Special Issue "Information Visualization Theory and Applications (IVAPP 2019)"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: closed (15 July 2019).

Special Issue Editors

Prof. Dr. Andreas Kerren
E-Mail Website
Guest Editor
Department of Computer Science, Linnaeus University, Vaxjo, Sweden
Interests: information visualization; visualizations in bioinformatics; visualization of geographical data; visual analytics; software visualization; human-computer interaction
Special Issues and Collections in MDPI journals
Prof. Dr. Christophe Hurter
E-Mail Website
Guest Editor
Interactive Data Visualization group, the French Civil Aviation University (ENAC), Toulouse, France
Interests: information visualization; human-computer interaction
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue intends to contain a selection of carefully revised and extended best papers to be presented at the 10th International Conference on Information Visualization Theory and Applications (IVAPP 2019), to be held in Prague, Czech Republic, 25–27 February, 2019. IVAPP aims at becoming a major point of contact between researchers, engineers and practitioners in Information Visualization. The conference covers a broad range of topics related to Information Visualization indicated by the topic list below. Papers describing advanced prototypes, systems, tools and techniques as well as general survey papers indicating future directions are also encouraged.

Selected papers which were presented at the conference are invited to be submitted as extended versions to this Special Issue of the journal Information after the conference.

The conference paper should be cited and noted on the first page of the paper; authors are asked to disclose that it is a conference paper in their cover letter and include a statement on what has been changed compared to the original conference paper. Each submission to this journal issue should contain at least 50% of new material, e.g., in the form of technical extensions, more in-depth evaluations, or additional use cases. All submitted papers will undergo our standard peer-review procedure. Accepted papers will be published in open access format in Information and collected together on this Special Issue website.

Prof. Dr. Andreas Kerren
Prof. Dr. Christophe Hurter
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

Open AccessArticle
Constructing and Visualizing High-Quality Classifier Decision Boundary Maps
Information 2019, 10(9), 280; https://doi.org/10.3390/info10090280 - 09 Sep 2019
Cited by 2 | Viewed by 1219
Abstract
Visualizing decision boundaries of machine learning classifiers can help in classifier design, testing and fine-tuning. Decision maps are visualization techniques that overcome the key sparsity-related limitation of scatterplots for this task. To increase the trustworthiness of decision map use, we perform an extensive [...] Read more.
Visualizing decision boundaries of machine learning classifiers can help in classifier design, testing and fine-tuning. Decision maps are visualization techniques that overcome the key sparsity-related limitation of scatterplots for this task. To increase the trustworthiness of decision map use, we perform an extensive evaluation considering the dimensionality-reduction (DR) projection techniques underlying decision map construction. We extend the visual accuracy of decision maps by proposing additional techniques to suppress errors caused by projection distortions. Additionally, we propose ways to estimate and visually encode the distance-to-decision-boundary in decision maps, thereby enriching the conveyed information. We demonstrate our improvements and the insights that decision maps convey on several real-world datasets. Full article
(This article belongs to the Special Issue Information Visualization Theory and Applications (IVAPP 2019))
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Open AccessArticle
Enhanced Grid-Based Visual Analysis of Retinal Layer Thickness with Optical Coherence Tomography
Information 2019, 10(9), 266; https://doi.org/10.3390/info10090266 - 23 Aug 2019
Cited by 1 | Viewed by 1316
Abstract
Optical coherence tomography enables high-resolution 3D imaging of retinal layers in the human eye. The thickness of the layers is commonly assessed to understand a variety of retinal and systemic disorders. Yet, the thickness data are complex and currently need to be considerably [...] Read more.
Optical coherence tomography enables high-resolution 3D imaging of retinal layers in the human eye. The thickness of the layers is commonly assessed to understand a variety of retinal and systemic disorders. Yet, the thickness data are complex and currently need to be considerably reduced prior to further processing and analysis. This leads to a loss of information on localized variations in thickness, which is important for early detection of certain retinal diseases. We propose an enhanced grid-based reduction and exploration of retinal thickness data. Alternative grids are computed, their representation quality is rated, and best fitting grids for given thickness data are suggested. Selected grids are then visualized, adapted, and compared at different levels of granularity. A visual analysis tool bundles all computational, visual, and interactive means in a flexible user interface. We demonstrate the utility of our tool in a complementary analysis procedure, which eases the evaluation of ophthalmic study data. Ophthalmologists successfully applied our solution to study localized variations in thickness of retinal layers in patients with diabetes mellitus. Full article
(This article belongs to the Special Issue Information Visualization Theory and Applications (IVAPP 2019))
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