IVAPP 2019 Abstracts


Area 1 - Abstract Data Visualization

Full Papers
Paper Nr: 6
Title:

Visual Analytics of Multidimensional Projections for Constructing Classifier Decision Boundary Maps

Authors:

Mateus Espadoto, Francisco M. Rodrigues and Alexandru C. Telea

Abstract: Visualizing decision boundaries of modern machine learning classifiers can notably help in classifier design, testing, and fine-tuning. Dense maps are a very recent method that overcomes the key sparsity-related limitation of scatterplots for this task. However, the trustworthiness of dense maps heavily depends on the underlying dimensionality-reduction (DR) techniques they use. We design and perform a detailed study aimed at finding the best DR techniques to use when creating trustworthy dense maps, by studying a large collection of 28 DR algorithms, 4 classifiers, and 2 datasets from a real-world challenging classification problem. Our results show how one can pick suitable DR algorithms to create dense maps that help understanding classifier behavior.

Paper Nr: 16
Title:

Equivalence of Turn-Regularity and Complete Extensions

Authors:

Alexander M. Esser

Abstract: The aim of the two-dimensional compaction problem is to minimize the total edge length or the area of an orthogonal grid drawing. The coordinates of the vertices and the length of the edges can be altered while all angles and the shape of the drawing have to be preserved. The problem has been shown to be NP-hard. Two commonly used compaction methods are the turn-regularity approach by (Bridgeman et al., 2000) and the approach by (Klau and Mutzel, 1999) considering complete extensions. We formally prove that these approaches are equivalent, i. e. a face of an orthogonal representation is turn-regular if and only if there exists a unique complete extension for the segments bounding this face.

Paper Nr: 23
Title:

Visual Growing Neural Gas for Exploratory Data Analysis

Authors:

Elio Ventocilla and Maria Riveiro

Abstract: This paper argues for the use of a topology learning algorithm, the Growing Neural Gas (GNG), for providing an overview of the structure of large and multidimensional datasets that can be used in exploratory data analysis. We introduce a generic, off-the-shelf library, Visual GNG, developed using the Big Data framework Apache Spark, which provides an incremental visualization of the GNG training process, and enables user-in-the-loop interactions where users can pause, resume or steer the computation by changing optimization parameters. Nine case studies were conducted with domain experts from different areas, each working on unique real-world datasets. The results show that Visual GNG contributes to understanding the distribution of multidimensional data; finding which features are relevant in such distribution; estimating the number of k clusters to be used in traditional clustering algorithms, such as K-means; and finding outliers.

Paper Nr: 52
Title:

Design and Implementation of Web-based Hierarchy Visualization Services

Authors:

Willy Scheibel, Judith Hartmann and Jürgen Döllner

Abstract: There is a rapidly growing, cross-domain demand for interactive, high-quality visualization techniques as components of web-based applications and systems. In this context, a key question is how visualization services can be designed, implemented, and operated based on Software-as-a-Service as software delivery model. In this paper, we present concepts and design of a SaaS framework and API of visualization techniques for tree-structured data, called HIVISER. Using representational state transfer (REST), the API supports different data formats, data manipulations, visualization techniques, and output formats. In particular, the API defines base resource types for all components required to create an image or a virtual scene of a hierarchy visualization. We provide a treemap visualization service as prototypical implementation for which subtypes of the proposed API resources have been created. The approach generally serves as a blue-print for fully web-based, high-end visualization services running on thin clients in a standard browser environment.

Short Papers
Paper Nr: 1
Title:

Dynamic Software Visualization of Quantum Algorithms with Rainbow Boxes

Authors:

Jean-Baptiste Lamy

Abstract: Quantum computing has emerged recently as a new computational paradigm. It considers quantum bits (qubits) instead of classical bits. However, quantum algorithms are often very difficult to understand. In this paper, we propose a tool for quantum software visualization. It presents visually the state of multiple-qubits and its evolution at runtime during the execution of a quantum program. This tool allows a unique representation of a quantum state, contrary to the usual vector notation. We show how the problem of visualizing a quantum state can be reduced to a set visualization problem, and our tool uses rainbow boxes to visualize the resulting sets. We also present the application of the proposed tool to quantum teleportation, an algorithm of high importance in cryptography. Finally, we discuss the limit of this approach and its perspectives, in particular for teaching quantum computing.

Paper Nr: 14
Title:

Multitree-like Graph Layering Crossing Optimization

Authors:

Radek Mařík

Abstract: We improve a method of multitree-like graph visualization using a spanning tree-driven layout technique with constraints specified by layers and the ordering of groups of nodes within layers. We propose a new method of how the order of subtrees selected by the driving spanning tree can be derived from the actual edge crossings. Such a subtree order leads to additional decreasing of total edge crossings from 1% to 50%. This depends on the shape of the processed graph, ranging from a pure tree to a general acyclic graph. Our achievements are demonstrated using several datasets containing up to millions of people, species, or services. The proposed subtree ordering method of layered graphs that are similar to acyclic multitrees retains the generating of acceptable layouts in almost linear time.

Paper Nr: 43
Title:

NEMESIS (NEtwork MEdicine analySIS): Towards Visual Exploration of Network Medicine Data

Authors:

Marco Angelini, Graziano Blasilli, Lorenzo Farina, Simone Lenti and Giuseppe Santucci

Abstract: The emerging Network Medicine domain is causing a shift between diagnosis based on the conventional reductionist approach, arguing that biological factors work in a simple linear way, and the analysis of perturbations within the comprehensive network map of molecular components and their interactions, i.e., the ”Interactome”. As a consequence, clinicians are investigating more than 140,000 interactions between more than 13,000 genes and their connections with drugs and diseases, along a sequence of ”networks”. Making sense of this complex structure is a challenging activity and the visual analytics application NEMESIS tries to attack such a problem allowing for interactively exploring this large body of knowledge, focusing on subsets of data and investigating their relationships with other relevant dimensions, pursuing the main goal of facilitating hypothesis formulation and validation.

Paper Nr: 46
Title:

Gradient Descent Analysis: On Visualizing the Training of Deep Neural Networks

Authors:

Martin Becker, Jens Lippel and Thomas Zielke

Abstract: We present an approach to visualizing gradient descent methods and discuss its application in the context of deep neural network (DNN) training. The result is a novel type of training error curve (a) that allows for an exploration of each individual gradient descent iteration at line search level; (b) that reflects how a DNN’s training error varies along each of the descent directions considered; (c) that is consistent with the traditional training error versus training iteration view commonly used to monitor a DNN’s training progress. We show how these three levels of detail can be easily realized as the three stages of Shneiderman’s Visual Information Seeking Mantra. This suggests the design and development of a new interactive visualization tool for the exploration of DNN learning processes. We present an example that showcases a conceivable interactive workflow when working with such a tool. Moreover, we give a first impression of a possible DNN hyperparameter analysis.

Paper Nr: 4
Title:

Qualitative and Quantitative Results of Enterprise Security Visualization Requirements Analysis through Surveying

Authors:

Ferda Ö. Sönmez and Banu Günel

Abstract: In order to find gaps or missing points in any domain, examination of the literature work is necessary and provides a good amount of information. Doing a requirement analysis on top of this literature search incorporating the domain experts is a convenient way to find out ideas to fill out the detected gaps. The security visualization domain has been popular for the latest twenty years. There have been many designs. However, our literature analyses work resulted with the conclusion that the majority of the earlier security visualization work focuses a known set of use-cases, and these are trying to be validated using these small sets of vulnerabilities and some commonly known threats through a few case studies or experimental results. In this work, a security visualization requirement analysis survey with 30 information security experts is done. The paper presents the qualitative and quantitative results of this survey.

Paper Nr: 7
Title:

Inside Mall: Visual Analytics of Customer Behavior and Activities

Authors:

Jens Opolka, Patrick Riehmann, Heiko Peter and Bernd Froehlich

Abstract: This paper presents a coordinated multi-view system consisting of visualizations for displaying customer behavior and activities in a shopping center environment based on indoor tracking information gathered by Bluetooth beacons. Different perspectives to find structures and hidden patterns within the data set are supported including different customer flow visualization methods based on actual floor plans as well as abstract flow graphs. These are linked and coordinated with Parallel Sets to gain insight into the specifics of the customer base and with different time-oriented visualizations to show and to compare the different periods of customer presence.

Paper Nr: 8
Title:

Combining Interactive Hierarchy Visualizations in a Web-based Application

Authors:

Michael Burch, Willem Aerts, Daan Bon, Sean McCarren, Laurent Rothuizen, Olivier Smet and Daan Wöltgens

Abstract: In this paper we describe a web-based tool combining several hierarchy visualization techniques. Those run in a browser and support the communication of hierarchy data that is omnipresent in many application fields like biology, software engineering, sports, or in algorithmic approaches like hierarchical clustering. To this end we provide node-link diagrams, Pythagoras trees, circular, as well as 3D treemaps also called 3D step-trees to give several visual perspectives on the same data and to improve data exploration tasks. The visualizations are interactive and linked, while the tool is available online, making it easily accessible for people all around the world without installing extra software or relying on additional libraries and frameworks. Hierarchy datasets can be uploaded to a server and shared with others. The visualizations were primarily implemented using JavaScript, and more specifically, rendered using the D3.js library. We illustrate the usefulness of the interactive visualization by applying them to the NCBI taxonomy and the Influenza dataset.

Paper Nr: 17
Title:

Effective Visual Exploration of Variables and Relationships in Parallel Coordinates Layout

Authors:

Gurminder Kaur and Bijaya B. Karki

Abstract: We present two innovative ways of enhancing parallel coordinates axes to better understand all variables and their interrelationships in high-dimensional datasets. Histogram and circle/ellipse plots based on uniform (linear) and non-uniform frequency/density mappings are adopted to visualize distributions of numerical and categorical data values. These plots are, particularly, helpful in emphasizing data values of low frequencies as well as those with similar frequencies. Color-mapped axis stripes are designed to visually connect numerical variables irrespective of their locations (adjacent or nonadjacent axes) in the parallel coordinates layout so that correlations can be fully realized in the same display. Distribution plots and axis stripes are integrated to further facilitate exploratory analysis of multivariate data with respect to a complete variable set.

Paper Nr: 25
Title:

Visual Exploration Tools for Ensemble Clustering Analysis

Authors:

Sonia Fiol-González, Cassio P. Almeida, Ariane B. Rodrigues, Simone J. Barbosa and Hélio Lopes

Abstract: Uncertainty Analysis is essential to support decisions, and it has been gaining attention in both visualization and machine learning communities —in the latter case, mainly because ensemble methods are becoming a robust approach in several applications. In particular, for unsupervised learning, there are several ensemble clustering methods that generate a co-association matrix, i.e., a matrix whose element (i, j) represents the estimated probability that the given sample pair is on the same cluster. This work studies the following decision problem: “Given a similarity function, which groups of elements of a set form robust clusters?” Robust here means that all elements of each cluster are connected with a probability within a given interval. Our main contribution is a prototype that helps decision makers, through visual exploration, to have insights to solve this task. To do so, we provide visual tools for ensemble clustering analysis. Such tools are grounded in the co-association matrix generated by the ensemble. With these tools we are better equipped to recommend the group of elements that form each cluster, considering the uncertainty generated by ensemble clustering methods.

Paper Nr: 31
Title:

Data Multiplexing Through Animated Texture Orientation and Color

Authors:

Maria-Jesus Lobo and Christophe Hurter

Abstract: Multidimensional data visualization is still a challenge for complex data exploration. Usually, each data dimension might be mapped to one available visual variable such as position, shape or color. While spatial and color data mappings have been previously intensively explored, animated encodings have been far less investigated. However, such techniques are widely used in existing visualizations. In this paper, we propose to assess the visual assets of direction and orientation of directed animated textures to encode data. We present a user study that compares three animated textures and a static representation. The results suggest that the animated techniques can be as effective as the static representation in terms of accuracy and data retrieval time. Finally, we present some design guidelines to efficiently use animated particle visualizations .

Paper Nr: 32
Title:

Visual Analytics of Bibliographical Data for Strategic Decision Support of University Leaders: A Design Study

Authors:

Paul Rosenthal, Nicholas H. Müller and Fabian Bolte

Abstract: As responsibilities about documentation of work in conjunction with an increase in third-party-funding for universities have been shifting over the last decade, new tools for the inspection and reporting of data are increasingly requested for strategic decision making. Therefore, we present a design study that aims to craft a stream visualization for the easy to use and easy to understand display of publications across university institutions. A formal design process was performed and led to a web-based visualization prototype of the available university data sets. The used visualization techniques, counting methodology, highlighting practices, and interaction paradigms are discussed and presented in detail. The design study was completed by an informal evaluation procedure within the ranks of strategic decision making staff. It turned out, that the developed expert tool allows to identify connections for future projects. In addition, it enables management to recognize promising departments or to apply support where it is needed.

Paper Nr: 37
Title:

A Design-based Approach to Enhancing Technical Drawing Skills in Design and Engineering Education using VR and AR Tools

Authors:

O. Huerta, A. Kus, E. Unver, R. Arslan, M. Dawood, M. Kofoğlu and V. Ivanov

Abstract: There are concerns from higher education (HE) institutions and industry about the decline in standards of technical drawings (TDs) due to the lack of understanding of basic geometric construction and the conventions of drafting skills that underpin the best practices. There is growing evidence that simulations/animations along with augmented and virtual reality (AR/VR) technologies can improve learners’ engagement, competence, and skills; especially when compared to traditional didactic methods. However, this approach to teaching and learning (T&L) is difficult when studied at distance, or without access to the appropriate technologies to carry out the suggested activities. Leading to the need to develop appropriate methods and content that suit this pedagogical problem. This paper describes the development of an AR/VR application to support the T&L of design and engineering students in education and industry. Using a multi-disciplinary design-based research methodology, this European (UK, Bulgaria, Turkey) funded research project combines pedagogy and technology to approach TDs education problems; and to develop an AR/VR education solution to address learning difficulties within the different critical TDs categories identified. This development is based on findings from an international study in three different categories covering the perception of TDs education, assessing of TDs knowledge and ability, and expectations of TDs education. This research project also covers the difficulties and good practices of multi-disciplinary teams for developing TDs and AR/VR contents where the approaches to T&L may differ between practices.

Paper Nr: 42
Title:

In-situ Comparison for 2.5D Treemaps

Authors:

Daniel Limberger, Matthias Trapp and Jürgen Döllner

Abstract: 2.5D treemaps can be used to visualize tree-structured data using the height dimension for additional information display. For tree-structured and time-variant data though, changes or variants in the data are difficult to visualize. This paper presents an in-situ approach to depict differences between two versions (original and comparative state) of a data set, e.g., metrics of different revisions of a software system, in a single 2.5D treemap. Multiple geometry variants for the in-situ representation of individual nodes, especially concerning height, area, and color, are presented and discussed. Finally, a preliminary study for the simultaneous change of attributes in height and area is described, hinting that arrow pattern help to clarify reading direction.

Paper Nr: 47
Title:

TypeVis: Visualization of Keystrokes and Typing Patterns based on Time Series Analysis

Authors:

Kinga Enyedi and Roland Kunkli

Abstract: In recent decades, people spend more and more time in front of their computers; therefore, instead of writing on a paper, it is more common to type on a keyboard. In the case of handwriting, there are personal traits implicitly left with the written text since people do not write the same way. Since how one writes on a paper using a writing tool results in a different outlook of the written text for each person, one may question whether the process of typing on a keyboard involves any specific personality traits or not. It can be proven that several particular properties exist in the case of keyboard usage too. One of the most typical properties is based on differences in time with respect to pressing and releasing keys on the keyboard. In this paper, we present a visualization method for representing the typing styles of people, based on differences in when they press and release each key on the keyboard over time. Using time differences of keystrokes for visualization purposes allows to create a visible image about an individual’s personal typing habits.

Area 2 - General Data Visualization

Full Papers
Paper Nr: 5
Title:

Breaking the Curse of Visual Data Exploration: Improving Analyses by Building Bridges between Data World and Real World

Authors:

Matthias Kraus, Niklas Weiler, Thorsten Breitkreutz, Daniel A. Keim and Manuel Stein

Abstract: Visual data exploration is a useful means to extract relevant information from large sets of data. The visual analytics pipeline processes data recorded from the real world to extract knowledge from gathered data. Subsequently, the resulting knowledge is associated with the real world and applied to it. However, the considered data for the analysis is usually only a small fraction of the actual real-world data and lacks above all in context information. It can easily happen that crucial context information is disregarded, leading to false conclusions about the real world. Therefore, conclusions and reasoning based on the analysis of this data pertain to the world represented by the data, and may not be valid for the real world. The purpose of this paper is to raise awareness of this discrepancy between the data world and the real world which has a high impact on the validity of analysis results in the real world. We propose two strategies which help to identify and remove specific differences between the data world and the real world. The usefulness and applicability of our strategies are demonstrated via several use cases.

Paper Nr: 18
Title:

Compensation of Simultaneous Orientation Contrast in Superimposed Textures

Authors:

Rudolf Netzel, Nils Rodrigues, Anja Haug and Daniel Weiskopf

Abstract: We propose a method that compensates the simultaneous orientation contrast in the visualization of superimposed textures. Such superposition plays a role in visualizations that overlay or enrich visual representations of data with additional information. Our compensation method extracts the direction and frequency within the input textures by using a Gabor filter bank. The foreground texture is then rotated to counterbalance the tilt illusion. The rotation angle is determined by a model that adopts results of previous studies that measured the influence of perceived contrast, direction, and frequency on the perceived tilt. The effectiveness of our method is demonstrated for artificial stimuli and a typical example of scientific flow visualization of multiple vector fields.

Paper Nr: 24
Title:

MEV: Visual Analytics for Medication Error Detection

Authors:

Tabassum Kakar, Xiao Qin, Cory M. Tapply, Oliver Spring, Derek Murphy, Daniel Yun, Elke A. Rundensteiner, Lane Harrison, Thang La, Sanjay K. Sahoo and Suranjan De

Abstract: To detect harmful medication errors and inform regulatory actions, the U.S. Food & Drug Administration uses the FAERS spontaneous reporting system to collect medication error reports. Drug safety analysts, however, review the submitted report narratives one by one to pinpoint critical medication errors. Based on a formative study of the review process requirements, we design an interactive visual analytics prototype called Medication Error Visual analytics (MEV), to facilitate the medication error review process. MEV visualizes distributions of the reports over multiple data attributes such as products, types of error, etc., to guide analysts towards most concerning medication errors. MEV supports interactive filtering on key data attributes that aim to help analysts hone in on the set of evidential reports. A multi-layer treemap visualizes the count and severity of the errors conveyed in the underlying reports, while the interaction between these layers aid in the analysis of the corresponding data attributes and their relationships. The results of a user study conducted with analysts at the FDA suggests that participants are able to perform the essential screening and review tasks more quickly with MEV and perceive tasks as being easier with MEV than with their existing tool set. Post-study qualitative interviews illustrates analysts’ interest in the use of visual analytics for FAERS reports analysis operations, opportunities for improving the capabilities of MEV, and new directions for analyzing critical spontaneous reports at scale.

Paper Nr: 26
Title:

Parallel Coordinates-based Visual Analytics for Materials Property

Authors:

Diwas Bhattarai and Bijaya B. Karki

Abstract: Because of major advances in experimental and computational techniques, materials data are abundant even for specific classes of materials such as magma-forming silicate melts. A given material property M can be posed as a complex multivariate data problem. The relevant variables or dimensions are the values of the property itself, the factors which influence the property (pressure P, temperature T, multicomponent composition X), and meta data information I. Here we present an innovative visual analytics system for the melt viscosity (η), which can be represented by M (η, P, T, X1, X2, ..., I1, I2, ...). Our system consists of a viscosity data store along with a web-based visualization support. In particular, we enrich the parallel coordinates plot with non-standard features, such as derived axes/sub-axes, dimension merging, binary scaling, and nested plot. It offers many insights of relevance to underlying physics, data modeling, and guiding future experiments/computations. Other material properties such as density can be incorporated as new attributes and corresponding new axes in the plot. Our aim is to collect all published data on various melt properties and develop a framework supporting database, visualization and modelling functions.

Paper Nr: 39
Title:

Lightweight Coordination of Multiple Independent Visual Analytics Tools

Authors:

Hans-Jörg Schulz, Martin Röhlig, Lars Nonnemann, Mario Aehnelt, Holger Diener, Bodo Urban and Heidrun Schumann

Abstract: With the advancement of Visual Analytics (VA) and its spread into various application fields comes along a specialization of methods and tools. This adds complexity and requires extra effort when devising domain-dependent VA solutions, as for every new domain question a new specialized tool or framework must be developed. In this paper, we investigate the possibility of using and re-using existing tools – domain-dependent and general-purpose – by loosely coupling them into specialized VA tool ensembles as needed. We call such coupling among independent tools lightweight coordination, as it is minimally-invasive, pair-wise, and opportunistic in utilizing whichever interface a VA tool offers. We propose the use of lightweight coordination for managing the workflow, the data flow, and the control flow among VA tools, and we show how it can be supported with suitable setups of the multiple tool UIs involved. This concept of lightweight coordination is exemplified with a health care scenario, where an ensemble of independent VA tools is used in a concerted way to pursue the visual analysis of a patient’s troublesome vital data.

Short Papers
Paper Nr: 10
Title:

Segmentation of Dashboard Screen Images: Preparation of Inputs for Object-based Metrics of UI Quality

Authors:

Jiří Hynek and Tomáš Hruška

Abstract: Using object-based metrics to analyze design aspects of user interfaces (UI) is a suitable approach for the quantitative evaluation of the visual quality of user interfaces. Balance or Symmetry are examples of such metrics. On the other hand, we need to deal with the problem of a detection of objects within a user interface screen which represent the inputs for the object-based metrics. Today’s user interfaces (e. g., dashboards) are complex. They consist of several color layers, and it is complicated to segment them by well-known page segmentation methods which are usually used for the segmentation of printed documents. We also need to consider the subjective perception of users and principles of objects grouping (as Gestalt laws). Users usually group simple objects (graphical elements and shapes) into coherent visually dominant objects. We analyzed the experience of 251 users manually segmenting dashboard screens and designed a novel method for the automatic segmentation of dashboard screen images. The method initially focuses on the reduction of image colors which represents image layers. Then, it detects the primitives which makes a screen layout. Finally, the method processes the screen layout using the combination of the top-down and bottom-up segmentation strategy and detects visually dominant regions.

Paper Nr: 13
Title:

ExploroBOT: Rapid Exploration with Chart Automation

Authors:

John McAuley, Rohan Goel and Tamara Matthews

Abstract: General-purpose visualization tools are used by people with varying degrees of data literacy. Often the user is not a professional analyst or data scientist and uses the tool infrequently, to support an aspect of their job. This can present difficulties as the user’s unfamiliarity with visualization practice and infrequent use of the tool can result in long processing time, inaccurate data representations or inappropriate visual encodings. To address this problem, we developed a visual analytics application called exploroBOT. The exploroBOT automatically generates visualizations and the exploration guidance path (an associated network of decision points, mapping nodes where visualizations change). These combined approaches enable users to explore visualizations based on a degree of “interestingness”. The user-driven approach draws on the browse/explore metaphor commonly applied in social media applications and is supported by guided navigation. In this paper we describe exploroBOT and present an evaluation of the tool.

Paper Nr: 27
Title:

A Hybrid Approach based on Parallel Coordinates and Star Plot

Authors:

Kang Xie and Bijaya B. Karki

Abstract: Multivariate data visualization has to accommodate all dimensions/variables of a given dataset in the same display so that the data items can be rendered with respect to these variables. We propose a hybrid approach based on the combination of the standard parallel coordinates and star plot techniques by implementing a focus + context scheme. The focus area displays the parallel coordinates plot of the data with respect to few selected dimensions by mapping them as vertical parallel axes sufficiently wide to provide a clear view of the variables and data. The context area then maps the rest of the variables as tightly packed radial axes forming one or two partial star plots. We design multiple layouts of combining the parallel and star axes. Each layout maintains the data continuity between the focus and context displays. Our tests show that the proposed hybrid axes plot can manage a large number of variables (even exceeding one hundred) to support effective visualization of ultra-high dimensional datasets.

Paper Nr: 44
Title:

Software Engineering Projects Analysis using Interactive Multimodal Graph Explorer – IMiGEr

Authors:

Lukas Holy, Petr Picha, Richard Lipka and Premek Brada

Abstract: This paper describes a visualization technique designed to help work with complex diagrams containing multiple types of nodes and edges, by using a combination of visual clutter reduction and graph exploration techniques. We show its application, including preliminary evaluation, on software engineering projects data gathered from various tools and repositories used for software development. An online tool implementing the technique and plans for its extension by a connected view of time perspective data are briefly presented.

Paper Nr: 11
Title:

A Data Visualization Approach for Intersection Analysis using AIS Data

Authors:

Ricardo C. Pereira, Pedro H. Abreu, Evgheni Polisciuc and Penousal Machado

Abstract: Automatic Identification System data has been used in several studies with different directions like traffic forecasting, pollution control or anomalous behavior detection in vessels trajectories. Considering this last subject, the intersection between vessels is often related with abnormal behaviors, but this topic has not been exploited yet. In this paper an approach to assist the domain experts in the task of analyzing these intersections is introduced, based on data processing and visualization. The work was experimented with a proprietary dataset that covers the Portuguese maritime zone, containing an average of 6460 intersections by day. The results show that several intersections were only noticeable with the visualization strategies here proposed.

Paper Nr: 28
Title:

Visualization Methods for Educational Timetabling Problems: A Systematic Review of Literature

Authors:

Wanderley S. Alencar, Hugo A. Dantas do Nascimento, Fabrizzio N. Soares and Humberto J. Longo

Abstract: This paper investigates, through a Systematic Review of the Literature (SRL), the application of advanced Information Visualization (IV) methods to the Educational Timetabling Problem (Ed-TTP). The aim is to show how IV can facilitate the human perception of the several elements embedded in a school or university timetable scheduling. We also investigates how interactive IVs have been proposed to help creating/improving timetabling solutions, particularly when time conflict is a major challenging to be solved. In this SRL we considered publications from the last twenty years (1998–2018) indexed by seven solid scientific databases. The review clearly identified that there is a small amount of studies devoted to the intersection between IV and Ed-TTP in that period. Ideas for future research in this intersection field are discussed.

Area 3 - Spatial Data Visualization

Full Papers
Paper Nr: 34
Title:

Synthesising Light Field Volumetric Visualizations in Real-time using a Compressed Volume Representation

Authors:

Seán Bruton, David Ganter and Michael Manzke

Abstract: Light field display technology will permit visualization applications to be developed with enhanced perceptual qualities that may aid data inspection pipelines. For interactive applications, this will necessitate an increase in the total pixels to be rendered at real-time rates. For visualization of volumetric data, where ray-tracing techniques dominate, this poses a significant computational challenge. To tackle this problem, we propose a deep-learning approach to synthesise viewpoint images in the light field. With the observation that image content may change only slightly between light field viewpoints, we synthesise new viewpoint images from a rendered subset of viewpoints using a neural network architecture. The novelty of this work lies in the method of permitting the network access to a compressed volume representation to generate more accurate images than achievable with rendered viewpoint images alone. By using this representation, rather than a volumetric representation, memory and computation intensive 3D convolution operations are avoided. We demonstrate the effectiveness of our technique against newly created datasets for this viewpoint synthesis problem. With this technique, it is possible to synthesise the remaining viewpoint images in a light field at real-time rates.

Paper Nr: 40
Title:

Coordinated Image- and Feature-space Visualization for Interactive Magnetic Resonance Spectroscopy Imaging Data Analysis

Authors:

Muhammad Jawad, Vladimir Molchanov and Lars Linsen

Abstract: Magnetic Resonance Spectroscopy Imaging (MRSI) is a medical imaging method that measures per voxel a spectrum of signal intensities. It allows for the analysis of chemical compositions within the scanned tissue, which is particularly useful for tumor classification and measuring its infiltration of healthy tissue. Common analysis approaches consider one metabolite concentration at a time to produce intensity maps in the image space, which does not consider all relevant information at hand. We propose a system that uses coordinated views between image-space visualizations and visual representations of the spectral (or feature) space. Coordinated interaction allows for analyzing both aspects and relating the analysis results back to the other for further investigations. We demonstrate how our system can be used to analyze brain tumors.

Paper Nr: 45
Title:

Grid-based Exploration of OCT Thickness Data of Intraretinal Layers

Authors:

Martin Röhlig, Jörg Stüwe, Christoph Schmidt, Ruby K. Prakasam, Oliver Stachs and Heidrun Schumann

Abstract: Optical coherence tomography (OCT) enables high-resolution 3D imaging of the human retina to understand a variety of retinal and systemic disorders. Commonly, the thickness of segmented intraretinal layers is used to assess the condition of the retina. However, the thickness data are complex and thus, need to be considerably reduced prior to further processing and analysis. This leads to a loss of information and may hinder the discovery of subtle and localized retinal changes, which are important for an early detection of certain diseases. On this account, we propose an enhanced grid-based reduction of OCT thickness data. We adapt established grid types for retinal thickness data and suggest alternative grids that capture more information. We integrate our data reduction approach into a visual analysis tool that supports an automated computation and interactive exploration of different grids. We demonstrate the application of our tool and show how it can be used to support experts in choosing and comparing appropriate grid representations for given OCT thickness data.

Short Papers
Paper Nr: 3
Title:

LoD PLI: Level of Detail for Visualizing Time-Dependent, Protein-Lipid Interaction

Authors:

Naif Alharbi, Michael Krone, Matthieu Chavent and Robert S. Laramee

Abstract: In Molecular Dynamics (MD) Visualization, representative surfaces of varying resolution are commonly used to depict protein molecules while a variety of geometric shapes, ribbons, and spheres are used to represent residues and atoms at different levels of detail (LoD). The focus of the visualization is usually on individual atoms or molecules themselves and less often on the interaction space between them. Here we focus on LoD interaction between lipids and proteins and the space in which this occurs in the context of a membrane simulation. With naive approaches, particles may overlap and significant interaction details can be obscured due to clutter. Furthermore, the spatial complexity of the protein-lipid interaction (PLI) increases over time. Co-developed with an MD domain expert, we address the challenge of visualizing complex, time-dependent interactions between lipids and proteins by introducing two abstract LoD representations with six levels of detail for lipid molecules and protein interaction. We also propose a fast GPU-based projection that maps lipid-constituents involved in the interaction onto the abstract LoD protein interaction space. The tool provides fast LoD, the imagery of PLI for 336,260 particles over almost 2,000 time-steps. The result is a great simplification in both perception and cognition of this complex interaction that reveals new patterns and insight for computational biologists. We also report feedback from the domain expert to our visualization.

Paper Nr: 12
Title:

Uncertainty-aware Prediction in Spatio-temporal Simulation Ensemble Visualizations

Authors:

Marina Evers and Lars Linsen

Abstract: Spatio-temporal simulation ensembles are used to investigate the dependence of the simulation behavior on input parameters. Running simulations for a large number of input parameter settings is computationally expensive. We propose a scheme for exploring the parameter space using predictions of simulation outcomes and estimating the uncertainty in the predictions. The prediction approximates the simulation result by interpolating feature vectors of existing runs. The feature vectors are used to compute similarities between simulation runs facilitating visualization of the entire ensemble within a 2D (or 1D-over-time) multi-dimensional scaling embedding. Uncertainties of the prediction are computed based on distance, interpolation and diversity, which are visually encoded by an uncertainty band in the embedding. To guide the user to choose suitable parameter settings for prediction, we also propose a parameter-space visualization of the uncertainty. The approach is applied to real-world data simulating deep-water impact of asteroids.

Paper Nr: 20
Title:

Energy-Based Visualization of 2D Flow Fields

Authors:

Karsten Hanser, Stefan Meggendorfer, Peter Hügel, Florian Fallenbüchel, Hafiz M. Fahad and Filip Sadlo

Abstract: In this paper, we present a novel approach for energy-based flow visualization. Inspired by Bernoulli’s principle, which is limited to steady inviscid flow, we derive a set of energies whose sum is conserved along pathlines in 2D time-dependent viscous flow. We present an interactive approach for visual analysis based on these quantities, as well as a compact color-coded representation. This enables effective analysis of energy conversion along selected pathlines, as well as its spatial coherence. We exemplify the utility of our approach using results from computational fluid dynamics and flow in elastic porous media.

Paper Nr: 29
Title:

Rendering Procedural Textures for Visualization of Thematic Data in 3D Geovirtual Environments

Authors:

Matthias Trapp, Frank Schlegel, Sebastian Pasewaldt and Jürgen Döllner

Abstract: 3D geovirtual environments, such as virtual 3D city and landscape models, can be used as scenery for visualizing thematic data. which can be communicated using suitable color mappings or hatch patterns. For rendering purposes, these hatches patterns can be represented as image-based or procedural textures. The resulting quality of image-based textures, and thus the effective communication of the respective thematic data, is subject to resolution and filtering artifacts. In contrast to thereto, procedural textures are not limited with respect to resolution and can be filtered adaptively to achieve high visual quality. However, challenges in parametrization and design often hinders their application. To counterbalance these drawbacks, this paper presents an interactive rendering technique that facilitates the application and design of procedural hatch patterns for the mapping of thematic data to 3D geovirtual environments.