EuroVA15

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/12462

Browse

Browsing EuroVA15 by Subject "H.5.2 [Information Interfaces and Presentation]"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    Inspector Gadget: Integrating Data Preprocessing and Orchestration in the Visual Analysis Loop
    (The Eurographics Association, 2015) Krüger, Robert; Herr, Dominik; Haag, Florian; Ertl, Thomas; E. Bertini and J. C. Roberts
    Nowadays, tracking devices are small and cheap. For analysis tasks, there is no problem to obtain sufficient amounts of data. The challenge is how to make sense of the data, which often contain complex situations. Multiple data sources related to time, space, and other dimensions, with different resolution and notation have to be mapped. Visual approaches often cover an analysis loop that starts right after the preprocessing. In this paper, we contribute methods to explicitly integrate data preprocessing and orchestration into the visual analysis loop. Subsequently, the big picture can be explored in detail and hypotheses can be created, refined, and validated. We showcase our approach with multiple heterogeneous datasets from the VAST Challenge 2014.
  • Thumbnail Image
    Item
    Supporting Historical Research Through User-Centered Visual Analytics
    (The Eurographics Association, 2015) Boukhelifa, Nadia; Giannisakis, Emmanouil; Dimara, Evanthia; Willett, Wesley; Fekete, Jean-Daniel; E. Bertini and J. C. Roberts
    In this paper we describe the development and evaluation of a visual analytics tool to support historical research. Historians continuously gather data related to their scholarly research from archival visits and background search. Organising and making sense of all this data can be challenging as many historians continue to rely on analog or basic digital tools. We built an integrated note-taking environment for historians which unifies a set of functionalities we identified as important for historical research including editing, tagging, searching, sharing and visualization. Our approach was to involve users from the initial stage of brainstorming and requirement analysis through to design, implementation and evaluation. We report on the process and results of our work, and conclude by reflecting on our own experience in conducting user-centered visual analytics design for digital humanities.
  • Thumbnail Image
    Item
    Visual Analysis of Relations in Attributed Time-Series Data
    (The Eurographics Association, 2015) Steiger, Martin; Bernard, Jürgen; Schader, Philipp; Kohlhammer, Jörn; E. Bertini and J. C. Roberts
    In this paper, we present visual-interactive techniques for revealing relations between two co-existing multivariate feature spaces. Such data is generated, for example, by sensor networks characterized by a set of (categorical) attributes which continuously measure physical quantities over time. A challenging analysis task is the seeking for interesting relations between the time-oriented data and the sensor attributes. Our approach uses visualinteractive analysis to enable analysts to identify correlations between similar time series and similar attributes of the data. It is based on a combination of machine-based encoding of this information in position and color and the human ability to recognize cohesive structures and patterns. In our figures, we illustrate how analysts can identify similarities and anomalies between time series and categorical attributes of metering devices and sensors.