IVAPP 2020 Abstracts


Area 1 - Abstract Data Visualization

Full Papers
Paper Nr: 4
Title:

LilyPads: Exploring the Spatiotemporal Dissemination of Historical Newspaper Articles

Authors:

Max Franke, Markus John, Moritz Knabben, Jana Keck, Tanja Blascheck and Steffen Koch

Abstract: Today, libraries provide digitized collections of historical newspapers, which researchers in the humanities seek to analyze. An important objective of this work is to enable researchers to overview and analyze the textual, temporal and geographical dissemination of an event expressed in document corpora of interest. For this, we propose LilyPads, which permits researchers to analyze such corpora using a novel, map-inset-based approach. In contrast to previous work, LilyPads is centered around one main view, which integrates key aspects of the visualized data, thereby facilitating an explorative approach to finding relationships in data. From LilyPads’ overview, researchers can select subsets of data as well as individual documents interactively, which supports detailed analysis of the corpus, combining close and distant reading methods. We show the applicability of LilyPads by demonstrating its use in a real-world analysis scenario.

Paper Nr: 7
Title:

Improving Neural Network-based Multidimensional Projections

Authors:

Mateus Espadoto, Nina T. Hirata, Alexandre X. Falcão and Alexandru C. Telea

Abstract: Dimensionality reduction methods are often used to explore multidimensional data in data science and information visualization. Techniques of the SNE-class, such as t-SNE, have become the standard for data exploration due to their good visual cluster separation, but are computationally expensive and don’t have out-of-sample capability by default. Recently, a neural network-based technique was proposed, which adds out-of-sample capability to t-SNE with good results, but with the disavantage of introducing some diffusion of the points in the result. In this paper we evaluate many neural network-tuning strategies to improve the results of this technique. We show that a careful selection of network architecture, loss function and data augmentation strategy can improve results.

Paper Nr: 15
Title:

Visual Exploration of 3D Shape Databases Via Feature Selection

Authors:

Xingyu Chen, Guangping Zeng, Jiří Kosinka and Alexandru Telea

Abstract: : We present a visual analytics approach for constructing effective visual representations of 3D shape databases as projections of multidimensional feature vectors extracted from their shapes. We present several methods to construct effective projections in which different-class shapes are well separated from each other. First, we propose a greedy heuristic for searching for near-optimal projections in the space of feature combinations. Next, we show how human insight can improve the quality of the constructed projections by iteratively identifying and selecting a small subset features that are responsible for characterizing different classes. Our methods allow users to construct high-quality projections with low effort, to explain these projections in terms of the contribution of different features, and to identify both useful features and features that work adversely for the separation task. We demonstrate our approach on a real-world 3D shape database.

Paper Nr: 24
Title:

AnnoXplorer: A Scalable, Integrated Approach for the Visual Analysis of Text Annotations

Authors:

Martin Baumann, Harutyun Minasyan, Steffen Koch, Kuno Kurzhals and Thomas Ertl

Abstract: Text annotation data in terms of a series of tagged text segments can pose scalability challenges within the dimensions of quantity (long texts bearing many annotations), configuration (overlapping annotations or annotations with multiple tags), or source (annotations by multiple annotators). Accordingly, exploration tasks such as navigating within a long annotated text, recognizing patterns in the annotation data or assessing differences between annotators can be demanding. Our approach of an annotation browser deals with all of these data and task challenges simultaneously by providing a continuous range of views on large amounts of complex annotation data from multiple sources. We achieve this by using a combined geometric/semantic zooming mechanism that operates on an abstract representation of the sequence of a text’s tokens and the annotations thereupon, which is interlinked with a view on the text itself. The approach was developed in the context of a joint project with researchers from fields concerned with textual sources. We derive our approach’s requirements from a series of tasks that are typical in natural language processing and digital humanities, show how it supports these tasks, and discuss it in the light of the feedback we got from our domain experts.

Paper Nr: 28
Title:

musiXplora: Visual Analysis of a Musicological Encyclopedia

Authors:

Richard Khulusi, Jakob Kusnick, Josef Focht and Stefan Jänicke

Abstract: Making large sets of digitized cultural heritage data accessible is a key task for digitization projects. While the amount of data available through print media is vast in humanities, common issues arise as information available for the digitization process is typically fragmented. One reason is the physical distribution of data through print media that has to be collected and merged. Especially, merging causes issues due to differences in terminology, hampering automatic processing. Hence, digitizing musicological data raises a broad range of challenges. In this paper, we present the current state of the on-going musiXplora project, including a multi-faceted database and a visual exploration system for persons, places, objects, terms, media, events, and institutions of musicological interest. A particular focus of the project is using visualizations to overcome traditional problems of handling both, vast amounts and anomalies of information induced by the historicity of data. We present several use cases that highlight the capabilities of the system to support musicologists in their daily workflows.

Paper Nr: 48
Title:

Visualizing Learning Space in Neural Network Hidden Layers

Authors:

Gabriel D. Cantareira, Fernando V. Paulovich and Elham Etemad

Abstract: Analyzing and understanding how abstract representations of data are formed inside deep neural networks is a complex task. Among the different methods that have been developed to tackle this problem, multidimensional projection techniques have attained positive results in displaying the relationships between data instances, network layers or class features. However, these techniques are often static and lack a way to properly keep a stable space between observations and properly convey flow in such space. In this paper, we employ different dimensionality reduction techniques to create a visual space where the flow of information inside hidden layers can come to light. We discuss the application of each used tool and provide experiments that show how they can be combined to highlight new information about neural network optimization processes.

Short Papers
Paper Nr: 10
Title:

Towards Collaborative and Dynamic Software Visualization in VR

Authors:

Florian Jung, Veronika Dashuber and Michael Philippsen

Abstract: To improve comprehension and maintenance of distributed software systems, some software visualization tools already bundle relevant information in a graphical way, but they either focus on the static structures and dependencies, or on tracing information. Our novel visual representation lifts the software city metaphor into VR and jointly addresses both static and dynamic behavioral aspects, such as call traces of microservice based systems. Users can navigate both the traces in the time domain and the static structure in the spatial domain. They can also collaborate with other developers. We argue that our 3D visualization provides the engineer with a better grasp on relevant information. With a controlled experiment we evaluated its user acceptance.

Paper Nr: 23
Title:

Time-series Visualization of Twitter Trends

Authors:

Atsuro Konishi and Hiroshi Hosobe

Abstract: Twitter provides a function called “trend” that presents popular words and hashtags. Typically, one trend word or hashtag is related to thousands of tweets. It is difficult to understand such thousands of tweets in a short time by using the standard sort methods and the standard display method provided by Twitter. Most of previous studies analyzed and visualized tweets by using text-based clustering methods. However, these methods suffer from the accuracy of clustering results, because a typical tweet has only poor textual information. This paper presents a Twitter trend analysis system that combines retweet clustering and time-series visualization to allow users to understand flows of topics in a Twitter trend in a short time. This system also provides a list of effective legends and a display of individual tweets with photos in order for users to further understand topics in a trend. To illustrate the effectiveness of this system, this paper presents the results of experiments on the analysis of Twitter trends related to a popular sport event and a popular music program.

Paper Nr: 34
Title:

Deep Dive into Deep Neural Networks with Flows

Authors:

Adrien Halnaut, Romain Giot, Romain Bourqui and David Auber

Abstract: Deep neural networks are becoming omnipresent in reason of their growing popularity in media and their daily use. However, their global complexity makes them hard to understand which emphasizes their black-box aspect and the lack of confidence given by their potential users. The use of tailored visual and interactive representations is one way to improve their explainability and trustworthy. Inspired by parallel coordinates and Sankey diagrams, this paper proposes a novel visual representation allowing tracing the progressive classification of a trained classification neural network by examining how each evaluation data is being processed by each network’s layer. It is thus possible to observe which data classes are quickly recognized, unstable, or lately recognized. Such information provides insights to the user about the model architecture’s pertinence and can guide on its improvement. The method has been validated on two classification neural networks inspired from the literature (LeNet5 and VGG16) using two public databases (MNIST and FashionMNIST).

Paper Nr: 42
Title:

A Taxonomy of Treemap Visualization Techniques

Authors:

Willy Scheibel, Matthias Trapp, Daniel Limberger and Jürgen Döllner

Abstract: A treemap is a visualization that has been specifically designed to facilitate the exploration of tree-structured data and, more general, hierarchically structured data. The family of visualization techniques that use a visual metaphor for parent-child relationships based “on the property of containment” (Johnson, 1993) is commonly referred to as treemaps. However, as the number of variations of treemaps grows, it becomes increasingly important to distinguish clearly between techniques and their specific characteristics. This paper proposes to discern between Space-filling Treemap TS, Containment Treemap TC, Implicit Edge Representation Tree TIE, and Mapped Tree TMT for classification of hierarchy visualization techniques and highlights their respective properties. This taxonomy is created as a hyponymy, i.e., its classes have an is-a relationship to one another: TS TC TIE TMT. With this proposal, we intend to stimulate a discussion on a more unambiguous classification of treemaps and, furthermore, broaden what is understood by the concept of treemap itself.

Paper Nr: 44
Title:

Interactive System Architecture Exploration: Case Studies with the IMiGEr Tool

Authors:

Premek Brada, Richard Lipka, Lukas Holy and Kamil Jezek

Abstract: Software systems of all kinds tend to be complex, easily comprising hundreds of components of various types and many more interconnections. Understanding of their internal structure through appropriate visualization is, therefore, a challenging task, especially when hierarchical decomposition is not possible. Among the key hindrances in existing graph-based visualizations of such systems are visual clutter and the contradictory requirements of ideally seeing the whole system context while showing enough details to analyze particular elements. Addressing such issues to enable effective comprehension of large multi-modal graphs, we developed a method of their exploration leaning on user interaction with the diagram and details on demand principle, implemented in IMiGEr. In this paper, we show the key techniques it employs, explain their combination and illustrate the benefits on the representative tasks in software architecture understanding.

Paper Nr: 8
Title:

Visualization to Assist Interpretation of the Multilevel Paradigm in Bipartite Graphs

Authors:

Diego S. Cintra, Alan Valejo, Alneu A. Lopes and Maria F. Oliveira

Abstract: Multilevel methods refer to a general framework for solving optimization problems in large graphs considering a hierarchy of contracted representations of the target graph. A recent extension to bipartite graphs has been introduced and successfully employed in diverse applications, but experience suggests the method is highly susceptible to the choice of vertex matching strategy for graph contraction and on whether the super-vertices are relevant generalizations to the problem addressed. Although the flexibility in obtaining contracted representations of an original graph is a potential advantage, appropriate choice and parameterization of the contracting algorithms is challenging. Experts would benefit from solutions capable of assisting them in assessing alternatives and making informed decisions. In this work we describe a visualization system that creates an interactive graphical representation of a multilevel graph hierarchy obtained as a result of executing a multilevel method on bipartite graphs. We provide illustrative case studies showing the proposed visualization can support algorithm developers in inspecting and interpreting how different parameter choices in the coarsening stage impact the resulting multilevel hierarchies.

Paper Nr: 11
Title:

Combining Image and Caption Analysis for Classifying Charts in Biodiversity Texts

Authors:

Pawandeep Kaur and Dora Kiesel

Abstract: Chart type classification through caption analysis is a new area of study. Distinct keywords in the captions that relate to the visualization vocabulary (e.g., for scatterplot: dot, y-axis, x-axis, bubble) and keywords from the specific domain (e.g., species richness, species abundance, phylogenetic associations in the case of biodiversity research), serve as parameters to train a text classifier. For better chart comprehensibility, along with the visual characteristics of the chart, a classifier should also understand these parameters well. Such conceptual/semantic chart classifiers then will not only be useful for chart classification purposes but also for other visualization studies. One of the applications of such a classifier is in the creation of the domain knowledge-assisted visualization recommendation system, where these text classifiers can provide the recommendation of visualization types based on the classification of the text provided along with the dataset. Motivated by this use case, in this paper, we have explored our idea of semantic chart classifiers. We have taken the assistance of state-of-the-art natural language processing (NLP) and computer vision algorithms to create a biodiversity domain-based visualization classifier. With an average test accuracy (F1-score) of 92.2% over all 15 classes, we can prove that our classifiers can differentiate between different chart types conceptually and visually.

Paper Nr: 14
Title:

Interactive 3D Visualization of Network Traffic in Time for Forensic Analysis

Authors:

Daniel Clark and Benjamin Turnbull

Abstract: This paper outlines a novel approach to 3D visualization of network traffic. Existing approaches, which present node-graphs in 3D space may not be making the best use of the advantages of 3D. By combining the time component of network traffic data with nodal information and displaying these on separate planes it should be possible to provide analysts with insights that go beyond just the nodal information. The goal of allowing analysts to quickly form a mental map that corresponds with the network traffic ground truth may be achieved with this approach. The visualization approach is demonstrated through development of a tool which implements the approach and discusses its application to a recent network forensics challenge.

Paper Nr: 30
Title:

A Genetic Algorithm Optimising Control Point Placement for Edge Bundling

Authors:

Ryosuke Saga, Tomoki Yoshikawa, Ken Wakita, Ken Sakamoto, Gerald Schaefer and Tomoharu Nakashima

Abstract: This paper describes a novel approach of edge bundling that employs a genetic algorithm (GA) to optimise the placement of control points. Edge bundling is a useful technique to reduce visual clutter and a number of model-based edge bundling approaches have been introduced in the literature. However, these do not attempt to optimise aesthetic rules directly. Differently from them, our approach assumes that edge bundling is regarded as an optimisation problem for aesthetic rules. To solve this problem, we present an GA-based algorithm where gene representation defines control points of edges in order to allow flexibility and the fitness function is defined based on quantitative criteria for edge bundling. Experimental results using a visualisation of a Japanese airline map demonstrates the feasibility of our proposed method and its usability.

Paper Nr: 31
Title:

Visualization of Data for Decision Making in a University

Authors:

Gabriela Cruz-Guzmán and Lorna V. Rosas-Téllez

Abstract: The management of a large amount of information generated from different media is chaotic when there is no technological tool that standardizes and organizes the data provided by different users. The present work shows a web system that allows to store in a database the information of the research products that each year the researchers of the institution perform, thus simplifying and improving information management, in order to support the making decision based in the follow-up of the projects and activities of investigation of the researchers. The system records and displays the changes made by researchers and allows generating the visualization of data, providing an easier and faster way to see and understand trends, outliers and patterns in the data which is essential for analyzing information and making decisions based on the data.

Paper Nr: 41
Title:

A New Algorithm using Independent Components for Classification and Prediction of High Dimensional Data

Authors:

Subhajit Chakrabarty and Haim Levkowitz

Abstract: Dimensionality reduction of high-dimensional data is often desirable, in particular where data analysis includes visualization – an ever more common scenario nowadays. Principal Component Analysis, and more recently Independent Component Analysis (ICA) are among the most common approaches. ICA may output components that are redundant. Interpretation of such groups of independent components may be achieved through application to tasks such as classification, regression, and visualization. One major problem is that grouping of independent components for high-dimensional time series is difficult. Our objective is to provide a comparative analysis using independent components for given grouping and prediction tasks related to high-dimensional time series. Our contribution is that we have developed a novel semi-supervised procedure for classification. This also provides consistency to the overall ICA result. We have conducted a comparative performance analysis for classification and prediction tasks on time series. This research has a broader impact on all kinds of ICA applied in several domains, including bio-medical sensors (such as electroencephalogram), astronomy, financial time series, environment and remote sensing.

Area 2 - General Data Visualization

Full Papers
Paper Nr: 33
Title:

Teaching on the Intersection of Visualization and Digital Humanities

Authors:

Stefan Jänicke

Abstract: Visualization as a means to generate hypotheses and to communicate insights on digitized cultural heritage data sets has become more and more important in the recent years. While many digital humanities researchers transform their data in order to be processed with ready-to-use tools, others engage in interdisciplinary collaborations with visualization scholars aiming to design novel solutions and interactive visual interfaces as occurring data features are more carefully mapped to visual attributes. Many of those collaborations suffer from loss of time at the beginning of the project as scholars with diverse research backgrounds require to understand each others’ mindsets, ways of thinking and research interests. This paper reports on three years of teaching visualization design for digital humanities projects. It provides an overview of theoretical course contents, practical training, collaboration setups—involving computer science, digital humanities as well as humanities students who experienced typical collaboration obstacles—and remarkable project results.

Short Papers
Paper Nr: 6
Title:

Curtain Graphs: Using a Floating Baseline for Comparison in a Two-dimensional Graphical Space

Authors:

Kassandra Raymond and Andrew Hamilton-Wright

Abstract: We present a novel visualization tool designed to provide support for the analysis of data sets focused around deviation from a baseline and including data from multiple series. The incorporation of a floating baseline makes the curtain graph distinct from waterfall plots and bar charts. Each data series therefore has a visual anchor that assists in interpretability, gives focus, and provides a means for easily broadening analysis across all presented series. The use of this tool in real-world examples based on relative and absolute comparisons is discussed.

Paper Nr: 9
Title:

DataShiftExplorer: Visualizing and Comparing Change in Multidimensional Data for Supervised Learning

Authors:

Bruno Schneider, Daniel A. Keim and Mennatallah El-Assady

Abstract: In supervised learning, to ensure the model's validity, it is essential to identify dataset shifts, i.e., when the data distribution changes from the one the model encountered at the time of training. To detect such changes, a comparative analysis of the multidimensional data distributions of the training data and new, unseen datasets is required. In this paper, we span the design space of visualizations for multidimensional comparative data analytics. Based on this design space, we present DataShiftExplorer, a technique tailored to identify and analyze the change in multidimensional data distributions. Throughout examples, we show how DataShiftExplorer facilitates the identification and analysis of data changes, supporting supervised learning.

Paper Nr: 12
Title:

Assessing the Feasibility of using Augmented Reality to Visualize Interventional Radiology Imagery

Authors:

Christopher Bartlett, Noelle LeRoy, Damian Schofield, Jonathan Ford and Summer Decker

Abstract: Image-guided procedures, such as those in radiology, are frequently reliant on data which is visualized on traditional monitors. In an operating theatre, these monitors are often placed at poor ergonomic positions, causing physicians to rotate their heads to the side while their hands are working before them. This study seeks to investigate whether visualizing data on an augmented reality headset that projects an image in front of the participant will reduce task-time and increase efficiency. The primary purpose behind this study is to alleviate neck and back pain in physicians performing data/image guided procedures. A number of augmented reality headsets were tested in a clinical setting and a number of experiments were undertaken to test the viability of this technology in an operating theatre. The experiment consisted of comparing the use of an augmented reality headset against a computer monitor while performing tasks that required similar hand eye co-ordination to that needed during a surgery. The research hypothesized that the use of an augmented reality headset would increase accuracy and efficiency; while decreasing eye fatigue and neck/back pain.

Paper Nr: 16
Title:

Visual Analysis of Billiard Dynamics Simulation Ensembles

Authors:

Stefan Boshe-Plois, Quynh Q. Ngo, Peter Albers and Lars Linsen

Abstract: Mathematical billiards assume a table of a certain shape and dynamical rules for handling collisions. Some trajectories exhibit distinguished patterns. Detecting such trajectories manually for a given billiard is cumbersome, especially, when assuming an ensemble of billiards with different parameter settings. We propose a visual analysis approach for simulation ensembles of billiard dynamics based on phase-space visualizations and multi-dimensional scaling. We apply our methods to the well-studied approach of dynamical billiards for validation and to the novel approach of symplectic billiards for new observations.

Paper Nr: 39
Title:

What ‘Work’ Can Dataviz Do in Popular Science Communication?

Authors:

Martin Engebretsen

Abstract: Data visualizations have proliferated on public arenas for information and communication – in journalism, PR and governmental information as well as in popular science communication (PSC). In the existing literature on PSC, simplicity, relevance and trust are identified as critical factors for the communication to succeed. This position paper argue that data visualizations represent a semiotic resource with unique potentials regarding all of these criteria. The paper aims at presenting a theoretical and methodological framework for studying data visualizations applied in popular science discourses. The main goals are a) to introduce social semiotics as an advanced analytical tool for the scrutiny of data visualizations, b) to introduce PUS (Public understanding of science) as a field relevant for empirical studies of data visualization, and c) to present a method of analysis combining a small scale corpus analysis with multimodal close reading of selected visualizations.

Paper Nr: 49
Title:

Dynamic Collaborative Visualization of the United Nations Sustainable Development Goals (SDGs): Creating an SDG Dashboard for Reporting and Best Practice Sharing

Authors:

Kathleen C. Garwood, David Steingard and Marcello Balduccini

Abstract: Dynamic data visualization is a collaborative dashboarding methodology used to identify trends and insights in data while revealing changes in activity and work progress. This paper introduces a dashboard technique that collects, reports, and shares global business schools’ fulfillment of the United Nations Sustainable Development Goals (SDGs)--the SDG Dashboard. With this tool, business schools can share experiences with the goal of promoting sustainable change and advancing the work (e.g. research papers, partnerships, and syllabi) being done internationally within schools. By revealing patterns and trends that may not be evident when reading individual school level accounts of SDG alignment, this dashboard was created to promote inter-school collaboration while highlighting best practices. Overall, it can be used as a high-level assessment tool to highlight areas of greatest impact on the SDGs as well as opportunities for growth. The SDG Dashboard allows users to drill down into the data, revealing patterns of global impact, while also highlighting the breadth of work that is being done. This dynamic dashboard is an agent for collaborations in all topics outlined in the United Nations Sustainable Development Goals, which are: sustainable economic growth, responsible consumption and production, availability for decent work, poverty eradication, cleaner energy, environmental conservation, and the foray of issues concerning overall inequality and quality education.

Paper Nr: 51
Title:

GlyphSOMe: Using SOM with Data Glyphs for Customer Profiling

Authors:

Catarina Maçãs, Evgheni Polisciuc and Penousal Machado

Abstract: With the possibility of storing customer data, retail companies can improve their marketing strategies, creating promotions and special offers specific for individual customers. The application of information visualisation combined with machine learning methods can facilitate the tasks related to customer profiling, and therefore, the creation of individualised campaigns. More specifically, we argue that clustering and segmentation methods, in particular SOM algorithms, foster customer characterisation by defining a shopping topology that can distinguish different patterns of consumption. Furthermore, we believe that adding visual descriptors of the shopping behaviours through the means of data glyphs, can further improve the efficiency and efficacy of SOMs. We present a visualisation method that combines SOMs and data glyphs, with an ultimate goal to reveal purchasing patterns of individual customers. Additionally, we apply two SOM projections: the traditional matrix projection, and a novel force-directed projection, for a more detailed view over the clusters of the SOM.

Paper Nr: 52
Title:

Usage of Visualization Techniques in Data Science Workflows

Authors:

Johanna Schmidt

Abstract: The increasing interest in data science and data analytics lead to a growing interest in data visualization and exploratory visual data analysis. However, there is still a clear gap between new developments in visualization research, and the visualization techniques currently applied in data analytics workflows. Most of the commonly used tools provide basic charting options, but more advanced visualization techniques have hardly been integrated as features yet. This especially applies for interactive exploratory data analysis, which has already been addressed as the ’Interactive Visualization Gap’ in the literature. In this paper we present a study on the usage of visualization techniques in common data science tools. The results of the study confirm that the gap still exists. For example, we hardly found support for advanced techniques for temporal data visualization or radial visualizations in the evaluated tools and applications. On the contrary, interviews with professional data analysts confirm strong interest in learning and applying new tools and techniques. Users are especially interested in techniques that can support their exploratory analysis workflow. Based on these findings and our own experience with data science projects, we present suggestions and considerations towards a better integration of visualization techniques in current data science workflows.

Paper Nr: 26
Title:

Vague Visualizations to Reduce Quantification Bias in Shared Medical Decision Making

Authors:

Michela Assale, Silvia Bordogna and Federico Cabitza

Abstract: This paper aims to contribute to the research focusing on how to render properly uncertainty in decision making, especially in regard to classification (like in medical diagnosis) or risk prediction (like in medical prognosis). Information visualizations leverage perception to convey information on data in ways that make their interpretation easier. Unfortunately, many visualizations omit uncertainty or communicate it less than effectively. We devised a novel way, which we call vague visualization, to render uncertainty without converting it in any numerical or symbolic form, and tested the usability and task fitness of these alternative solutions in a user study that involved a panel of lay people (as proxies of potential patients). In so doing, we aimed to understand whether our solutions facilitate (or at least do not hinder) communication and understanding of probabilistic estimates in a medical context, and if one solution is more effective than the others. We observed that three different vague visualizations convey the right sense of risk with respect to chance (50%) of percentage shown, and inspire an interpretation of the magnitude of the percentages that replicates the typical response of decision making under uncertainty condition. We then claim that these methods are effective because they allow for data interpretations that are uncertain (vague), and yet correct and compatible with appropriate decisions and actions.

Paper Nr: 35
Title:

A Timeline Metaphor for Analyzing the Relationships between Musical Instruments and Musical Pieces

Authors:

J. Kusnick, R. Khulusi, J. Focht and S. Jänicke

Abstract: Digitization projects make cultural heritage data sustainably available. However, while digital libraries may capture various aspects, relations across different sources often remain unobserved. In our project, musicologists aimed to relate musical instruments with historical performances of musical pieces, both contained in different sources. We defined a similarity measure taking instrumentation, temporal as well as geospatial metadata into account, with which we were able to hypothesize potential relations. We propose a novel timeline design that offers a specific semantic zoom metaphor enabling the collaborating musicologists to observe and evaluate the results of our similarity analysis. The value of our system for research in musicology is documented in three case studies.

Paper Nr: 37
Title:

Irosashi: Visualization of the Colors of a Building Which Leave an Impression to Identify Characteristics of an Urban Environment

Authors:

Yota Kikuchi and Makoto Okamoto

Abstract: The purpose of this research is to propose a GUI for visualizing data showing the characteristics of the city and evaluate its effect. The author was interested in visual impressions of the urban environment and wanted to share these impressions with others. First, I made a prototype of "Irosashi Treemap", which plots all colors used for buildings in the western part of Hakodate city. However, the colors displayed by the Irosashi Treemap differed from the impressions of the subjects who knew the western area due to the many achromatic colors. Therefore, I collected color from the triangular roof and walls on the first floor of the building I felt frequent. In addition, it was suggested that the "Irosashi Treemap" plotted all colors, making it difficult to understand the characteristics of the colors. Therefore, a prototype of "Irosashi Impression", which plots representative colors, was produced. In evaluation experiment, three subjects answered that the impression of the city was close to the impression of the color taken from the triangular roof and walls. In the future, we will ask many people to collect impressive colors of the city. And evaluate the effect of "Irosashi Impression".

Paper Nr: 53
Title:

In Situ Visual Quality Control in 3D Printing

Authors:

Charalampos Kopsacheilis, Paschalis Charalampous, Ioannis Kostavelis and Dimitrios Tzovaras

Abstract: In the past decade, additive manufacturing technology has gained an immense attention in numerous research areas and has already been adopted in a wide range of industries relevant to transportation, healthcare, electronics and energy. However, the presence of defects and dimensional deviations that occur during the process hinder the broad exploitation of 3D printing. In order to enhance the capabilities of this emerging technology, online quality control methodologies and verifications of the manufacturing process are necessary to be developed. In the present article, a low cost in-situ vision-based monitoring technique applied in Fused Deposition Modeling (FDM) 3D printing technology is introduced. An optical scanning system was integrated in a commercial 3D Printer in order to scan and validate the performance of the procedure. The proposed methodology monitors the FDM process and correlates the theoretical 3D model with the manufactured one. This technique can be utilized in various additive manufacturing technologies providing integrity and reliability of the process, high quality standards and reduced production costs.

Area 3 - Spatial Data Visualization

Full Papers
Paper Nr: 17
Title:

Temporally Coherent Topological Landscapes for Time-varying Scalar Fields

Authors:

Maria Herick, Vladimir Molchanov and Lars Linsen

Abstract: Topological structures capture the main features of scalar fields. Topological landscapes have been proposed for an intuitive depiction of n-dimensional scalar field topology using 2D landscapes with matching topology. For time-varying scalar fields, each time step could be visualized by a 2D landscape, but there would be no temporal coherence among the landscapes. We propose the concept of a time-varying contour tree that is obtained by merging contour trees of all time steps into a meta data structure. The time-varying contour tree can be exploited to generate temporally coherent topological landscapes. Visual analysis of time-varying scalar field topology is, then, supported by animating landscapes over time or by volume rendering a stack of temporal slices that represent color-coded landscapes.

Paper Nr: 29
Title:

MapStack: Exploring Multilayered Geospatial Data in Virtual Reality

Authors:

Maxim Spur, Vincent Tourre, Erwan David, Guillaume Moreau and Patrick L. Callet

Abstract: Virtual reality (VR) headsets offer a large and immersive workspace for displaying visualizations with stereoscopic vision, compared to traditional environments with monitors or printouts. The controllers for these devices further allow direct three-dimensional interaction with the virtual environment. In this paper, we make use of these advantages to implement a novel multiple and coordinated view (MCV) in the form of a vertical stack, showing tilted layers of geospatial data to facilitate an understanding of multi-layered maps. A formal study based on a use-case from urbanism that requires cross-referencing four layers of geospatial urban data augments our arguments for it by comparing it to more conventional systems similarly implemented in VR: a simpler grid of layers, and switching (blitting) layers on one map. Performance and oculometric analyses showed an advantage of the two spatial-multiplexing methods (the grid or the stack) over the temporal multiplexing in blitting. Overall, users tended to prefer the stack, be ambivalent to the grid, and show dislike for the blitting map. Perhaps more interestingly, we were also able to associate preferences in systems with user characteristics and behavior.

Short Papers
Paper Nr: 18
Title:

Visual-auditory Volume Rendering of Dynamic Quantum Chemistry Molecular Fields

Authors:

Evgeniya Malikova, Valery Adzhiev, Oleg Fryazinov and Alexander Pasko

Abstract: This work deals with a visual-auditory visualisation of dynamic heterogeneous objects represented by continuous scalar fields obtained from quantum chemistry. The research concentrates on complex phenomena modelling and rendering aspects and takes advantage of GPU implementation. The approach uses the constructive HyperVolume for the multi-scale representation of the molecular phenomena. To propose an approach to the visual-auditory rendering, we adapt the real-time interactive volume ray-casting to compute the optical and auditory properties. We demonstrate the approach application for the visual-auditory rendering of dynamic molecular structures.

Paper Nr: 43
Title:

3D Printing and 3D Virtual Models for Surgical and Percutaneous Planning of Congenital Heart Diseases

Authors:

Katia Capellini, Paolo Tripicchio, Emanuele Vignali, Emanuele Gasparotti, Lamia A. Ali, Massimiliano Cantinotti, Duccio Federici, Giuseppe Santoro, Francesca Alfonzetti, Chiara Evangelista, Camilla Tanca and Simona Celi

Abstract: Despite increasing evidence of their utility, 3D models have never been extensively tested so far in pediatric cardiac surgery planning. 3D models may offer advantages over traditional imaging examinations: 1) a deeper understanding of 3D anatomy in complex defects allowing visual and tactile inspection from any point of view, 2) the possibility to interact with a tangible replica of the real organs, 3) the surgical planning and simulation maneuvers on the printed and virtual model, and 4) interaction with anatomical structures thank to Virtual Reality technologies. The work aims to test and compare the accuracy and the incremental diagnostic value of 3D printed and virtual models in patients undergoing cardiac surgery for CHDs.

Paper Nr: 54
Title:

Geovisualization: Multidimensional Exploration of the Territory

Authors:

Sidonie Christophe

Abstract: The purpose of this position paper is to emphasize the remaining challenges for geovisualization in an evolutive context of data, users and spatio-temporal problems to solve in an interdisciplinary approach. Geovisualization is the visualization of spatio-temporal data, phenomena and dynamics on earth, based on the user interaction with heterogeneous data, and their capacities of perception and cognition. This implies to bring closer together knowledge, concepts and models from related scientific visualization domains, for a better understanding, interpretation and analysis of spatio-temporal phenomena on earth. We currently face and cross several types of complexities, regarding spaces, data, models and tools. Our position here, based on past and on-going works, as first proofs of concept, is to model a multidimensional exploration of the territory, because integrating explorations of uses, styles, interaction and immersion capacities, until various ’points of view’ on the represented spatio-temporal phenomenon.