hendrik; Kannanayikkal, Clindo Devassy; Kohlhammer, Jörn; Christian Tominski and Tatiana von Landesberger
Understanding network activity and cyber threats is of major concern these days, for business and private users alike. As more and more online applications assist us in our daily life, there is a growing potential vulnerability to cyber crime. With this paper, we want to share our vision of cyber security analytics becoming an accessible everyday task through visual analysis tools. We describe the context of this vision and our experience with the first achievements in this direction. With our new prototype, anyone can analyze their network traffic logs and get security-relevant information out of it, a task that was too difficult and sometimes too expensive in the past. We present an open, accessible and user-friendly visual network analyzer for PCAP (packet capture) files, critically discuss our first prototype, and give an outlook to anomaly detection supported by active learning in this context.
A Visual Analytics Framework for Renewable Energy Profiling and Resource Planning
(The Eurographics Association, 2023) Pammi, Ramakrishna P.; Afzal, Shehzad; Dasari, Hari Prasad; Yousaf, Muhammad; Ghani, Sohaib; Venkatraman, Murali Sankar; Hoteit, Ibrahim; Angelini, Marco; El-Assady, Mennatallah
Renewable energy growth is one of the focus areas globally against the backdrop of the global energy crisis and climate change. Energy planners are looking into clean, safe, affordable, and reliable energy generation sources for a net zero future. Countries are setting energy targets and policies prioritizing renewable energy, shifting the dependence on fossil fuels. The selection of renewable energy sources depends on the suitability of the region under consideration and requires analyzing relevant environmental datasets. In this work, we present a visual analytics framework that facilitates users to explore solar and wind energy datasets consisting of Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), Diffusive Horizontal Irradiance (DHI), and Wind Power (WP) spanning across a 40 year period. This framework provides a suite of interactive decision support tools to analyze spatiotemporal patterns, variability in the variables across space and time at different temporal resolutions, Typical Meteorological Year (TMY) data with varying percentiles, and provides the capability to interactively explore and evaluate potential solar and wind energy equipment installation locations and study different energy acquisition scenarios. This work is conducted in collaboration with domain experts involved in sustainable energy planning. Different use case scenarios are also explained in detail, along with domain experts feedback and future directions.

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