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This work presents an approach to support the visual analysis of parameter dependencies of time-series segmentation. The goal is to help analysts understand which parameters have high influence and which segmentation properties ar...
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This work presents an approach to support the visual analysis of parameter dependencies of time-series segmentation. The goal is to help analysts understand which parameters have high influence and which segmentation properties are highly sensitive to parameter changes. Our approach first derives features from the segmentation output and then calculates correlations between the features and the parameters, more precisely, in parameter subranges to capture global and local dependencies. Dedicated overviews visualize the correlations to help users understand parameter impact and recognize distinct regions of influence in the parameter space. A detailed inspection of the segmentations is supported by means of visually emphasizing parameter ranges and segments participating in a dependency. This involves linking and highlighting, and also a special sorting mechanism that adjusts the visualization dynamically as users interactively explore individual dependencies. The approach is applied in the context of segmenting time series for activity recognition. Informal feedback from a domain expert suggests that our approach is a useful addition to the analyst's toolbox for time-series segmentation.
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In the past 10 years, how to visualize human emotions in communication has become an important topic. For providing personalized customer service for enterprises from self-reflection in psychology to opinion mining, emotional visu...
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In the past 10 years, how to visualize human emotions in communication has become an important topic. For providing personalized customer service for enterprises from self-reflection in psychology to opinion mining, emotional visualization uses coded emotional computing results to make various basic charts, and some novel visual analysis systems for all-round analysis which intuitively reveal personal views and emotional styles. Emotion visualization uses coded emotion computing results to reflect the emotion analysis tasks, such as self-reflection in psychology or social media opinion mining results. With the help of various basic charts, infographics, and some novel visual analysis systems, it makes all directions' analysis and intuitively reveals personal opinions and emotional styles. At present, emotional visualization has developed to use different platforms or multiple platforms to analyze various complex data, including text, sound, image, video, physiological signal or any mixed data. In this paper, we discuss a total of 75 approaches from four different categories: data source type, emotional computing, visual coding and visualization and visual analysis tasks, and 15 subcategories, including visual works mentioned in published paper and interactive visual works published on the Internet. Then, we discuss the further research approaches of emotional visualization and the prospects of emotional visualization under multidimensional data collaboration. We expect that this survey can help researchers interested in emotional visualization of varied data to find a more suitable visualization method for their data and projects.
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Abstract Music analysis tasks, such as structure identification and modulation detection, are tedious when performed manually due to the complexity of the common music notation (CMN). Fully automated analysis instead misses human ...
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Abstract Music analysis tasks, such as structure identification and modulation detection, are tedious when performed manually due to the complexity of the common music notation (CMN). Fully automated analysis instead misses human intuition about relevance. Existing approaches use abstract data‐driven visualizations to assist music analysis but lack a suitable connection to the CMN. Therefore, music analysts often prefer to remain in their familiar context. Our approach enhances the traditional analysis workflow by complementing CMN with interactive visualization entities as minimally intrusive augmentations. Gradual step‐wise transitions empower analysts to retrace and comprehend the relationship between the CMN and abstract data representations. We leverage glyph‐based visualizations for harmony, rhythm and melody to demonstrate our technique's applicability. Design‐driven visual query filters enable analysts to investigate statistical and semantic patterns on various abstraction levels. We conducted pair analytics sessions with 16 participants of different proficiency levels to gather qualitative feedback about the intuitiveness, traceability and understandability of our approach. The results show that MusicVis?supports music analysts in getting new insights about feature characteristics while increasing their engagement and willingness to explore.
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Analyzing important changes to massive transportation datasets like national bottleneck statistics, passenger data for domestic flights, airline maintenance budgets, or even publication data from the Transportation Research Record...
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Analyzing important changes to massive transportation datasets like national bottleneck statistics, passenger data for domestic flights, airline maintenance budgets, or even publication data from the Transportation Research Record can be extremely complex. These types of datasets are often grouped by attributes in a tree structure hierarchy. The parent-child relationships of these hierarchical datasets allow for unique analytical opportunities, including the ability to track changes in the dataset at different levels of granularity, over time or between versions. For example, analysts can use hierarchies to uncover changes in the patterns of passengers flying in the United States over the last ten years, breaking down the data by states, cities, airports, and number of passengers. Exploring changes in travel patterns over time can help carriers make better decisions regarding their operations and long-range planning. This paper describes TreeVersity2, a web-based data comparison tool that provides users with information visualization techniques to find what has changed in a dataset over time. TreeVersity2 enables users to explore data that can be inherently hierarchical or not (by categorizing them by their attributes). An interactive textual reporting tool complements the visual exploration when the amount of data is very large. The results of two case studies conducted with transportation domain experts along with the results of an exit questionnaire are also described. TreeVersity2 preloaded with several demo datasets can be found at along with several example videos.
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Visualization, a vibrant field for researchers, practitioners, and higher educational institutions, is growing and evolving very rapidly. Tremendous progress has been made since 1987, the year often cited as the beginning of data ...
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Visualization, a vibrant field for researchers, practitioners, and higher educational institutions, is growing and evolving very rapidly. Tremendous progress has been made since 1987, the year often cited as the beginning of data visualization as a distinct field. As such, the number of visualization resources and the demand for those resources is increasing at a rapid pace. After a decades-equivalent long search process, we present a survey of open visualization resources for all those with an interest in interactive data visualization and visual analytics. Because the number of resources is so large, we focus on collections of resources, of which there are already many ranging from literature collections to collections of practitioner resources. Based on this, we develop a classification of visualization resource collections with a focus on the resource type, e.g. literature-based, web-based, developer focused and special topics. The result is an overview and details-on-demand of many useful resources. The collection offers a valuable jump-start for those seeking out data visualization resources from all backgrounds spanning from beginners such as students to teachers, practitioners, developers, and researchers wishing to create their own advanced or novel visual designs. This paper is a response to students and others who frequently ask for visualization resources available to them.
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Multi‐modal data of the complex human anatomy contain a wealth of information. To visualize and explore such data, techniques for emphasizing important structures and controlling visibility are essential.
Multi‐modal data of the complex human anatomy contain a wealth of information. To visualize and explore such data, techniques for emphasizing important structures and controlling visibility are essential. Such fused overview visualizations guide physicians to suspicious regions to be analysed in detail, e.g. with slice‐based viewing. We give an overview of state of the art in multi‐modal medical data visualization techniques. Multi‐modal medical data consist of multiple scans of the same subject using various acquisition methods, often combining multiple complimentary types of information. Three‐dimensional visualization techniques for multi‐modal medical data can be used in diagnosis, treatment planning, doctor–patient communication as well as interdisciplinary communication. Over the years, multiple techniques have been developed in order to cope with the various associated challenges and present the relevant information from multiple sources in an insightful way. We present an overview of these techniques and analyse the specific challenges that arise in multi‐modal data visualization and how recent works aimed to solve these, often using smart visibility techniques. We provide a taxonomy of these multi‐modal visualization applications based on the modalities used and the visualization techniques employed. Additionally, we identify unsolved problems as potential future research directions.
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Abstract Modelling the dynamics of a growing financial environment is a complex task that requires domain knowledge, expertise and access to heterogeneous information types. Such information can stem from several sources at differ...
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Abstract Modelling the dynamics of a growing financial environment is a complex task that requires domain knowledge, expertise and access to heterogeneous information types. Such information can stem from several sources at different scales, complicating the task of forming a holistic impression of the financial landscape, especially in terms of the economical relationships between firms. Bringing this scattered information into a common context is, therefore, an essential step in the process of obtaining meaningful insights about the state of an economy. In this paper, we present Sabrina?2.0, a Visual Analytics (VA) approach for exploring financial data across different scales, from individual firms up to nation‐wide aggregate data. Our solution is coupled with a pipeline for the generation of firm‐to‐firm financial transaction networks, fusing information about individual firms with sector‐to‐sector transaction data and domain knowledge on macroscopic aspects of the economy. Each network can be created to have multiple instances to compare different scenarios. We collaborated with experts from finance and economy during the development of our VA solution, and evaluated our approach with seven domain experts across industry and academia through a qualitative insight‐based evaluation. The analysis shows how Sabrina?2.0 enables the generation of insights, and how the incorporation of transaction models assists users in their exploration of a national economy.
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Visualization is an interdisciplinary imaging technique devoted to make the invisible visible by the techniques of experimental and computer-aided visualizations. It is applicable to various phenomena such as flow, heat, sound, el...
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Visualization is an interdisciplinary imaging technique devoted to make the invisible visible by the techniques of experimental and computer-aided visualizations. It is applicable to various phenomena such as flow, heat, sound, electromagnetism, chemical kinetics or any combination of these fields. This report describes the process of development, the present state and fruit of application, and the expectation of future development of visualization using examples. These examples expand to a wide range of fields including engineering, physics, medical science, agriculture, oceanography, meteorology and sports science.
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In this paper, we present a novel method for the direct volume rendering of large smoothed-particle hydrodynamics (SPH) simulation data without transforming the unstructured data to an intermediate representation. By directly visu...
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In this paper, we present a novel method for the direct volume rendering of large smoothed-particle hydrodynamics (SPH) simulation data without transforming the unstructured data to an intermediate representation. By directly visualizing the unstructured particle data, we avoid long preprocessing times and large storage requirements. This enables the visualization of large, time-dependent, and multivariate data both as a post-process and in situ. To address the computational complexity, we introduce stochastic volume rendering that considers only a subset of particles at each step during ray marching. The sample probabilities for selecting this subset at each step are thereby determined both in a view-dependent manner and based on the spatial complexity of the data. Our stochastic volume rendering enables us to scale continuously from a fast, interactive preview to a more accurate volume rendering at higher cost. Lastly, we discuss the visualization of free-surface and multi-phase flows by including a multi-material model with volumetric and surface shading into the stochastic volume rendering.
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