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Selected Papers from IVAPP 2018
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Selected Papers from IVAPP 2018

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

Deadline for manuscript submissions: closed (1 May 2018) | Viewed by 19157

Special Issue Editors

Prof. Alexandru Telea

E-Mail Website
Guest Editor
Department of Mathematics and Computing Science, University of Groningen, The Netherlands
Interests: scientific visualization; information visualization; multiscale visualization; interactive systems design; multivariate visualization; computer imaging; object-oriented and component-oriented software design; static source code analysis
Special Issues, Collections and Topics in MDPI journals
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, Collections and Topics 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 9th International Conference on Information Visualization Theory and Applications (IVAPP 2018), to be held at Funchal, Madeira, Portugal, 27–29 January, 2018. IVAPP aims at becoming a major point of contact for 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. The paper acceptance for IVAPP 2018 will be based on quality, relevance to the conference theme and originality.

The authors of a number of selected full papers of high quality will be invited after the conference to submit revised and extended versions of their originally accepted conference papers to this Special Issue of Information published by MDPI in open access. The selection of these best papers will be based on their ratings in the conference review process, quality of the presentation during the conference, and expected impact to the research community. 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. These extended submissions will undergo a peer-review process according to the journal’s rules of action as described in the Manuscript Submission Information given below. At least two members of the IVAPP 2018 program committee will act as reviewers for each extended article submitted to this Special Issue; if needed additional external reviewers will be invited to guarantee the high quality of the reviewing process.

The preliminary deadline for submission to this Special Issue is 1 May, 2018.

The scope and topics of interest of the Special Issue papers follow those of IVAPP 2018 and are listed below.

TOPIC 1: ABSTRACT DATA VISUALIZATION

●        Visual Data Analysis and Knowledge Discovery
●        Visual Representation and Interaction
●        Data Management and Knowledge Representation
●        Mathematical Foundations of Interactive Visual Analysis
●        Display and Interaction Technology
●        Databases and visualization, Visual Data Mining
●        Graph Visualization
●        Interface and Interaction Techniques for Visualization
●        Internet, Web and Security Visualization
●        Software Visualization
●        Information Visualization
●        Visual Analytical Reasoning
●        Hardware-Assisted Visualization
●        High-dimensional Data and Dimensionality Reduction
●        Text and Document Visualization

TOPIC 2: GENERAL DATA VISUALIZATION

●      Interactive Visual Interfaces for Visualization
●      Interpretation and Evaluation Methods
●      Knowledge-assisted Visualization
●      Large Data Visualization
●      Perception and Cognition in Visualization
●      Visualization Applications
●      Visualization Taxonomies and Models
●      Visualization Algorithms and Technologies
●      Visualization Tools and Systems for Simulation and Modeling
●      Time-dependent Visualization
●      Usability Studies and Visualization
●      Glyph-based Visualization
●      Collaborative Visualization
●      Coordinated and Multiple Views

TOPIC 3: SPATIAL DATA VISUALIZATION

●      Biomedical Visualization and Applications
●      Flow Visualization
●      GPU-based Visualization
●      Image/Video Summarization and Visualization
●      Multi-field Visualization
●      Parallel Visualization
●      Uncertainty Visualization
●      Vector/Tensor Field Visualization
●      Virtual Environments and Data Visualization
●      Volume Visualization
●      Scientific Visualization

Prof. Alexandru Telea
Prof. Andreas Kerren
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 submissions that pass pre-check are 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 1600 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 (5 papers)

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Editorial

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2 pages, 143 KiB  
Editorial
Special Issue on Selected Papers from IVAPP 2018
by Alexandru Telea and Andreas Kerren
Information 2018, 9(7), 171; https://doi.org/10.3390/info9070171 - 13 Jul 2018
Viewed by 2460
Abstract
Recent developments at the crossroads of data science, datamining,machine learning, and graphics and imaging sciences have further established information visualization and visual analytics as central disciplines that deliver methods, techniques, and tools for making sense of and extracting actionable insights and results fromlarge [...] Read more.
Recent developments at the crossroads of data science, datamining,machine learning, and graphics and imaging sciences have further established information visualization and visual analytics as central disciplines that deliver methods, techniques, and tools for making sense of and extracting actionable insights and results fromlarge amounts of complex,multidimensional, hybrid, and time-dependent data.[...] Full article
(This article belongs to the Special Issue Selected Papers from IVAPP 2018)

Research

Jump to: Editorial

24 pages, 5493 KiB  
Article
A Top-Down Interactive Visual Analysis Approach for Physical Simulation Ensembles at Different Aggregation Levels
by Alexey Fofonov and Lars Linsen
Information 2018, 9(7), 163; https://doi.org/10.3390/info9070163 - 03 Jul 2018
Cited by 2 | Viewed by 3855
Abstract
Physical simulations aim at modeling and computing spatio-temporal phenomena. As the simulations depend on initial conditions and/or parameter settings whose impact is to be investigated, a larger number of simulation runs is commonly executed. Analyzing all facets of such multi-run multi-field spatio-temporal simulation [...] Read more.
Physical simulations aim at modeling and computing spatio-temporal phenomena. As the simulations depend on initial conditions and/or parameter settings whose impact is to be investigated, a larger number of simulation runs is commonly executed. Analyzing all facets of such multi-run multi-field spatio-temporal simulation data poses a challenge for visualization. It requires the design of different visual encodings that aggregate information in multiple ways and at multiple abstraction levels. We present a top-down interactive visual analysis tool of multi-run data from physical simulations named MultiVisA that is based on plots at different aggregation levels. The most aggregated visual representation is a histogram-based plot that allows for the investigation of the distribution of function values within all simulation runs. When expanding over time, a density-based time-series plot allows for the detection of temporal patterns and outliers within the ensemble of multiple runs for single and multiple fields. Finally, not aggregating over runs in a similarity-based plot allows for the comparison of multiple or individual runs and their behavior over time. Coordinated views allow for linking the plots of the three aggregation levels to spatial visualizations in physical space. We apply MultiVisA to physical simulations from the field of climate research and astrophysics. We document the analysis process, demonstrate its effectiveness, and provide evaluations involving domain experts. Full article
(This article belongs to the Special Issue Selected Papers from IVAPP 2018)
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18 pages, 1468 KiB  
Article
Upsampling for Improved Multidimensional Attribute Space Clustering of Multifield Data
by Vladimir Molchanov and Lars Linsen
Information 2018, 9(7), 156; https://doi.org/10.3390/info9070156 - 27 Jun 2018
Cited by 2 | Viewed by 3988
Abstract
Clustering algorithms in the high-dimensional space require many data to perform reliably and robustly. For multivariate volume data, it is possible to interpolate between the data points in the high-dimensional attribute space based on their spatial relationship in the volumetric domain (or physical [...] Read more.
Clustering algorithms in the high-dimensional space require many data to perform reliably and robustly. For multivariate volume data, it is possible to interpolate between the data points in the high-dimensional attribute space based on their spatial relationship in the volumetric domain (or physical space). Thus, sufficiently high number of data points can be generated, overcoming the curse of dimensionality for this particular type of multidimensional data. We applies this idea to a histogram-based clustering algorithm. We created a uniform partition of the attribute space in multidimensional bins and computed a histogram indicating the number of data samples belonging to each bin. Without interpolation, the analysis was highly sensitive to the histogram cell sizes, yielding inaccurate clustering for improper choices: Large histogram cells result in no cluster separation, while clusters fall apart for small cells. Using an interpolation in physical space, we could refine the data by generating additional samples. The depth of the refinement scheme was chosen according to the local data point distribution in attribute space and the histogram’s bin size. In the case of field discontinuities representing sharp material boundaries in the volume data, the interpolation can be adapted to locally make use of a nearest-neighbor interpolation scheme that avoids averaging values across the sharp boundary. Consequently, we could generate a density computation, where clusters stay connected even when using very small bin sizes. We exploited this result to create a robust hierarchical cluster tree, apply our technique to several datasets, and compare the cluster trees before and after interpolation. Full article
(This article belongs to the Special Issue Selected Papers from IVAPP 2018)
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27 pages, 15815 KiB  
Article
An Evolutionary Algorithm for an Optimization Model of Edge Bundling
by Joelma De M. Ferreira, Hugo A. D. Do Nascimento and Les R. Foulds
Information 2018, 9(7), 154; https://doi.org/10.3390/info9070154 - 26 Jun 2018
Cited by 5 | Viewed by 4168
Abstract
This paper discusses three edge bundling optimization problems that aim to minimize the total number of bundles of a graph drawing, in conjunction with other aspects, as the main goal. A novel evolutionary algorithm for edge bundling for these problems is described. The [...] Read more.
This paper discusses three edge bundling optimization problems that aim to minimize the total number of bundles of a graph drawing, in conjunction with other aspects, as the main goal. A novel evolutionary algorithm for edge bundling for these problems is described. The algorithm was successfully tested by solving the related problems applied to real-world instances in reasonable computational time. The development and analysis of optimization models have received little attention in the area of edge bundling. However, the reported experimental results demonstrate the effectiveness and the applicability of the proposed evolutionary algorithm to help resolve edge bundling problems by formally defining them as optimization models. Full article
(This article belongs to the Special Issue Selected Papers from IVAPP 2018)
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24 pages, 2334 KiB  
Article
More Compact Orthogonal Drawings by Allowing Additional Bends †
by Michael Jünger, Petra Mutzel and Christiane Spisla
Information 2018, 9(7), 153; https://doi.org/10.3390/info9070153 - 26 Jun 2018
Cited by 3 | Viewed by 4197
Abstract
Compacting orthogonal drawings is a challenging task. Usually, algorithms try to compute drawings with small area or total edge length while preserving the underlying orthogonal shape. We suggest a moderate relaxation of the orthogonal compaction problem, namely the one-dimensional monotone flexible edge compaction [...] Read more.
Compacting orthogonal drawings is a challenging task. Usually, algorithms try to compute drawings with small area or total edge length while preserving the underlying orthogonal shape. We suggest a moderate relaxation of the orthogonal compaction problem, namely the one-dimensional monotone flexible edge compaction problem with fixed vertex star geometry. We further show that this problem can be solved in polynomial time using a network flow model. An experimental evaluation shows that by allowing additional bends could reduce the total edge length and the drawing area. Full article
(This article belongs to the Special Issue Selected Papers from IVAPP 2018)
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