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Item Guidance or No Guidance? A Decision Tree Can Help(The Eurographics Association, 2018) Ceneda, Davide; Gschwandtner, Theresia; May, Thorsten; Miksch, Silvia; Streit, Marc; Tominski, Christian; Christian Tominski and Tatiana von LandesbergerGuidance methods have the potential of bringing considerable benefits to Visual Analytics (VA), alleviating the burden on the user and allowing a positive analysis outcome. However, the boundary between conventional VA approaches and guidance is not sharply defined. As a consequence, framing existing guidance methods is complicated and the development of new approaches is also compromised. In this paper, we try to bring these concepts in order, defining clearer boundaries between guidance and no-guidance. We summarize our findings in form of a decision tree that allows scientists and designers to easily frame their solutions. Finally, we demonstrate the usefulness of our findings by applying our guideline to a set of published approaches.Item 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 LandesbergerFor 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.Item Visual Parameter Space Exploration in Time and Space(© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Piccolotto, Nikolaus; Bögl, Markus; Miksch, Silvia; Hauser, Helwig and Alliez, PierreComputational models, such as simulations, are central to a wide range of fields in science and industry. Those models take input parameters and produce some output. To fully exploit their utility, relations between parameters and outputs must be understood. These include, for example, which parameter setting produces the best result (optimization) or which ranges of parameter settings produce a wide variety of results (sensitivity). Such tasks are often difficult to achieve for various reasons, for example, the size of the parameter space, and supported with visual analytics. In this paper, we survey visual parameter space exploration (VPSE) systems involving spatial and temporal data. We focus on interactive visualizations and user interfaces. Through thematic analysis of the surveyed papers, we identify common workflow steps and approaches to support them. We also identify topics for future work that will help enable VPSE on a greater variety of computational models.Item Exploring Time Series Segmentations Using Uncertainty and Focus+Context Techniques(The Eurographics Association, 2020) Bors, Christian; Eichner, Christian; Miksch, Silvia; Tominski, Christian; Schumann, Heidrun; Gschwandtner, Theresia; Kerren, Andreas and Garth, Christoph and Marai, G. ElisabetaTime series segmentation is employed in various domains and continues to be a relevant topic of research. A segmentation pipeline is composed of different steps involving several parameterizable algorithms. Existing Visual Analytics approaches can help experts determine appropriate parameterizations and corresponding segmentation results for a given dataset. However, the results may also be afflicted with different types of uncertainties. Hence, experts face the additional challenge of understanding the reliability of multiple alternative the segmentation results. So far, the influence of uncertainties in the context of time series segmentation could not be investigated. We present an uncertainty-aware exploration approach for analyzing large sets of multivariate time series segmentations. The approach features an overview of uncertainties and time series segmentations, while detailed exploration is facilitated by (1) a lens-based focus+context technique and (2) uncertainty-based re-arrangement. The suitability of our uncertainty-aware design was evaluated in a quantitative user study, which resulted in interesting findings of general validity.Item Persistent Interaction: User-Generated Artefacts in Visual Analytics(The Eurographics Association, 2024) Pérez-Messina, Ignacio; Ceneda, Davide; Schetinger, Victor; Miksch, Silvia; El-Assady, Mennatallah; Schulz, Hans-JörgWhile traditional approaches in visual analytics (VA) prioritize insight generation and knowledge discovery, we argue that user-generated artefacts-annotations, model parameters, subset selections, spatializations, and other constructs-constitute a significant outcome of the analytical process. Drawing from theoretical models in VA literature, we introduce persistent interaction as techniques capturing user decisions. These interactions, called operations, provide a formalization of how users attach subjective judgments to datasets, condensing this input into artefacts serving specific purposes within broader workflows. We provide a description and classification of persistent interaction techniques and outcomes, demonstrating their practical implications in VA systems for system design, information transferability, and guidance capabilities.Item VisMiFlow: Visual Analytics to Support Citizen Migration Understanding Over Time and Space(The Eurographics Association, 2021) Scheidl, Andreas; Leite, Roger A.; Miksch, Silvia; Agus, Marco and Garth, Christoph and Kerren, AndreasMultivariate networks are complex data structures, which are ubiquitous in many application domains. Driven by a real-world problem, namely the movement behavior of citizens in Vienna, we designed and implemented a Visual Analytics (VA) approach to ease citizen behavior analyses over time and space. We used a dataset of citizens' movement behavior to, from, or within Vienna from 2007 to 2018, provided by Vienna's city. To tackle the complexity of time, space, and other moving people's attributes, we follow a data-user-tasks design approach to support urban developers. We qualitatively evaluated our VA approach with five experts coming from the field of VA and one non-expert. The evaluation illustrated the importance of task-specific visualization and interaction techniques to support users' decision-making and insights. We elaborate on our findings and suggest potential future works to the field.Item Shapes of Time: Visualizing Set Changes Over Time in Cultural Heritage Collections(The Eurographics Association, 2019) Salisu, Saminu; Mayr, Eva; Filipov, Velitchko Andreev; Leite, Roger A.; Miksch, Silvia; Windhager, Florian; Madeiras Pereira, João and Raidou, Renata GeorgiaIn cultural heritage collections, categorization is a central technique used to distinguish cultural movements, styles, or genres. For that end, objects are tagged with set-typed metadata and other information, such as time of origin. Visualizations can communicate how such sets organize a collection - and how they change over time. But existing interfaces fall short of a) representing an overview of temporal set-developments in an integrated fashion and b) of representing the set elements (i.e., the cultural objects) themselves to be contemplated on demand. Against this background, we introduce two integrated visualization techniques - a superimposition and a space-time cube view - depicting the development of sets and their elements over time. We share first results from a qualitative evaluation with casual users and outline open challenges for future research.Item Multi-Ensemble Visual Analytics via Fuzzy Sets(The Eurographics Association, 2023) Piccolotto, Nikolaus; Bögl, Markus; Miksch, Silvia; Angelini, Marco; El-Assady, MennatallahAnalysis of ensemble datasets, i.e., collections of complex elements such as geochemical maps, is widespread in science and industry. The elements' complexity arises from the data they capture, which are often multivariate or spatio-temporal. We speak of multi-ensemble datasets when the analysis pertains to multiple ensembles. While many visualization approaches were suggested for ensemble datasets, multi-ensemble datasets remain comparatively underexplored. Our years-long collaboration with statisticians and geochemists taught us that they frame many questions about multi-ensemble data as set operations. E.g., what are the most common members (intersection of ensembles), or what features exist in one member but not another (difference of members)? As classical crisp set relations cannot account for the elements' complexity, we propose to model multi-ensembles as fuzzy relations. We provide examples of fuzzy set-based queries on a multi-ensemble of geochemical maps and integrate this approach into an existing ensemble visualization pipeline. We evaluated two visualizations obtained by applying this pipeline with experts in geochemistry and statistics. The experts confirmed known information and got directions for further research, which is one Visual Analytics (VA) goal. Hence, our proposal is highly promising for an interactive VA approach.Item Visually Exploring Data Provenance and Quality of Open Data(The Eurographics Association, 2018) Bors, Christian; Gschwandtner, Theresia; Miksch, Silvia; Anna Puig and Renata RaidouWhile open data platforms are increasingly popular among end-users as well as data providers, there is a growing problem with inconsistent update frequencies and lack of quality in datasets. Efforts to monitor data quality are currently limited to checking meta-information and creating revisions to allow manual inspection of former datasets.We employ a Visual Analytics framework for generating and visualizing data provenance from data quality to facilitate data analysis and help users to understand the impact of updates on the data. Data quality metrics are utilized to quantify the development of data quality over time for open data projects. We combine quality metrics, data provenance, and data transformation information in an interactive exploration environment to expedite assessment and selection of appropriate open datasets.Item A Review of Guidance Approaches in Visual Data Analysis: A Multifocal Perspective(The Eurographics Association and John Wiley & Sons Ltd., 2019) Ceneda, Davide; Gschwandtner, Theresia; Miksch, Silvia; Laramee, Robert S. and Oeltze, Steffen and Sedlmair, MichaelVisual data analysis can be envisioned as a collaboration of the user and the computational system with the aim of completing a given task. Pursuing an effective system-user integration, in which the system actively helps the user to reach his/her analysis goal has been focus of visualization research for quite some time. However, this problem is still largely unsolved. As a result, users might be overwhelmed by powerful but complex visual analysis systems which also limits their ability to produce insightful results. In this context, guidance is a promising step towards enabling an effective mixed-initiative collaboration to promote the visual analysis. However, the way how guidance should be put into practice is still to be unravelled. Thus, we conducted a comprehensive literature research and provide an overview of how guidance is tackled by different approaches in visual analysis systems. We distinguish between guidance that is provided by the system to support the user, and guidance that is provided by the user to support the system. By identifying open problems, we highlight promising research directions and point to missing factors that are needed to enable the envisioned human-computer collaboration, and thus, promote a more effective visual data analysis.
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