EuroVA: International Workshop on Visual Analytics

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

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Adaptive Interactive Multi-Resolution Computational Steering for Complex Engineering Systems
(The Eurographics Association, 2011) Matkovic, K.; Gracanin, D.; Jelovic, M.; Cao, Y.; Silvia Miksch and Giuseppe Santucci
Computational steering integrates modeling, computation, data analysis, visualization, and data input components of a simulation. Since the simulation space is, in general, very large and continuous, selecting discrete simulation points that can reasonably represent the whole simulation space is difficult. We need to interpolate the missing values and cover a continuous region of interest in the simulation space. We describe an approach that, in an iterative manner, allows a domain expert to interactively select data points (design of experiments), approximate the values in a continuous region of the simulation space (regression) and automatically find the best points in that continuous region based on the specified constraints and objectives (optimization), using the regression and aggregated data. Once the objectives are found, the data points in the neighborhood of the objective are generated by the simulation tool thus providing a denser coverage of the regions of interest.
AMPLIO VQA - A Web Based Visual Query Analysis System for Micro Grid Energy Mix Planning
(The Eurographics Association, 2012) Stoffel, Andreas; Zhang, Leishi; Weber, Stefan Hagen; Keim, Daniel A.; Kresimir Matkovic and Giuseppe Santucci
Micro grid technology brings the opportunity to integrate renewable energies with a traditional energy mix on a regional level and to achieve specific local goals. To obtain an optimal energy mix plan, different scenarios need to be simulated and analyzed. However, effective tools for analyzing such simulation results are lacking. Here we present an interactive visual query analysis tool designed for that purpose. The tool integrates effective visualization techniques and advanced pattern detection methods.
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.
Attribute-based Visual Explanation of Multidimensional Projections
(The Eurographics Association, 2015) Silva, Renato R. O. da; Rauber, Paulo E.; Martins, Rafael M.; Minghim, Rosane; Telea, Alexandru C.; E. Bertini and J. C. Roberts
Multidimensional projections (MPs) are key tools for the analysis of multidimensional data. MPs reduce data dimensionality while keeping the original distance structure in the low-dimensional output space, typically shown by a 2D scatterplot. While MP techniques grow more precise and scalable, they still do not show how the original dimensions (attributes) influence the projection's layout. In other words, MPs show which points are similar, but not why. We propose a visual approach to describe which dimensions contribute mostly to similarity relationships over the projection, thus explain the projection's layout. For this, we rank dimensions by increasing variance over each point-neighborhood, and propose a visual encoding to show the least-varying dimensions over each neighborhood. We demonstrate our technique with both synthetic and real-world datasets.
Capturing Reasoning Process through User Interaction
(The Eurographics Association, 2010) Dou, Wenwen; Ribarsky, William; Chang, Remco; Joern Kohlhammer and Daniel Keim
In recent years, visual analytics has taken an important role in solving many types of complex analytical problems that require deep and specific domain knowledge from users. While the analysis products generated by these expert users are of great importance, how these users apply their domain expertise in using the visualization to validate their hypotheses and arrive at conclusions is often just as invaluable. Recent research efforts in capturing an expert's reasoning process using a visualization have shown that some of a user's analysis process is indeed recoverable. However, there does not exist a generalizable principle that explains the success of these domainspecific systems in capturing the user's reasoning process. In this paper, we present a framework that examines two aspects of the capturing process. First, we inspect how a user's reasoning process can be captured by utilizing van Wijk's operational model of visualization. Second, we evaluate the likelihood of success in capturing a user's interactions in a visualization by introducing three criteria designed for disambiguating the meanings behind the interactions. Various visualization systems in the visualization and HCI communities are examined for the purpose of demonstrating the impact of the three criteria.
Characterizing Visual Analytics in Diagnostic Imaging
(The Eurographics Association, 2011) Lundström, C.; Persson, A.; Silvia Miksch and Giuseppe Santucci
Many necessary and desired improvements in healthcare are dependent on progress in medical imaging. As shown in this paper, the challenges targeted by visual analytics (VA) coincide with main challenges for radiologists' diagnostic work. Key prerequisites for VA in this application domain have been identified through analysis of a survey among 22 radiologists at a university hospital. Two major findings are that efficiency is perceived as the most challenging aspect of their diagnostic work and that an exploratory approach is necessary in everyday image review. The presented characterization constitutes a validated input for design of future VA research initiatives within medical imaging.
ChatKG: Visualizing Temporal Patterns as Knowledge Graph
(The Eurographics Association, 2023) Christino, Leonardo; Paulovich, Fernando V.; Angelini, Marco; El-Assady, Mennatallah
Line-chart visualizations of temporal data enable users to identify interesting patterns for the user to inquire about. Using oracles, such as chat AIs, Visual Analytic tools can automatically uncover explicit knowledge related information to said patterns. Yet, visualizing the association of data, patterns, and knowledge is not straightforward. In this paper, we present ChatKG, a novel visualization strategy that allows exploratory data analysis of a Knowledge Graph which associates a dataset of temporal sequences, the patterns found in each sequence, the temporal overlap between patterns, and related explicit knowledge to each given pattern. We exemplify and informally evaluate ChatKG by analyzing the world's life expectancy. For this, we implement an oracle that automatically extracts relevant or interesting patterns, inquires chatGPT for related information, and populates the Knowledge Graph which is visualized. Our tests and an interview conducted showed that ChatKG is well suited for temporal analysis of temporal patterns and their related knowledge when applied to history studies.
CODAS: Integrating Business Analytics and Report Authoring
(The Eurographics Association, 2022) Zhang, Zhuohao; Malik, Sana; Guo, Shunan; Hoffswell, Jane; Rossi, Ryan; Du, Fan; Koh, Eunyee; Bernard, Jürgen; Angelini, Marco
Business analysts create rich dashboards to find data insights and subsequently communicate these findings with data-driven reports that combine visualization screenshots and descriptive text. Conventional analytics reports convey findings statically and passively, which suffers from limited interactivity and adaptability to data changes. There is therefore a need to facilitate authoring of interactive reports in business analytics. To better support the needs of business analysts, we developed CODAS: a report authoring tool that allows analysts to transform dashboards into interactive, web-based reports through a no-coding user interface and a workflow that is compatible to business analysts' existing data analytics pipelines. CODAS supports authoring multiple levels of interactions, organizing story elements, and generating the final artifact. Through our case studies with two expert analysts, we discuss the usefulness of our system and report our findings on analysts' report authoring workflow. Our findings suggest that CODAS enables business analysts to create interactive, data-driven reports comfortably, and can complement their exisitng data analytics workflow without extra learning effort.
Combining Cluster and Outlier Analysis with Visual Analytics
(The Eurographics Association, 2017) Bernard, Jürgen; Dobermann, Eduard; Sedlmair, Michael; Fellner, Dieter W.; Michael Sedlmair and Christian Tominski
Cluster and outlier analysis are two important tasks. Due to their nature these tasks seem to be opposed to each other, i.e., data objects either belong to a cluster structure or a sparsely populated outlier region. In this work, we present a visual analytics tool that allows the combined analysis of clusters and outliers. Users can add multiple clustering and outlier analysis algorithms, compare results visually, and combine the algorithms' results. The usefulness of the combined analysis is demonstrated using the example of labeling unknown data sets. The usage scenario also shows that identified clusters and outliers can share joint areas of the data space.
Combining Details of the Chi-Square Goodness-of-Fit Test with Multivariate Data Visualization
(The Eurographics Association, 2010) May, Thorsten; Davey, James; Kohlhammer, Jörn; Joern Kohlhammer and Daniel Keim
In this work, we combine KVMaps, a visualization technique presented in [May07] for the visualization of statistical aggregations in multivariate contingency tables, with the measures used for the statistical Chi-Square goodness-of-fit test. Goodness-of-fit tests are used to check whether a given distribution of values matches an expected distribution. A single test statistic is calculated to represent the deviation of the complete dataset. By visualizing the deviations for all entries in the contingency table, it is possible to identify the patterns in the distribution of data items, which contribute most to the overall deviation of the dataset. We present two use cases to illustrate how the information about the patterns can be used.
Combining the Automated Segmentation and Visual Analysis of Multivariate Time Series
(The Eurographics Association, 2018) Bernard, Jürgen; Bors, Christian; Bögl, Markus; Eichner, Christian; Gschwandtner, Theresia; Miksch, Silvia; Schumann, Heidrun; Kohlhammer, Jörn; Christian Tominski and Tatiana von Landesberger
For the automatic segmentation of multivariate time series domain experts at first need to consider a huge space of alternative configurations of algorithms and parameters. We assume that only a small subset of these configurations needs to be computed and analyzed to lead users to meaningful configurations. To expedite this search, we propose the conceptualization of a segmentation workflow. First, with an algorithmic segmentation pipeline, domain experts can calculate segmentation results with different parameter configurations. Second, in an interactive visual analysis step, domain experts can explore segmentation results to further adapt and improve segmentation pipeline in an informed way. In the interactive analysis approach influences of algorithms, parameters, and different types of uncertainty information are conveyed, which is decisive to trigger selective and purposeful re-calculations. The workflow is built upon reflections on collaborations with domain experts working in activity recognition, which also defines our usage scenario demonstrating the applicability of the workflow.
ComModeler: Topic Modeling Using Community Detection
(The Eurographics Association, 2018) Dang, Tommy; Nguyen, Vinh The; Christian Tominski and Tatiana von Landesberger
This paper introduces ComModeler, a novel approach for topic modeling using community finding in dynamic networks. Our algorithm first extracts the terms/keywords, formulates a network of collocated terms, then refines the network based on various features (such as term/relationship frequency, sudden changes in their frequency time series, or vertex betweenness centrality) to reveal the structures/communities in dynamic social networks. These communities correspond to different hidden topics in the input text documents. Although initially motivated to analyze text documents, we soon realized the ComModeler has more general implications for other application domains. We demonstrate the ComModeler on several real-world datasets, including the IEEE VIS publications from 1990 to 2016, together with collocated phrases obtained from various political blogs.
Comparative Visual Analysis of Cross-Linguistic Features
(The Eurographics Association, 2010) Rohrdantz, Christian; Mayer, Thomas; Butt, Miriam; Plank, Frans; Keim, Daniel A.; Joern Kohlhammer and Daniel Keim
Approaches in Visual Analytics have so far been developed for a wide array of research areas, mainly with a focus on industrial or business applications. The field of linguistics, however, has only marginally incorporated visualizations in its research, e.g. using simple tree representations, attribute-value matrices or network analyses. This paper suggests a new interesting field of application demonstrating how Visual Analytics is able to support linguists in their research. We show this with respect to one concrete linguistic phenomenon, named Vowel Harmony, where visual analysis allows an at-a-glance comparison across a variety of languages. Our approach covers the entire pipeline of Visual Analytics methodology: data processing, feature extraction and the creation of an interactive visual representation. Our results allow for a novel approach to linguistic investigation in that we enable an at-a-glance analysis of whether vowel harmony is present in a language and, beyond that, a precise indication of the particular type of vowel interdependence and patterning in a given language.
A Comprehensive Workflow for Effective Imitation and Reinforcement Learning with Visual Analytics
(The Eurographics Association, 2022) Metz, Yannick; Schlegel, Udo; Seebacher, Daniel; El-Assady, Mennatallah; Keim, Daniel; Bernard, Jürgen; Angelini, Marco
Multiple challenges hinder the application of reinforcement learning algorithms in experimental and real-world use cases even with recent successes in such areas. Such challenges occur at different stages of the development and deployment of such models. While reinforcement learning workflows share similarities with machine learning approaches, we argue that distinct challenges can be tackled and overcome using visual analytic concepts. Thus, we propose a comprehensive workflow for reinforcement learning and present an implementation of our workflow incorporating visual analytic concepts integrating tailored views and visualizations for different stages and tasks of the workflow.
Computing Fast and Accurate Decision Boundary Maps
(The Eurographics Association, 2024) Grosu, Cristian; Wang, Yu; Telea, Alexandru; El-Assady, Mennatallah; Schulz, Hans-Jörg
Decision boundary maps (DBMs) are image representations of the behavior of trained machine learning classification models. They are used in examining how the model partitions its data space into decision zones separated by decision boundaries and how this partition is influenced by the training data. However, all current DBM methods require significant computational effort, which precludes their use in interactive visual analytics scenarios. We present FastDBM, a set of techniques for the fast computation of DBMs. Our methods can accelerate any existing DBM algorithm by over one order of magnitude, yield results very similar to the original DBM methods, have a single parameter to set (with good presets), and are simple to implement. We demonstrate our method on various combinations of DBM techniques, datasets, and classification models.
A Concept for Consensus-based Ordering of Views
(The Eurographics Association, 2018) Jentner, Wolfgang; Jäckle, Dominik; Engelke, Ulrich; Keim, Daniel A.; Schreck, Tobias; Christian Tominski and Tatiana von Landesberger
High-dimensional data poses a significant challenge for analysis, as patterns typically exist only in subsets of dimensions or records. A common approach to reveal patterns, such as meaningful structures or relationships, is to split the data and then to create a visual representation (views) for each data subset. This introduces the problem of ordering the views effectively because patterns can depend on the presented sequence. Existing methods provide metrics and heuristics to achieve an ordering of views based on their data characteristics. However, an effective ordering of subspace views is expected to rely on task- and data-dependent properties. Hence, heuristic-based ordering methods can be highly objective and not relevant to the task at hand, which is why the user involvement is key to find a meaningful ordering. We introduce a concept for a consensus-based ordering of views that learns to form sequences of subset views fitting the overall users' needs. This concept allows users to decide on the ordering freely and accumulates their preference into a global view that reflects the consensus. We showcase and discuss this concept based on ordering colored tiles from the controversially discussed rainbow color map.
Congnostics: Visual Features for Doubly Time Series Plots
(The Eurographics Association, 2020) Nguyen, Bao Dien Quoc; Hewett, Rattikorn; Dang, Tommy; Turkay, Cagatay and Vrotsou, Katerina
In this paper, we propose an analytical approach to automatically extract visual features from doubly time series capturing the unusual associations which are not otherwise possible by investigating individual time series alone. We have extended the visual measures for 2D scatterplots, incorporated univariate time series analysis, and proposed new visual features for doubly time series plots. These measures are discussed and demonstrated via visual examples to clarify their implications and their effectiveness. The results show that distributions, trend, shape, noise, among other characteristics, can be used to uncover the latent features and events in temporal datasets.
Contextualized Analysis of Movement Events
(The Eurographics Association, 2019) Chen, Siming; Andrienko, Gennady; Andrienko, Natalia; Doulkeridis, Christos; Koumparos, Athanasios; Landesberger, Tatiana von and Turkay, Cagatay
For understanding the circumstances, causes, and consequences of events that may happen during movement (e.g., harsh brake, sharp turn), it is necessary to analyze event context. The context includes dynamic attributes of the moving objects before and after the event and external context elements such as other moving objects, weather, terrain, etc. To explore events in context, we propose an analytical workflow including event contextualization, context pattern detection, and exploration of the spatio-temporal distribution of the detected patterns. The approach involves clustering of events based on the similarity of their contexts and interactive visual techniques for exploration of the distribution of the clusters in time, geographic space, and multidimensional attribute space. In close collaboration with domain experts, we apply our method to real-world vehicle trajectories with the purpose of identifying and investigating potentially dangerous driving behaviors.
Contingency Wheel: Visual Analysis of Large Contingency Tables
(The Eurographics Association, 2011) Alsallakh, Bilal; Groeller, Eduard; Miksch, Silvia; Suntinger, Martin; Silvia Miksch and Giuseppe Santucci
We present the Contingency Wheel, a visual method for finding and analyzing associations in a large nxm contingency table with m less than 100 and n being two to three orders of magnitude larger than m. The method is demonstrated on a large table from the Book-Crossing dataset, which counts the number of ratings each book received from each country. It enables finding books that received a disproportionately high number of ratings from a specific country. It further allows to visually analyze what these books have in common, and with which countries they are also highly associated. Pairs of similar countries can further be identified (in the sense that many books are associated with both countries). Compared with existing visual methods, our approach enables analyzing and gaining insight into larger tables.
cPro: Circular Projections Using Gradient Descent
(The Eurographics Association, 2024) Buchmüller, Raphael; Jäckl, Bastian; Behrisch, Michael; Keim, Daniel A.; Dennig, Frederik L.; El-Assady, Mennatallah; Schulz, Hans-Jörg
Typical projection methods such as PCA or MDS rely on mapping data onto an Euclidean space, limiting the design of resulting visualizations to lines, planes, or cubes and thus may fail to capture the intrinsic non-linear relationships within data, resulting in inefficient use of two-dimensional space. We introduce the novel projection technique -cPro-, which aligns high-dimensional data onto a circular layout. We apply gradient descent, an adaptable optimization technique to efficiently reduce a customized loss function. We use selected distance measures to reduce high data dimensionality and reveal patterns on a two-dimensional ring layout. We evaluate our approach compared to 1D and 2D MDS and discuss further use cases and potential extensions. cPro enables the design of novel visualization techniques that employ semantic distances on a circular layout.

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