28,263 research outputs found
A Framework for XML-based Integration of Data, Visualization and Analysis in a Biomedical Domain
Biomedical data are becoming increasingly complex and heterogeneous in nature. The data are stored in distributed information systems, using a variety of data models, and are processed by increasingly more complex tools that analyze and visualize them. We present in this paper our framework for integrating biomedical research data and tools into a unique Web front end. Our framework is applied to the University of Washington’s Human Brain Project. Specifically, we present solutions to four integration tasks: definition of complex mappings from relational sources to XML, distributed XQuery processing, generation of heterogeneous output formats, and the integration of heterogeneous data visualization and analysis tools
Data Changes Everything: Challenges and Opportunities in Data Visualization Design Handoff
Complex data visualization design projects often entail collaboration between
people with different visualization-related skills. For example, many teams
include both designers who create new visualization designs and developers who
implement the resulting visualization software. We identify gaps between data
characterization tools, visualization design tools, and development platforms
that pose challenges for designer-developer teams working to create new data
visualizations. While it is common for commercial interaction design tools to
support collaboration between designers and developers, creating data
visualizations poses several unique challenges that are not supported by
current tools. In particular, visualization designers must characterize and
build an understanding of the underlying data, then specify layouts, data
encodings, and other data-driven parameters that will be robust across many
different data values. In larger teams, designers must also clearly communicate
these mappings and their dependencies to developers, clients, and other
collaborators. We report observations and reflections from five large
multidisciplinary visualization design projects and highlight six data-specific
visualization challenges for design specification and handoff. These challenges
include adapting to changing data, anticipating edge cases in data,
understanding technical challenges, articulating data-dependent interactions,
communicating data mappings, and preserving the integrity of data mappings
across iterations. Based on these observations, we identify opportunities for
future tools for prototyping, testing, and communicating data-driven designs,
which might contribute to more successful and collaborative data visualization
design.Comment: 11 pages, 11 figures. To appear in IEEE Transactions on Visualization
and Computer Graphics. To be presented at the IEEE VIS 2019 Conferenc
Bottom-up vs. top-down : trade-offs in efficiency, understanding, freedom and creativity with InfoVis tools
The emergence of tools that support fast-and-easy visualization creation by non-experts has made the benefits of InfoVis widely accessible. Key features of these tools include attribute-level operations, automated mappings, and visualization templates. However, these features shield people from lower-level visualization design steps, such as the specific mapping of data points to visuals. In contrast, recent research promotes constructive visualization where individual data units and visuals are directly manipulated. We present a qualitative study comparing people's visualization processes using two visualization tools: one promoting a top-down approach to visualization construction (Tableau Desktop) and one implementing a bottom-up constructive visualization approach (iVoLVER). Our results show how the two approaches influence: 1) the visualization process, 2) decisions on the visualization design, 3) the feeling of control and authorship, and 4) the willingness to explore alternative designs. We discuss the complex trade-offs between the two approaches and outline considerations for designing better visualization tools.Postprin
Dimensionality Reduction Mappings
A wealth of powerful dimensionality reduction methods has been established which can be used for data visualization and preprocessing. These are accompanied by formal evaluation schemes, which allow a quantitative evaluation along general principles and which even lead to further visualization schemes based on these objectives. Most methods, however, provide a mapping of a priorly given finite set of points only, requiring additional steps for out-of-sample extensions. We propose a general view on dimensionality reduction based on the concept of cost functions, and, based on this general principle, extend dimensionality reduction to explicit mappings of the data manifold. This offers simple out-of-sample extensions. Further, it opens a way towards a theory of data visualization taking the perspective of its generalization ability to new data points. We demonstrate the approach based on a simple global linear mapping as well as prototype-based local linear mappings.
Conjugate Function Method for Numerical Conformal Mappings
We present a method for numerical computation of conformal mappings from
simply or doubly connected domains onto so-called canonical domains, which in
our case are rectangles or annuli. The method is based on conjugate harmonic
functions and properties of quadrilaterals. Several numerical examples are
given.Comment: 23 pages, 15 figures, 5 table
Understanding Visualization: A formal approach using category theory and semiotics
This article combines the vocabulary of semiotics and category theory to provide a formal analysis of visualization. It shows how familiar processes of visualization fit the semiotic frameworks of both Saussure and Peirce, and extends these structures using the tools of category theory to provide a general framework for understanding visualization in practice, including: relationships between systems, data collected from those systems, renderings of those data in the form of representations, the reading of those representations to create visualizations, and the use of those visualizations to create knowledge and understanding of the system under inspection. The resulting framework is validated by demonstrating how familiar information visualization concepts (such as literalness, sensitivity, redundancy, ambiguity, generalizability, and chart junk) arise naturally from it and can be defined formally and precisely. This article generalizes previous work on the formal characterization of visualization by, inter alia, Ziemkiewicz and Kosara and allows us to formally distinguish properties of the visualization process that previous work does not
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