IVAPP 2017 Abstracts

Area 1 - Abstract Data Visualization
Full Papers Short Papers
Area 2 - General Data Visualization
Full Papers Short Papers
Area 3 - Spatial Data Visualization
Full Papers Short Papers

Area 1 - Abstract Data Visualization

Full Papers
Paper Nr: 1
Title:

The Code-Map Metaphor - A Review of Its Use Within Software Visualisations
Authors:

Ivan Bacher, Brian Mac Namee and John Kelleher
Abstract: Software developers can use software visualisations employing the code-map metaphor to discover and correlate facts spread over a large code base. This work presents an extensive review of the use of the code-map metaphor for software visualisation. The review analyses a set of 29 publications, which together describe 21 software development tools that use visualisations employing the code-map metaphor. The review follows a task oriented framework to guide the analysis of the literature in terms of the task, audience, target, medium, representation, and evidence dimensions based on the code- map metaphor. Although the literature indicates that software visualisations based on the code-map metaphor are perceived by the research community to be helpful across all aspects of the software develop process, the main finding of our review is that there is a lack of quantitative evidence to support this perception. Thus, the effectiveness of visualisations incorporating the code-map metaphor is still unclear. The majority of the software visualisations analysed in this study, however, do provide qualitative observations regarding their usage in various scenarios. These are summarised and presented in this review as we believe the observations can be used as motivation for future empirical evaluations.
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Paper Nr: 2
Title:

A Hierarchical Magnification Approach for Enhancing the Insight in Data Visualizations
Authors:

Stavros Papadopoulos, Anastasios Drosou and Dimitrios Tzovaras
Abstract: Non-linear deformations are useful for applications where users face highly cluttered visual displays, either due to large datasets, or visualizations on small screens, or a combination of both, that increases the density of the data and makes the perception of patterns difficult. Non-linear deformations have been used to magnify significant/cluttered regions in data visualization, for the purpose of reducing clutter and enhancing the perception of patterns. General deformation methods (e.g. logarithmic scaling and fish-eye views) suffer from several drawbacks, since they do not consider the prominent features that must be preserved in the visualization. This work introduces a hierarchical approach for non-linear deformation that aims to reduce visual clutter by magnifying significant regions, and lead to enhanced visualizations of two/three-dimensional datasets on highly cluttered displays. The proposed approach utilizes an energy function, which aims to determine the optimal deformation for every local region in the data, taking the information from multiple single-layer significance maps into account. The problem is subsequently transformed into an optimization problem for the minimization of the energy function under specific spatial constraints. The proposed hierarchical approach for the generation of the significance map, surpasses current methods, and manages to efficiently identify significant regions and achieve better results.
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Paper Nr: 4
Title:

Parallel Coordinate Plots for Neighbor Retrieval
Authors:

Jaakko Peltonen and Ziyuan Lin
Abstract: Parallel Coordinate Plots (PCPs) are a prominent approach to visualize the full feature set of high-dimensional vectorial data, either standalone or complementing other visualizations like scatter plots. Optimization of PCPs has concentrated on ordering and positioning of the coordinate axes based on various statistical criteria. We introduce a new method to construct PCPs that are directly optimized to support a common data analysis task: analyzing neighborhood relationships of data items within each coordinate axis and across the axes. We optimize PCPs on 1D lines or 2D planes for accurate viewing of neighborhood relationships among data items, measured as an information retrieval task. Both the similarity measurement between axes and the axis positions are directly optimized for accurate neighbor retrieval. The resulting method, called Parallel Coordinate Plots for Neighbor Retrieval (PCP-NR), achieves better information retrieval performance than traditional PCPs in experiments.
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Paper Nr: 6
Title:

Evaluating Multi-attributes on Cause and Effect Relationship Visualization
Authors:

Juhee Bae, Elio Ventocilla, Maria Riveiro, Tove Helldin and Göran Falkman
Abstract: This paper presents findings about visual representations of cause and effect relationship’s direction, strength, and uncertainty based on an online user study. While previous researches focus on accuracy and few attributes, our empirical user study examines accuracy and the subjective ratings on three different attributes of a cause and effect relationship edge. The cause and effect direction was depicted by arrows and tapered lines; causal strength by hue, width, and a numeric value; and certainty by granularity, brightness, fuzziness, and a numeric value. Our findings point out that both arrows and tapered cues work well to represent causal direction. Depictions with width showed higher conjunct accuracy and were more preferred than that with hue. Depictions with brightness and fuzziness showed higher accuracy and were marked more understandable than granularity. In general, depictions with hue and granularity performed less accurately and were not preferred compared to the ones with numbers or with width and brightness.
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Paper Nr: 11
Title:

Visual-Interactive Similarity Search for Complex Objects by Example of Soccer Player Analysis
Authors:

Jürgen Bernard, Christian Ritter, David Sessler, Matthias Zeppelzauer, Jörn Kohlhammer and Dieter Fellner
Abstract: The definition of similarity is a key prerequisite when analyzing complex data types in data mining, information retrieval, or machine learning. However, the meaningful definition is often hampered by the complexity of data objects and particularly by different notions of subjective similarity latent in targeted user groups. Taking the example of soccer players, we present a visual-interactive system that learns users’ mental models of similarity. In a visual-interactive interface, users are able to label pairs of soccer players with respect to their subjective notion of similarity. Our proposed similarity model automatically learns the respective concept of similarity using an active learning strategy. A visual-interactive retrieval technique is provided to validate the model and to execute downstream retrieval tasks for soccer player analysis. The applicability of the approach is demonstrated in different evaluation strategies, including usage scenarions and cross-validation tests.
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Paper Nr: 19
Title:

Visualizing Temporal Graphs using Visual Rhythms - A Case Study in Soccer Match Analysis
Authors:

Daniele C. Uchoa Maia Rodrigues, Felipe A. Moura, Sergio Augusto Cunha and Ricardo da S. Torres
Abstract: In several applications, a huge amount of graph data have been generated, demanding the creation of appropriate tools for graph visualization. One class of graph data which is attracting a lot of attention recently are the temporal graphs, which encode how objects and their relationships evolve over time. This paper introduces the Graph Visual Rhythm, a novel image-based representation to visualize changing patterns typically found in temporal graphs. The use of visual rhythms is motivated by its capacity of providing a lot of contextual information about graph dynamics in a compact way. We validate the use of graph visual rhythms through the creation of a visual analytics tool to support the decision-making process based on complex-network-oriented soccer match analysis.
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Paper Nr: 22
Title:

Regularized Nonlinear Discriminant Analysis - An Approach to Robust Dimensionality Reduction for Data Visualization
Authors:

Martin Becker, Jens Lippel and André Stuhlsatz
Abstract: We present a novel approach to dimensionality reduction for data visualization that is a combination of two deep neural networks (DNNs) with different objectives. One is a nonlinear generalization of Fisher’s linear discriminant analysis (LDA). It seeks to improve the class separability in the desired feature space, which is a natural strategy to obtain well-clustered visualizations. The other DNN is a deep autoencoder. Here, an encoding and a decoding DNN are optimized simultaneously with respect to the decodability of the features obtained by encoding the data. The idea behind the combined DNN is to use the generalized discriminant analysis as an encoding DNN and to equip it with a regularizing decoding DNN. Regarding data visualization, a well-regularized DNN guarantees to learn sufficiently similar data visualizations for different sets of samples that represent the data approximately equally good. Clearly, such a robustness against fluctuations in the data is essential for real-world applications. We therefore designed two extensive experiments that involve simulated fluctuations in the data. Our results show that the combined DNN is considerably more robust than the generalized discriminant analysis alone. Moreover, we present reconstructions that reveal how the visualizable features look like back in the original data space.
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Paper Nr: 24
Title:

Influence of Mental Models on the Design of Cyber Security Dashboards
Authors:

Janosch Maier, Arne Padmos, Mortaza S. Bargh and Wolfgang Wörndl
Abstract: Governments make cyber security related policies to protect citizens’ interests and national infrastructures against cyber attacks. Cyber security related data can enable evidence based policymaking. Data visualisation via dashboards can help understanding of these cyber security data. Designing such dashboards, however, is not straightforward due to difficulty for potential dashboard users to correctly interpret the displayed information. In this contribution we investigate the use of mental models for correct interpretation of displayed information. Our research question is: How useful are mental models for designing cyber security dashboards? We qualitatively investigate the mental models of seven cyber security experts from a typical governmental organisation. This research shows how operators, analysts and managers have different cyber security mental models. Based on the insight gained on these mental models, we develop a cyber security dashboard to assess the impact of mental models on dashboard design. An experience evaluation shows that the realised dashboard is easy to understand and does not obstruct users. We, however, do not see any meaningful difference in how the experts perceive the dashboard, despite their different cyber security mental models. We propose some directions for future research on using mental models for cyber security dashboard design.
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Paper Nr: 27
Title:

NodeTrix-CommunityHierarchy: Techniques for Finding Hierarchical Communities for Visual Analytics of Small-world Networks
Authors:

Jaya Sreevalsan-Nair and Shivam Agarwal
Abstract: While there are several visualizations of the small world networks (SWN), how does one find an appropriate set of visualizations and data analytic processes in a data science workflow? Hierarchical communities in SWN aid in managing and understanding the complex network better. To enable a visual analytics workflow to probe and uncover hierarchical communities, we propose to use both the network data and metadata (e.g. node and link attributes). Hence, we propose to use the network topology and node-similarity graph using metadata, for knowledge discovery. For the construction of a four-level hierarchy, we detect communities on both the network and the similarity graph, by using specific community detection at specific hierarchical level. We enable the flexibility of finding non-overlapping or overlapping communities, as leaf nodes, by using spectral clustering. We propose NodeTrix-CommunityHierarchy (NTCH), a set of visual analytic techniques for hierarchy construction, visual exploration and quantitative analysis of community detection results. We extend NodeTrix-Multiplex framework (Agarwal et al., 2017), which is for visual analytics of multilayer SWN, to probe hierarchical communities. We propose novel visualizations of overlapping and non-overlapping communities, which are integrated into the framework. We show preliminary results of our case-study of using NTCH on co-authorship networks.
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Paper Nr: 39
Title:

Interpretation of Dimensionally-reduced Crime Data: A Study with Untrained Domain Experts
Authors:

Dominik Jäckle, Florian Stoffel, Sebastian Mittelstädt, Daniel A. Keim and Harald Reiterer
Abstract: Dimensionality reduction (DR) techniques aim to reduce the amount of considered dimensions, yet preserving as much information as possible. According to many visualization researchers, DR results lack interpretability, in particular for domain experts not familiar with machine learning or advanced statistics. Thus, interactive visual methods have been extensively researched for their ability to improve transparency and ease the interpretation of results. However, these methods have primarily been evaluated using case studies and interviews with experts trained in DR. In this paper, we describe a phenomenological analysis investigating if researchers with no or only limited training in machine learning or advanced statistics can interpret the depiction of a data projection and what their incentives are during interaction. We, therefore, developed an interactive system for DR, which unifies mixed data types as they appear in real-world data. Based on this system, we provided data analysts of a Law Enforcement Agency (LEA) with dimensionally-reduced crime data and let them explore and analyze domain-relevant tasks without providing further conceptual information. Results of our study reveal that these untrained experts encounter few difficulties in interpreting the results and drawing conclusions given a domain relevant use case and their experience. We further discuss the results based on collected informal feedback and observations.
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Paper Nr: 40
Title:

Reducing Visual Complexity in Software Maps using Importance-based Aggregation of Nodes
Authors:

Daniel Limberger, Willy Scheibel, Sebastian Hahn and Jürgen Döllner
Abstract: Depicting massive software system data using treemaps can result in visual clutter and increased cognitive load. This paper introduces an adaptive level-of-detail (LoD) technique that uses scoring for interactive aggregation on a per-node basis. The scoring approximates importance by degree-of-interest measures as well as screen and user-interaction scores. The technique adheres to established aggregation guidelines and was evaluated by means of two user studies. The first investigates task completion time in visual search. The second evaluates the readability of the presented nesting level contouring for aggregates. With the adaptive LoD technique software maps allow for multi-resolution depictions of software system information while facilitating annotation and efficient identification of important nodes.
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Paper Nr: 42
Title:

Evaluating the Memorability and Readability of Micro-filter Visualisations
Authors:

Gerwald Tschinkel and Vedran Sabol
Abstract: When using classical search engines, researchers are often confronted with a number of results far beyond what they can realistically manage to read; when this happens, recommender systems can help, by pointing users to the most valuable sources of information. In the course of a long-term research project, research into one area can extend over several days, weeks, or even months. Interruptions are unavoidable, and, when multiple team members have to discuss the status of a project, it’s important to be able to communicate the current research status easily and accurately. Multiple type-specific interactive views can help users identify the results most relevant to their focus of interest. Our recommendation dashboard uses micro-filter visualizations intended to improve the experience of working with multiple active filters, allowing researchers to maintain an overview of their progress. Within this paper, we carry out an evaluation of whether micro-visualizations help to increase the memorability and readability of active filters in comparison to textual filters. Five tasks, quantitative and qualitative questions, and the separate view on the different visualisation types enabled us to gain insights on how micro-visualisations behave and will be discussed throughout the paper.
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Short Papers
Paper Nr: 8
Title:

HistoGlobe - Teaching History Visually
Authors:

Marcus Kossatz, Sebastian Utzig, Simon Schneegans, Felix Lauer, Tobias Westphal, Jens Geelhaar, Bernd Froehlich and Patrick Riehmann
Abstract: HistoGlobe is an interactive historical geographic information system (HGIS) that provides students with gathered and curated historical information for self-study and aids teachers during history classes. The system visually integrates temporal and spatial aspects of historical events, as well as affiliations and alliances of states, routes of historic people and groups, and finally, detailed multimedia information. HistoGlobe relies on familiar interfaces such as globes and timelines but augments them with new techniques including directly manipulable moving entities such as troops and a direction-preserving presentation of treaties and other collaborations based on routing lenses. A field study with 12th graders revealed an overall solid usability of the system and inspired the development of further features.
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Paper Nr: 13
Title:

Towards Enhancing the Visual Analysis of Interdomain Routing
Authors:

Alex Ulmer, Jörn Kohlhammer and Haya Shulman
Abstract: Interdomain routing with Border Gateway Protocol (BGP) plays a critical role in the Internet, determining paths that packets must traverse from a source to a destination. Due to its importance BGP also has a long history of prefix hijack attacks, whereby attackers cause the traffic to take incorrect routes, enabling traffic hijack, monitoring and modification by the attackers. Proposals for securing the protocol are adopted slowly or erroneous. Our goal is to create a novel visual analytics approach that facilitates easy and timely detection of misconfigurations and vulnerabilities both in BGP and in the secure proposals for BGP. This work initiates the analysis of the problem, the target users and state of the art approaches. We provide a comprehensive overview of the BGP threats and describe incidents that happened over the past years. The paper introduces two new user groups beside the network administrators, which should also be addressed in future approaches. It also contributes a survey about visual analysis of interdomain routing with BGP and secure proposals for BGP. The visualization approaches are rated and we derive seven key challenges that arise when following our roadmap for an enhanced visual analysis of interdomain routing.
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Paper Nr: 14
Title:

Approaches and Challenges in the Visual-interactive Comparison of Human Motion Data
Authors:

Jürgen Bernard, Anna Vögele, Reinhard Klein and Dieter Fellner
Abstract: Many analysis goals involving human motion capture (MoCap) data require the comparison of motion patterns. Pioneer works in visual analytics recently recognized visual comparison as substantial for visual-interactive analysis. This work reflects the design space for visual-interactive systems facilitating the visual comparison of human MoCap data, and presents a taxonomy comprising three primary factors, following the general visual analytics process: algorithmic models, visualizations for motion comparison, and back propagation of user feedback. Based on a literature review, relevant visual comparison approaches are discussed. We outline remaining challenges and inspiring works on MoCap data, information visualization, and visual analytics.
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Paper Nr: 17
Title:

Investigating Graph Similarity Perception: A Preliminary Study and Methodological Challenges
Authors:

Tatiana von Landesberger, Margit Pohl, Günter Wallner, Martin Distler and Kathrin Ballweg
Abstract: Graphs have become an indispensable model for representing data in a multitude of domains, including biology, business, financing, and social network analysis. In many of these domains humans are repeatedly confronted with the need to visually compare node-link representations of graphs in order to identify their commonalities or differences. Yet, despite its importance little is known about how much visual differences affect users' perception of graph similarity. As a result, more systematic investigations addressing this issue are necessary. However, from a methodological point of view there are still many open questions regarding the investigation of graph comparisons. To this end, this paper provides an overview of methodological challenges, presents results of an explorative study conducted to identify individual factors influencing the recognition of graph differences, and discusses lessons learned from this study. Our considerations and results can serve as foundation for further studies in this area and can contribute to the comparability of these investigations.
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Paper Nr: 23
Title:

Subspace Clustering and Visualization of Data Streams
Authors:

Ibrahim Louhi, Lydia Boudjeloud-Assala and Thomas Tamisier
Abstract: In this paper, we propose a visual subspace clustering approach for data streams, allowing the user to visually track data stream behavior. Instead of detecting elements changes, the approach shows visually the variables impact on the stream evolution, by visualizing the subspace clustering at different levels in real time. First we apply a clustering on the variables set to obtain subspaces, each subspace consists of homogenous variables subset. Then we cluster the elements within each subspace. The visualization helps to show the approach originality and its usefulness in data streams processing.
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Paper Nr: 28
Title:

Visual Interactive Creation of Geo-located Networks
Authors:

Felix Brodkorb, Manuel Kopp, Arjan Kuijper and Tatiana von Landesberger
Abstract: Nodes in real world networks often have a geographic position. In many cases such as for simulation or optimization, there is a need for non-trivial synthetic geo-located networks. As synthetic datasets are required to have specific properties such as connectivity and geographic distribution, often networks need to be generated. However, their creation is cumbersome if done purely by hand, and inflexible if done fully automated. Here, we present a framework for creating artificial geographic located networks in a visually interactive way. We designed our framework with the what-you-see-is-what-you-get principle in mind, i.e. showing the (intermediate) results of the interactive creation process at any time and allowing the user to adjust the network iteratively. This design allows our system to be also used as a simple viewer for networks that have incomplete location information. Our approach consists of two steps: (1) Creating the network topology and (2) assigning locations to its nodes. Our half automatic system enables the user to easily set the location of the nodes to predefined areas like countries, states, and urban regions, while still being able to flexibly and interactively control the creation process. We show the utility of our system by creating a real-world-like geo-located network.
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Paper Nr: 41
Title:

On Visual Stability and Visual Consistency for Progressive Visual Analytics
Authors:

Marco Angelini and Giuseppe Santucci
Abstract: The emerging field of Progressive Visual Analytics (PVA in what follows) deals with the objective of progressively create the final visualization through a series of intermediate visual results, affected by a degree of uncertainty and, in some cases, a non monotonic behaviour. According to that, it is a critical issue providing the user with no confusing visualization and that results in a novel point of view on stability and consistency. This position paper deals with the novel and challenging issues that PVA poses in term of visual stability and consistency, providing a preliminary framework in which this problem can be contextualized, measured, and formalized. In particular, the framework proposes a set of metrics, able to explore both data and visual changes; a preliminary case study demonstrates their applicability and advantages in adequately representing data changes in a visualization.
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Paper Nr: 33
Title:

PGX.UI: Visual Construction and Exploration of Large Property Graphs
Authors:

Julia Kindelsberger, Daniel Langerenken, Malte Husmann, Korbinian Schmid and Hassan Chafi
Abstract: Transforming existing data into graph formats and visualizing large graphs in a comprehensible way are two key areas of interest of information visualization. Addressing these issues requires new visualization approaches for large graphs that support users with graph construction and exploration. In addition, graph visualization is becoming more important for existing graph processing systems, which are often based on the property graph model. Therefore this paper presents concepts for visually constructing property graphs from data sources and a summary visualization for large property graphs. Furthermore, we introduce the concept of a graph construction time line that keeps track of changes and provides branching and merging, in a version control like fashion. Finally, we present a tool that visually guides users through the graph construction and exploration process.
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Paper Nr: 34
Title:

Cluster-based Edge Bundling based on a Line Graph
Authors:

Takafumi Yamashita and Ryosuke Saga
Abstract: Information visualization enables simple and intuitive understanding of data. Edge bundling is a visualization technique and is beneficial for visual analysis. By transforming data into a network diagram, the relationships among data can be recognized intuitively. In such situation, edge bundling reduces the visual clutter by bundling the edges on the basis of several approaches. Results show the bundles of edges are organized in a few relationships. In other words, the bundles can be regarded as clusters of edges. Therefore, we propose a new bundling method based on edge clustering. By changing a network into a line graph, edges can be regarded as nodes such that several node clustering methods can be applied to edge clustering. We bundle edges on the basis of the result of edge clustering. This approach is a novel concept of edge bundling and edge clustering. Using the proposed method, most edges are clearly bundled whereas a few edges belonging to different clusters are not bundled.
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Paper Nr: 35
Title:

Visualization of Customer Expectations from Web Text using Co-Occurrence Graph and Auto-labeling in the Service Market
Authors:

Ryosuke Saga, Naoaki Ohkusa, Takafumi Yamashita and Nahomi Maki
Abstract: This study describes the visualization of customer expectations using the service science domain. Customer expectations influence service quality and are considered important factors for user evaluation of services. Customer expectations are constructed from word of mouth, rumors, and user experience. Investigation using a questionnaire is useful in comprehending customer expectations, but this method is costly and time consuming. In this research, we extract customer expectations from Web text consisting of massive word-of-mouth data and visualize them using a co-occurrence graph. In addition, we apply clustering and auto-labeling methods to easily understand the results of the co-occurrence graph. In the case study of a coffee service, we are able extract topics related to customer expectations, but labeling methods are still subject to improvement.
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Area 2 - General Data Visualization

Full Papers
Paper Nr: 12
Title:

Relative Direction Change - A Topology-based Metric for Layout Stability in Treemaps
Authors:

Sebastian Hahn, Joseph Bethge and Jürgen Döllner
Abstract: This paper presents a topology-based metric for layout stability in treemaps—the Relative Direction Change (RDC). The presented metric considers the adjacency and arrangement of single shapes in a treemap, and allows for a rotation-invariant description of layout changes between two snapshots of a dataset depicted with treemaps. A user study was conducted that shows the applicability of the Relative Direction Change in comparison and addition to established layout metrics, such as Average Distance Change (ADC) and Average Aspect Ratio (AAR), with respect to human perception of treemaps. This work contributes to the establishment of a more precise model for the replicable and reliable comparison of treemap layout algorithms.
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Paper Nr: 31
Title:

WAVE: A 3D Online Previewing Framework for Big Data Archives
Authors:

Nicholas Tan Jerome, Suren Chilingaryan, Andrei Shkarin, Andreas Kopmann, Michael Zapf, Alexander Lizin and Till Bergmann
Abstract: With data sets growing beyond terabytes or even petabytes in scientific experiments, there is a trend of keeping data at storage facilities and providing remote cloud-based services for analysis. However, accessing these data sets remotely is cumbersome due to additional network latency and incomplete metadata description. To ease data browsing on remote data archives, our WAVE framework applies an intelligent cache management to provide scientists with a visual feedback on the large data set interactively. In this paper, we present methods to reduce the data set size while preserving visual quality. Our framework supports volume rendering and surface rendering for data inspection and analysis. Furthermore, we enable a zoom-on-demand approach, where a selected volumetric region is reloaded with higher details. Finally, we evaluated theWAVE framework using a data set from the entomology science research.
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Short Papers
Paper Nr: 25
Title:

A Review on Visualization Recommendation Strategies
Authors:

Pawandeep Kaur and Michael Owonibi
Abstract: Choosing the best visualization of a given dataset becomes more and more complex as not only the amount of data, but also the number of visualization types and the number of potential uses of visualizations grow tremendously. This challenge has spurred on the research into visualization recommendation systems. The ultimate aim of such a system is the suggestion of visualizations which provide interesting insights into the data. It should ideally consider data characteristics, domain knowledge and individual preferences to produce aesthetically appealing and easy to understand charts. Based on the mentioned factors, we have reviewed in this paper the state-of-the-art in visualization recommendation systems starting from the earliest attempt made on this subject. We identify challenges to visualization and visualization recommendation to guide future research directions.
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Paper Nr: 26
Title:

Visualizing Ergonomic Data of Industrial Work Processes: A Design Study
Authors:

Walentin Heft, Linda Pfeiffer and Paul Rosenthal
Abstract: Ergonomics deals with the analysis and design of work processes. To identify ergonomically critical situations, appropriate evaluation options have to be developed to enable an efficient analysis process. Nowadays, such analyses are typically carried out with the help of digital models of the surrounding area and virtual humans. These produce a multitude of geo-referenced and time-oriented data. We present a design study on how to visualize this data to support the ergonomically analysis process optimally. As consequence of a thorough requirements analysis and design process, we propose a novel interactive visualization which provides the user an overview of ergonomically critical situations and their causes. Simultaneously, the user obtains the main stress factors in a compressed form by a glyph-based visual design. Final expert interviews and a usability study depict the utility of the proposed visualization tool.
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Paper Nr: 30
Title:

A Strategy for Automating the Presentation of Statistical Graphics for Users without Data Visualization Expertise - A Position Paper
Authors:

Pere Millán-Martínez and Pedro Valero-Mora
Abstract: The growing need to convert the data in databases into knowledge for a public without data visualization expertise requires the ever more precise selection of graphics to be presented to the user for consideration. This can be achieved through a more detailed characterization of the data as well as the data visualization task that the user wishes to accomplish. One way to limit the number of possible graphics based on the data is to characterize the multiple properties that can be described for each variable represented by a column of data. This paper presents seven dimensions with their respective levels that can serve as a framework for classifying statistical graphics such that their effectiveness in performing a given task may then be evaluated.
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Paper Nr: 37
Title:

Inspiration from VR Gaming Technology: Deep Immersion and Realistic Interaction for Scientific Visualization
Authors:

Till Bergmann, Matthias Balzer, Torsten Hopp, Thomas van de Kamp, Andreas Kopmann, Nicholas Tan Jerome and Michael Zapf
Abstract: The computer gaming industry is traditionally the moving power and spirit in the development of computer visualization hardware and software. This year, affordable and high quality virtual reality headsets became available and the science community is eager to get benefit from it. This paper describes first experiences in adapting the new hardware for three different visualization use cases. In all three examples existing visualization pipelines were extended by virtual reality technology. We describe our approach, based on the HTC Vive VR headset, the open source software Blender and the Unreal Engine 4 game engine. The use cases are from three different fields: large-scale particle physics research, X-ray-imaging for entomology research and medical imaging with ultrasound computer tomography. Finally we discuss benefits and limits of the current virtual reality technology and present an outlook to future developments.
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Paper Nr: 15
Title:

Adapting Heuristic Evaluation to Information Visualization - A Method for Defining a Heuristic Set by Heuristic Grouping
Authors:

Maurício Rossi de Oliveira and Celmar Guimarães da Silva
Abstract: Heuristic evaluation technique is a classical evaluation method in Human-Computer Interaction area. Researchers and software developers broadly use it, given that it is fast, cheap and easy to use. Using it in other areas demands creating a new heuristic set able to identify common problems of these areas. Information Visualization (InfoVis) researchers commonly use this technique with the original usability heuristic set proposed by Nielsen, which does not cover many relevant aspects of InfoVis. InfoVis literature presents sets of guidelines that cover InfoVis concepts, but it does not present most of them as heuristics, or they cover much specific concepts. This work presents a method for defining a set of InfoVis heuristics for use in heuristic evaluation. The method clusters heuristics and guidelines found in a literature review, and creates a new heuristic set based on each group. As a result, we created a new set of 15 generic heuristics, from a set of 62 ones, which we hypothesize that will help evaluators to take into account a broad set of visualization aspects during evaluation with possibly less cognitive effort.
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Paper Nr: 16
Title:

A Clustering-based Visual Analysis Tool for Genetic Algorithm
Authors:

Habib Daneshpajouh and Nordin Zakaria
Abstract: While Genetic Algorithm (GA) is a powerful tool for combinatorial optimization, the vast population of candidate solutions it typically deploys and algorithm’s intrinsic randomness lead to difficulty in understanding its search behavior. We discuss in this paper a clustering-based visualization tool for GA that attempts to mediate this problem. GA population across its entire generations are clustered, and each cluster and its individuals are mapped to a visual symbol. The tool enables a GA researcher or user to understand better the behavior of a GA run, specifically the local searches it performs in its global exploration to go from one generation to another.
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Paper Nr: 32
Title:

The MEDEA Experiment - Can You Accelerate Simulation-based Learning by Combining Information Visualization and Interaction Design Principles?
Authors:

Christopher J. Garasi, Richard R. Drake, John-Mark Collins, Rafael Picco and Benjamin E. Hankin
Abstract: The intent of the multipurpose display engineering analysis (MEDEA) experiment was to apply the principles of computer-mediated learning and “play” in the context of high-performance computing (HPC) modeling analysis. Our approach involved the development of software workflow based on interaction design principles using a team of graphic artists, experts in graphics- and touch-based displays, computer programmers, and scientists. The desired outcome was to develop software to overcome perceived HPC modeling usage and learning barriers common to scientific modeling and visualization. Using multiple interaction types, a variety of user workflow experiences were captured (novice/learner, analyst, expert) resulting in a more intuitive and enjoyable experience with a workflow which fosters accelerated learning.
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Paper Nr: 36
Title:

Multisensory Analytics: Case of Visual-auditory Analysis of Scalar Fields
Authors:

E. Malikova, V. Pilyugin, V. Adzhiev, G. Pasko and A. Pasko
Abstract: A well-known definition of visualization is the mapping of initial data to a visual representation, which can be perceived and interpreted by humans. Human senses include not only vision, but also hearing, sense of touch, smell and others including their combinations. Visual analytics and its more general version that we call Multisensory Analytics are areas that consider visualization as one of its components. We present a particular case of the multisensory analytics with a hybrid visual-auditory representation of data to show how auditory display can be used in the context of data analysis. Some generalizations based on using real-valued vector functions for solving data analysis problems by means of multisensory analytics are proposed. These generalizations might be considered as a first step to formalization of the correspondence between the initial data and various sensory stimuli. An illustration of our approach with a case study of analysis of a scalar field using both visual and auditory data representations is given.
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Area 3 - Spatial Data Visualization

Full Papers
Paper Nr: 5
Title:

Transportation-based Visualization of Energy Conversion
Authors:

Oliver Fernandes, Steffen Frey and Thomas Ertl
Abstract: We present a novel technique to visualize the transport of and conversion between internal and kinetic energy in compressible flow data. While the distribution of energy can be directly derived from flow state variables (e.g., velocity, pressure and temperature) for each time step individually, there is no information regarding the involved transportation and conversion processes. To visualize these, we model the energy transportation problem as a graph that can be solved by a minimum cost flow algorithm, inherently respecting energy conservation. In doing this, we explicitly consider various simulation parameters like boundary conditions and energy transport mechanisms. Based on the resulting flux, we then derive a local measure for the conversion between energy forms using the distribution of internal and kinetic energy. To examine this data, we employ different visual mapping techniques that are specifically targeted towards different research questions. In particular, we introduce glyphs for visualizing local energy transport, which we place adaptively based on conversion rates to mitigate issues due to clutter and occlusion. We finally evaluate our approach by means of data sets from different simulation codes and feedback by a domain scientist.
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Paper Nr: 20
Title:

Interactive Visualization of DICOM Volumetric Datasets in the Web - Providing VR Experiences within the Web Browser
Authors:

Ander Arbelaiz, Aitor Moreno, Luis Kabongo, Helen V. Diez and Alejandro García Alonso
Abstract: Recently the possibility to visualize interactively volumetric datasets in the Web has opened new methods of exploration and sharing of 3D images coming from different fields. At the same time, VR technologies are gaining momentum in the society, where several HMD’s are ready to be bought. This paper presents how volumetric datasets represented as DICOM images can be loaded and visualized interactively in a WebVR compatible setup. DICOM images are loaded from local or remote repositories into X3D volume rendering nodes, which are displayed in the VR devices using WebVR technology. The results show that WebVR and X3D are compatible web technologies that can be joined together to provide easy and extensible tools to interact with DICOM datasets. Some enhancements for the interactive VR and non-VR experiences are presented.
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Short Papers
Paper Nr: 21
Title:

Open Data Sources for 3D Data Visualisation - Generating 3D Worlds based on OpenStreetMaps Data
Authors:

Almar Joling
Abstract: Georeferenced data is becoming increasingly more available through open source licenses. In this paper, an approach is explained to build a real-time interactive 3D virtual world using the Unity 3D engine by using the freely available OpenStreetMaps data. This virtual environment can serve as a base for the visualisations of spatial and georeferenced data. By making use of OpenStreetMaps this virtual environment can be kept up to date with changes in the world. This paper provides an introduction to OpenStreetMaps, discusses some of the challenges and provides examples how to process this data in order to generate a virtual environment.
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