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A special issue of Informatics (ISSN 2227-9709).

Deadline for manuscript submissions: closed (31 May 2017) | Viewed by 116671

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Special Issue Editors

Prof. Dr. Achim Ebert

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Guest Editor

Computer Graphics and Human Computer Interaction Lab, University of Kaiserslautern, Gottlieb-Daimler-Str., 67663 Kaiserslautern, Germany
Interests: human–computer interaction; information visualization and applications
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Gunther H. Weber

E-Mail Website
Guest Editor

1. Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720, USA
2. University of California at Davis, One Shields Avenue, Davis, CA 95616, USA
Interests: data analysis; visualization; high performance computing

Special Issue Information

Dear Colleagues,

Data available in today’s information society is ever growing in size and complexity—i.e., unstructured, multidimensional, uncertain, etc.—making it impossible to survey and understand this data. Traditionally, most of these datasets are stored and depicted as huge tables, hindering efficient retrieval of salient information—similarities, outliers, structures, origin, etc. Interactive visualization provides an interface to this data that can help gleaning valuable information from it, thus supporting a better data understanding by significantly reducing cognitive load on the analyst. As the term implies, two concepts form the fundamental basis of the underlying scientific methods: visualization and interaction. Combining these concepts builds a bridge between two key research areas in computer science: visualization and human-computer interaction (HCI) and brings together practitioners from many disciplines. This results in highly multi-disciplinary work with significant impact.

Applications for interactive visualization are virtually unlimited and include:

Analysis of complex data sets (“big data”),
Virtual reality environments,
Augmented reality,
Mobile Environments,
Cooperative Work,
Computer-supported surgery,
Large-scale simulations,
Experimental and observational data, and
Sensor networks.

However, truly interactive visualizations are hard to design and implement. Researchers have to solve multiple problems, e.g.:

Transforming complexity into simplicity,
Efficient algorithms and implementations,
Guaranteeing real-time performance,
Scaling to multiple platforms and user types,
Minimizing and managing data transfer, and
Efficient parallel implementations.

This Special Issue will provide an insight on the current state-of-the-art of “Interactive Visualization”. It will show recent works in the field, as well as trends for future development.

Prof. Dr. Achim Ebert
Prof. Dr. Gunther H. Weber
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. Informatics is an international peer-reviewed open access quarterly 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 1800 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.

Keywords

Interactive visualization
Mobile visualization
Collaborative visualization
Virtual Reality
Augmented Reality
Interactive websites
Visual analysis of time-critical data
Interaction methods for interactive systems
Scalability (e.g. usage on different devices)
Real-time rendering
Level-of-detail rendering
High-Performance Computing
Interactive visualization frameworks
Evaluation studies of interactive systems
Handling and visualization of Big Data / Smart Data
Challenges for scalable interactive visualization
Smart Software Ecosystems
Data management and movement
Data abstraction
Feature detection

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Published Papers (11 papers)

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Research

39827 KiB

Open AccessArticle

Scalable Interactive Visualization for Connectomics

by Daniel Haehn, John Hoffer, Brian Matejek, Adi Suissa-Peleg, Ali K. Al-Awami, Lee Kamentsky, Felix Gonda, Eagon Meng, William Zhang, Richard Schalek, Alyssa Wilson, Toufiq Parag, Johanna Beyer, Verena Kaynig, Thouis R. Jones, James Tompkin, Markus Hadwiger, Jeff W. Lichtman and Hanspeter Pfister

Informatics 2017, 4(3), 29; https://doi.org/10.3390/informatics4030029 - 28 Aug 2017

Cited by 22 | Viewed by 15957

Abstract

Connectomics has recently begun to image brain tissue at nanometer resolution, which produces petabytes of data. This data must be aligned, labeled, proofread, and formed into graphs, and each step of this process requires visualization for human verification. As such, we present the BUTTERFLY middleware, a scalable platform that can handle massive data for interactive visualization in connectomics. Our platform outputs image and geometry data suitable for hardware-accelerated rendering, and abstracts low-level data wrangling to enable faster development of new visualizations. We demonstrate scalability and extendability with a series of open source Web-based applications for every step of the typical connectomics workflow: data management and storage, informative queries, 2D and 3D visualizations, interactive editing, and graph-based analysis. We report design choices for all developed applications and describe typical scenarios of isolated and combined use in everyday connectomics research. In addition, we measure and optimize rendering throughput—from storage to display—in quantitative experiments. Finally, we share insights, experiences, and recommendations for creating an open source data management and interactive visualization platform for connectomics. Full article

(This article belongs to the Special Issue Scalable Interactive Visualization)

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5153 KiB

Open AccessArticle

Sampling and Estimation of Pairwise Similarity in Spatio-Temporal Data Based on Neural Networks

by Steffen Frey

Informatics 2017, 4(3), 27; https://doi.org/10.3390/informatics4030027 - 26 Aug 2017

Cited by 6 | Viewed by 8395

Abstract

Increasingly fast computing systems for simulations and high-accuracy measurement techniques drive the generation of time-dependent volumetric data sets with high resolution in both time and space. To gain insights from this spatio-temporal data, the computation and direct visualization of pairwise distances between time steps not only supports interactive user exploration, but also drives automatic analysis techniques like the generation of a meaningful static overview visualization, the identification of rare events, or the visual analysis of recurrent processes. However, the computation of pairwise differences between all time steps is prohibitively expensive for large-scale data not only due to the significant cost of computing expressive distance between high-resolution spatial data, but in particular owing to the large number of distance computations

(O (| T |^{2}))

, with

| T |

being the number of time steps). Addressing this issue, we present and evaluate different strategies for the progressive computation of similarity information in a time series, as well as an approach for estimating distance information that has not been determined so far. In particular, we investigate and analyze the utility of using neural networks for estimating pairwise distances. On this basis, our approach automatically determines the sampling strategy yielding the best result in combination with trained networks for estimation. We evaluate our approach with a variety of time-dependent 2D and 3D data from simulations and measurements as well as artificially generated data, and compare it against an alternative technique. Finally, we discuss prospects and limitations, and discuss different directions for improvement in future work. Full article

(This article belongs to the Special Issue Scalable Interactive Visualization)

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6498 KiB

Open AccessArticle

Multidimensional Data Exploration by Explicitly Controlled Animation

by Johannes F. Kruiger, Almoctar Hassoumi, Hans-Jörg Schulz, AlexandruC Telea and Christophe Hurter

Informatics 2017, 4(3), 26; https://doi.org/10.3390/informatics4030026 - 20 Aug 2017

Cited by 5 | Viewed by 11012

Abstract

Understanding large multidimensional datasets is one of the most challenging problems in visual data exploration. One key challenge that increases the size of the exploration space is the number of views that one can generate from a single dataset, based on the use of multiple parameter values and exploration paths. Often, no such single view contains all needed insights. The question thus arises of how we can efficiently combine insights from multiple views of a dataset. We propose a set of techniques that considerably reduce the exploration effort for such situations, based on the explicit depiction of the view space, using a small multiple metaphor. We leverage this view space by offering interactive techniques that enable users to explicitly create, visualize, and follow their exploration path. This way, partial insights obtained from each view can be efficiently and effectively combined. We demonstrate our approach by applications using real-world datasets from air traffic control, software maintenance, and machine learning. Full article

(This article belongs to the Special Issue Scalable Interactive Visualization)

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26005 KiB

Open AccessArticle

Visual Analysis of Stochastic Trajectory Ensembles in Organic Solar Cell Design

by Sathish Kottravel, Riccardo Volpi, Mathieu Linares, Timo Ropinski and Ingrid Hotz

Informatics 2017, 4(3), 25; https://doi.org/10.3390/informatics4030025 - 1 Aug 2017

Cited by 2 | Viewed by 8790

Abstract

We present a visualization system for analyzing stochastic particle trajectory ensembles, resulting from Kinetic Monte-Carlo simulations on charge transport in organic solar cells. The system supports the analysis of such trajectories in relation to complex material morphologies. It supports the inspection of individual trajectories or the entire ensemble on different levels of abstraction. Characteristic measures quantify the efficiency of the charge transport. Hence, our system led to better understanding of ensemble trajectories by: (i) Capturing individual trajectory behavior and providing an ensemble overview; (ii) Enabling exploration through linked interaction between 3D representations and plots of characteristics measures; (iii) Discovering potential traps in the material morphology; (iv) Studying preferential paths. The visualization system became a central part of the research process. As such, it continuously develops further along with the development of new hypothesis and questions from the application. Findings derived from the first visualizations, e.g., new efficiency measures, became new features of the system. Most of these features arose from discussions combining the data-perspective view from visualization with the physical background knowledge of the underlying processes. While our system has been built for a specific application, the concepts translate to data sets for other stochastic particle simulations. Full article

(This article belongs to the Special Issue Scalable Interactive Visualization)

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4613 KiB

Open AccessArticle

Big Data Management with Incremental K-Means Trees–GPU-Accelerated Construction and Visualization

by Jun Wang, Alla Zelenyuk, Dan Imre and Klaus Mueller

Informatics 2017, 4(3), 24; https://doi.org/10.3390/informatics4030024 - 28 Jul 2017

Cited by 9 | Viewed by 9824

Abstract

While big data is revolutionizing scientific research, the tasks of data management and analytics are becoming more challenging than ever. One way to remit the difficulty is to obtain the multilevel hierarchy embedded in the data. Knowing the hierarchy enables not only the revelation of the nature of the data, it is also often the first step in big data analytics. However, current algorithms for learning the hierarchy are typically not scalable to large volumes of data with high dimensionality. To tackle this challenge, in this paper, we propose a new scalable approach for constructing the tree structure from data. Our method builds the tree in a bottom-up manner, with adapted incremental k-means. By referencing the distribution of point distances, one can flexibly control the height of the tree and the branching of each node. Dimension reduction is also conducted as a pre-process, to further boost the computing efficiency. The algorithm takes a parallel design and is implemented with CUDA (Compute Unified Device Architecture), so that it can be efficiently applied to big data. We test the algorithm with two real-world datasets, and the results are visualized with extended circular dendrograms and other visualization techniques. Full article

(This article belongs to the Special Issue Scalable Interactive Visualization)

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6878 KiB

Open AccessArticle

Constructing Interactive Visual Classification, Clustering and Dimension Reduction Models for n-D Data

by Boris Kovalerchuk and Dmytro Dovhalets

Informatics 2017, 4(3), 23; https://doi.org/10.3390/informatics4030023 - 25 Jul 2017

Cited by 7 | Viewed by 7894

Abstract

Abstract: The exploration of multidimensional datasets of all possible sizes and dimensions is a long-standing challenge in knowledge discovery, machine learning, and visualization. While multiple efficient visualization methods for n-D data analysis exist, the loss of information, occlusion, and clutter continue to be a challenge. This paper proposes and explores a new interactive method for visual discovery of n-D relations for supervised learning. The method includes automatic, interactive, and combined algorithms for discovering linear relations, dimension reduction, and generalization for non-linear relations. This method is a special category of reversible General Line Coordinates (GLC). It produces graphs in 2-D that represent n-D points losslessly, i.e., allowing the restoration of n-D data from the graphs. The projections of graphs are used for classification. The method is illustrated by solving machine-learning classification and dimension-reduction tasks from the domains of image processing, computer-aided medical diagnostics, and finance. Experiments conducted on several datasets show that this visual interactive method can compete in accuracy with analytical machine learning algorithms. Full article

(This article belongs to the Special Issue Scalable Interactive Visualization)

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6120 KiB

Open AccessArticle

PERSEUS-HUB: Interactive and Collective Exploration of Large-Scale Graphs

by Di Jin, Aristotelis Leventidis, Haoming Shen, Ruowang Zhang, Junyue Wu and Danai Koutra

Informatics 2017, 4(3), 22; https://doi.org/10.3390/informatics4030022 - 18 Jul 2017

Cited by 8 | Viewed by 10847

Abstract

Graphs emerge naturally in many domains, such as social science, neuroscience, transportation engineering, and more. In many cases, such graphs have millions or billions of nodes and edges, and their sizes increase daily at a fast pace. How can researchers from various domains explore large graphs interactively and efficiently to find out what is ‘important’? How can multiple researchers explore a new graph dataset collectively and “help” each other with their findings? In this article, we present Perseus-Hub, a large-scale graph mining tool that computes a set of graph properties in a distributed manner, performs ensemble, multi-view anomaly detection to highlight regions that are worth investigating, and provides users with uncluttered visualization and easy interaction with complex graph statistics. Perseus-Hub uses a Spark cluster to calculate various statistics of large-scale graphs efficiently, and aggregates the results in a summary on the master node to support interactive user exploration. In Perseus-Hub, the visualized distributions of graph statistics provide preliminary analysis to understand a graph. To perform a deeper analysis, users with little prior knowledge can leverage patterns (e.g., spikes in the power-law degree distribution) marked by other users or experts. Moreover, Perseus-Hub guides users to regions of interest by highlighting anomalous nodes and helps users establish a more comprehensive understanding about the graph at hand. We demonstrate our system through the case study on real, large-scale networks. Full article

(This article belongs to the Special Issue Scalable Interactive Visualization)

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15031 KiB

Open AccessArticle

Visual Exploration of Large Multidimensional Data Using Parallel Coordinates on Big Data Infrastructure

by Joris Sansen, Gaëlle Richer, Timothée Jourde, Frédéric Lalanne, David Auber and Romain Bourqui

Informatics 2017, 4(3), 21; https://doi.org/10.3390/informatics4030021 - 12 Jul 2017

Cited by 17 | Viewed by 10976

Abstract

The increase of data collection in various domains calls for an adaptation of methods of visualization to tackle magnitudes exceeding the number of available pixels on screens and challenging interactivity. This growth of datasets size has been supported by the advent of accessible and scalable storage and computing infrastructure. Similarly, visualization systems need perceptual and interactive scalability. We present a complete system, complying with the constraints of aforesaid environment, for visual exploration of large multidimensional data with parallel coordinates. Perceptual scalability is addressed with data abstraction while interactions rely on server-side data-intensive computation and hardware-accelerated rendering on the client-side. The system employs a hybrid computing method to accommodate pre-computing time or space constraints and achieves responsiveness for main parallel coordinates plot interaction tools on billions of records. Full article

(This article belongs to the Special Issue Scalable Interactive Visualization)

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1363 KiB

Open AccessArticle

TOPCAT: Desktop Exploration of Tabular Data for Astronomy and Beyond

by Mark Taylor

Informatics 2017, 4(3), 18; https://doi.org/10.3390/informatics4030018 - 27 Jun 2017

Cited by 13 | Viewed by 11443

Abstract

TOPCAT, the Tool for OPerations on Catalogues And Tables, is an interactive desktop application for retrieval, analysis and manipulation of tabular data, offering a powerful and flexible range of interactive visualization options amongst other features. Its visualization capabilities focus on enabling interactive exploration of large static local tables—millions of rows and hundreds of columns can easily be handled on a standard desktop or laptop machine, and various options are provided for meaningful graphical representation of such large datasets. TOPCAT has been developed in the context of astronomy, but many of its features are equally applicable to other domains. The software, which is free and open source, is written in Java, and the underlying high-performance visualisation library is suitable for re-use in other applications. Full article

(This article belongs to the Special Issue Scalable Interactive Visualization)

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4661 KiB

Open AccessArticle

Web-Scale Multidimensional Visualization of Big Spatial Data to Support Earth Sciences—A Case Study with Visualizing Climate Simulation Data

by Sizhe Wang, Wenwen Li and Feng Wang

Informatics 2017, 4(3), 17; https://doi.org/10.3390/informatics4030017 - 26 Jun 2017

Cited by 15 | Viewed by 10288

Abstract

The world is undergoing rapid changes in its climate, environment, and ecosystems due to increasing population growth, urbanization, and industrialization. Numerical simulation is becoming an important vehicle to enhance the understanding of these changes and their impacts, with regional and global simulation models producing vast amounts of data. Comprehending these multidimensional data and fostering collaborative scientific discovery requires the development of new visualization techniques. In this paper, we present a cyberinfrastructure solution—PolarGlobe—that enables comprehensive analysis and collaboration. PolarGlobe is implemented upon an emerging web graphics library, WebGL, and an open source virtual globe system Cesium, which has the ability to map spatial data onto a virtual Earth. We have also integrated volume rendering techniques, value and spatial filters, and vertical profile visualization to improve rendered images and support a comprehensive exploration of multi-dimensional spatial data. In this study, the climate simulation dataset produced by the extended polar version of the well-known Weather Research and Forecasting Model (WRF) is used to test the proposed techniques. PolarGlobe is also easily extendable to enable data visualization for other Earth Science domains, such as oceanography, weather, or geology. Full article

(This article belongs to the Special Issue Scalable Interactive Visualization)

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1976 KiB

Open AccessArticle

Visual Analysis of Relationships between Heterogeneous Networks and Texts: An Application on the IEEE VIS Publication Dataset

by Björn Zimmer, Magnus Sahlgren and Andreas Kerren

Informatics 2017, 4(2), 11; https://doi.org/10.3390/informatics4020011 - 11 May 2017

Cited by 3 | Viewed by 9838

Abstract

The visual exploration of large and complex network structures remains a challenge for many application fields. Moreover, a growing number of real-world networks is multivariate and often interconnected with each other. Entities in a network may have relationships with elements of other related datasets, which do not necessarily have to be networks themselves, and these relationships may be defined by attributes that can vary greatly. In this work, we propose a comprehensive visual analytics approach that supports researchers to specify and subsequently explore attribute-based relationships across networks, text documents and derived secondary data. Our approach provides an individual search functionality based on keywords and semantically similar terms over the entire text corpus to find related network nodes. For examining these nodes in the interconnected network views, we introduce a new interaction technique, called Hub2Go, which facilitates the navigation by guiding the user to the information of interest. To showcase our system, we use a large text corpus collected from research papers listed in the visualization publication dataset that consists of 2752 documents over a period of 25 years. Here, we analyze relationships between various heterogeneous networks, a bag-of-words index and a word similarity matrix, all derived from the initial corpus and metadata. Full article

(This article belongs to the Special Issue Scalable Interactive Visualization)

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