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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. Computer Graphics Forum, 41 (1) ISSN:1467-8659 ISSN:0167-7055

Flow visualization techniques are vastly used to visualize scientific data among many fields including meteorology, computational fluid dynamics, medical visualization and aerodynamics. In this paper, we employ flow visualization techniques in conjunction with conventional network visualization methods to represent geographic network traffic data. The proposed visualization system integrates two visualization techniques, flow visualization and node-link diagram. While flow visualization emphasizes on general trends, node-link diagram visualization concentrates on the detailed analysis of the data. A usability study with multiple experiments is performed to evaluate the success of our approach.

A new design process for the development of parallel performance visualizations that uses existing scientific data visualization software to prototype new performance visualizations can lead to drastic reductions in the graphics and data manipulation programming overhead currently experienced by performance visualization developers. The process evolves from a formal methodology relating performance abstractions to visual representations under which performance visualizations are described as mappings from performance objects to view objects, independent of any graphical programming. This prototyping environment also facilitates iterative design and evaluation of new and existing displays. Our work examines how an existing data visualization tool can provide a robust prototyping environment for next-generation parallel performance visualizations.

Information visualization encounters a wide variety of different data domains. The visualization community has developed representation methods and interactive techniques. As a community, we have realized that the requirements in each domain are often dramatically different. In order to easily apply existing methods, researchers have developed a semiology of graphic representations. We have extended this research into a framework that includes operators and interactions in visualization systems, such as a visualization spreadsheet. We discuss properties of this framework and use it to characterize operations spanning a variety of different visualization techniques. The framework developed in the paper enables a new way of exploring and evaluating the design space of visualization operators, and helps end users in their analysis tasks.

Understanding the performance of program execution is essential when optimizing simulations run on high-performance supercomputers. Instrumenting and profiling codes is itself a difficult task and interpreting the resulting complex data is often facilitated through visualization of the gathered measures. However, these measures typically ignore spatial information specific to a simulation, which may contain useful knowledge on program behavior. Linking the instrumentation data to the visualization of performance within a spatial context is not straightforward as information needed to create the visualizations is not, by default, included in data collection, and the typical visualization approaches do not address spatial concerns. In this work, we present an approach that links the collection of spatially-aware performance data to a visualization paradigm through both analysis and visualization abstractions to facilitate better understanding of performance in the spatial context of the simulation. Because the potential costs for such a system are quite high, we leverage existing performance profiling and visualization systems and demonstrate their combined potential on climate simulation.

Data visualization is an application-driven field, that is always trying to satisfy its customers and to adapt to the demands, cultures, and workflows of many application areas. Therefore, it is difficult to keep focus on techniques and approaches that are not too application specific. A lot of good work on data visualization consists of single-problem solutions, that cannot be easily merged into general-purpose systems. In this talk, I will briefly review some current trends and issues, and identify some approaches that are common to many applications. One such approach in data visualization that has attracted interest from the early days is the detection of salient features, or patterns of interest in a data set. The main idea is to extract information at a higher level of abstraction from a mass of data, that is richer in semantics but much smaller in size, and that can help to define scenes and objects for visualization. This idea was pioneered in areas such as flow visualization, but is now more widely applied. It is often considered to be necessity to keep up with the ever rapidly increasing size of data sets, and the demand for interactivity in data visualization and analysis. Another generic approach in data visualization is called interactive visual analysis (TVA), consisting of a strongly interactive multiple-linked-view interfaces with integrated, powerful data analysis techniques taken from statistical analysis, pattern recognition, machine learning, and other fields. This is built on the assumptions that a single 2D or 3D visualization is often not enough, and spatial views can be augmented with abstract, derived data spaces; that strong interaction helps to promote insight; and that a better balance is needed between human visual inspection and computer-based analysis and reasoning. Interestingly, an IVA interface can serve not only as an environment for exploration of low-level data, but also for defining the high-level features to be extracted, that should summarize the essence of the data. The high-level features are usually highly application specific, and can only be found using theories from the application domains. The big challenge is to create environments for general purpose visual data analysis, and yet allow users to introduce advanced theories and methods from many application domains. The trend towards more integration in data visualization will be illustrated with cross-links between very different areas, such as medical and flow visualization, and the combined use of techniques from scientific visualization and information visualization, and the absorption of other data analysis techniques. Also, historic and contemporary examples of feature extraction and interactive visual analysis will be shown.

The scientific data visualization process involves a sequence of transformations that convert a data set into a displayable image. One of the most important transformations in this process is the visualization mapping which defines a set of bindings between data and graphical primitives. Since these bindings describe how data is going to be visualized, the effectiveness of visualization critically depends on the mapping defined at this stage. Establishing a proper mapping which leads to an effective data visualization requires significant knowledge in several fields, such as, data management, computer graphics, and visual perception. However, scientists who could benefit most from data visualization usually lack this knowledge. In order to identify, acquire, formalize, and provide this knowledge, the existing visualization techniques, that are known to be useful, have been thoroughly analyzed. The analysis shows that most of the existing data visualization techniques can be described in terms of attributes of data, a set of primitive visualization techniques, marks (graphical symbols) that modify primitive visualization techniques, and a set of rules used in their design. The analysis further suggests a design process leading to the automatic synthesis of scientific data visualization techniques.

Human Bio-Behavioral Rhythms (HBRs) such as sleep-wake cycles and their regularity have important health ramifications. Smartphones can sense HBRs by gathering and analyzing data from built-in sensors, which provide behavioral clues. The multichannel nature (multiple sensor streams) of such data makes it challenging to pin-point the causes of disruptions in HBRs. Prior work has utilized machine learning for HBR classification but has not facilitated deeper understanding or reasoning about the potential disruption causes. In this paper, we propose ARGUS, an interactive visual analytics framework to discover and understand HBR disruptions and causes. The foundation of ARGUS is a Rhythm Deviation Score (RDS) that extracts a user's underlying 24-hour rhythm from their smartphone sensor data and quantifies its irregularity. ARGUS then visualizes the RDS using a glyph to easily recognize disruptions in HBRs, along with multiple linked panes that overlay sensor information and user-provided or smartphone-inferred ground truth as supporting context. This framework visually captures a comprehensive picture of HBRs and their disruptions. ARGUS was designed by an expert lead goal-and-task analysis. To demonstrate its generalizability, two different smartphone-sensed datasets were visualized using ARGUS in conjunction with expert feedback. CCS Concepts: Visualization --> Visualization systems and tools; Visualization application domains --> Visual analytics Hamid Mansoor, Walter Gerych, Luke Buquicchio, Abdulaziz Alajaji, Kavin Chandrasekaran, Emmanuel Agu, and Elke Rundensteiner EuroVis 2020 - Short Papers Analytics and Evaluation 25 29

This video proposes readability visualization, genre visualization, and combined visualization to provide unconventional information for book selection. Data visualization was initiated for the practical purpose of delivering information, as it efficiently links visual perception and data so that readers are able to instantly recognize patterns in overcrowded data. In this interdisciplinary research we used the strength of data visualization, and this paper suggests three possible textual visualizations of a book, which may help users to find a desirable book, with the use of intuitive information out of a large volume of book data.