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International Journal for Uncertainty Quantification

Publication de 6  numéros par an

ISSN Imprimer: 2152-5080

ISSN En ligne: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

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PREDICTABILITY-BASED ADAPTIVE MOUSE INTERACTION AND ZOOMING FOR VISUAL FLOW EXPLORATION

Volume 3, Numéro 3, 2013, pp. 225-240
DOI: 10.1615/Int.J.UncertaintyQuantification.2012003943
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RÉSUMÉ

Flow fields are often investigated by adopting a Lagrangian view, for example, by particle tracing of integral curves such as streamlines and path lines or by computing delocalized quantities. For visual exploration, mouse interaction is predominantly used to define starting points for time-dependent Lagrangian methods. This paper focuses on the uncertainty of mouse input and its impact on the visualization process. In typical cases, the interaction is achieved by mouse motion, exhibiting uncertainty in the range of a screen pixel. From the perspective of dynamical systems theory, an integral curve represents an initial value problem, the uncertainty a perturbation of its initial condition, and the uncertainty of the visualization procedure a predictability problem. Predictability analysis is concerned with the growth of perturbations under the action of flow. In our case, it is not unusual that the perturbations grow from single pixels to substantial deviations. We therefore present an interaction scheme based on the largest finite-time Lyapunov exponent and the flow map gradient, providing accurate, smooth, and easy-to-use flow exploration. This scheme employs data-driven adaptation of mouse speed and direction as well as optional augmentation by an adaptive zoom lens with consistent magnification. We compare our approach to nonadaptive mouse interaction and demonstrate it for several examples of data sets. Furthermore, we present results from a user study with nine domain experts.

CITÉ PAR
  1. Karch Grzegorz K., Sadlo Filip, Weiskopf Daniel, Ertl Thomas, Visualization of 2D unsteady flow using streamline-based concepts in space-time, Journal of Visualization, 19, 1, 2016. Crossref

  2. Goebel Fabian, Kurzhals Kuno, Schinazi Victor R., Kiefer Peter, Raubal Martin, Gaze-Adaptive Lenses for Feature-Rich Information Spaces, ACM Symposium on Eye Tracking Research and Applications, 2020. Crossref

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