IVAPP 2021 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

Area 1 - Abstract Data Visualization

Full Papers
Paper Nr: 1
Title:

A Layered Software City for Dependency Visualization
Authors:

Veronika Dashuber, Michael Philippsen and Johannes Weigend
Abstract: A Software City is a an established way to visualize metrics such as the test coverage or complexity. As current layouting algorithms are mainly based on the static code structure (e.g., classes and packages), dependencies that are orthogonal to this structure often clutter the visualization and are hard to grasp. This paper applies layered graph drawing to layout a Software City in 3D. The proposed layout takes both the dependencies and the static code structure into account. This minimizes the number of explicitly displayed dependencies. The resulting lower cognitive load makes the software architecture easier to understand. We evaluate the advantages of our layout over a classic layouting algorithm in a controlled study on a real world project.
Download
Paper Nr: 2
Title:

Self-supervised Dimensionality Reduction with Neural Networks and Pseudo-labeling
Authors:

Mateus Espadoto, Nina T. Hirata and Alexandru C. Telea
Abstract: Dimensionality reduction (DR) is used to explore high-dimensional data in many applications. Deep learning techniques such as autoencoders have been used to provide fast, simple to use, and high-quality DR. However, such methods yield worse visual cluster separation than popular methods such as t-SNE and UMAP. We propose a deep learning DR method called Self-Supervised Network Projection (SSNP) which does DR based on pseudo-labels obtained from clustering. We show that SSNP produces better cluster separation than autoencoders, has out-of-sample, inverse mapping, and clustering capabilities, and is very fast and easy to use.
Download
Paper Nr: 4
Title:

CauseWorks: A Framework for Transforming User Hypotheses into a Computational Causal Model
Authors:

Thomas Kapler, Derek Gray, Holland Vasquez and William Wright
Abstract: Causal Model building for complex problems has typically been completed manually by domain experts and is a time-consuming, cumbersome process. Operational Design defines a process of rapid, structured discourse for teams to envision systems and relationships about complex, “wicked” problems, however, the resulting models are simple diagrams produced on whiteboards or slides, and as such, do not support computational analytics, thus limiting usefulness. We introduce CauseWorks, an application that helps operators “sketch” complex systems and transforms sketches into computational causal models using automatic and semiautomatic causal model construction from knowledge extracted from unstructured and structured documents. CauseWorks then provides computational analytics to assist users in understanding and influencing the system. We walk through human-machine collaborative model-building with CauseWorks and describe its application to regional conflict scenarios. We discuss feedback from subject matter experts as well as lessons learned.
Download
Paper Nr: 28
Title:

Software Forest: A Visualization of Semantic Similarities in Source Code using a Tree Metaphor
Authors:

Daniel Atzberger, Tim Cech, Merlin de La Haye, Maximilian Söchting, Willy Scheibel, Daniel Limberger and Jürgen Döllner
Abstract: Software visualization techniques provide effective means for program comprehension tasks as they allow developers to interactively explore large code bases. A frequently encountered task during software development is the detection of source code files of similar semantic. To assist this task we present Software Forest, a novel 2.5D software visualization that enables interactive exploration of semantic similarities within a software system, illustrated as a forest. The underlying layout results from the analysis of the vocabulary of the software documents using Latent Dirichlet Allocation and Multidimensional Scaling and therefore reflects the semantic similarity between source code files. By mapping properties of a software entity, e.g., size metrics or trend data, to visual variables encoded by various, figurative tree meshes, aspects of a software system can be displayed. This concept is complemented with implementation details as well as a discussion on applications.
Download
Paper Nr: 30
Title:

OptMap: Using Dense Maps for Visualizing Multidimensional Optimization Problems
Authors:

Mateus Espadoto, Francisco M. Rodrigues, Nina T. Hirata and Alexandru C. Telea
Abstract: Operations Research is a very important discipline in many industries, and although there were many developments since its inception, to our knowledge there are no visualization tools focused on helping users understand the decision variables’ domain space and its constraints for problems with more than two input dimensions. In this paper, we propose OptMap, a technique that enables the visual exploration of optimization problems using a two-dimensional dense map, regardless of the number of variables and constraints in the problem and for any kind of single-valued objective function. We show the technique in action for several optimization problems of different types, such as linear, nonlinear and integer, constrained and unconstrained problems.
Download
Short Papers
Paper Nr: 6
Title:

SIMDGiraffe: Visualizing SIMD Functions
Authors:

P. M. Ntang and D. Lemire
Abstract: Many common processors offer advanced parallel-processing features to accelerate computations. In particular, most commodity processors support Single Instruction on Multiple Data (SIMD) instructions. Algorithms designed to benefit from these instructions can be several times faster than conventional algorithms. However, they can be difficult to understand, and therefore to review. We build SIMDGiraffe, a tool that can help visualize SIMD code written using the popular Intel intrinsics in C.
Download
Paper Nr: 7
Title:

Annotations in Different Steps of Visual Analytics
Authors:

Christoph Schmidt, Bastian Grundel, Heidrun Schumann and Paul Rosenthal
Abstract: Annotations in Visual Analytics (VA) have become a common means to support the analysis by integrating additional information into the VA system. Here, annotations often differ between the individual steps of VA. For example, during data preprocessing it may be necessary to add information on the data, such as redundancy or discrepancy information, while annotations, used during exploration, often refer to the externalization of findings and insights. Describing the particular needs for these step-dependent annotations is challenging. To tackle this issue, we examine the data preprocessing, data cleansing, and data exploration steps for the analysis of heterogeneous and error prone data in respect to the design of specific annotations. By that, we describe their peculiarities for each step in the analysis, and thus aim to improve the visual analytics approach on clinical data. We show the applicability of our annotation concept by integrating it into an existing visual analytics tool to analyze and annotate data from the ophthalmic domain. In interviews and application sessions with experts, we assess the usefulness of our annotation concept for the analysis of the visual acuity development for patients, undergoing a specific therapy.
Download
Paper Nr: 37
Title:

Visualization of Joint Spatio-temporal Models via Feature Recognition with an Application to Wildland Fires
Authors:

Devan G. Becker, Douglas G. Woolford and Charmaine B. Dean
Abstract: Many spatial statistics applications result in a collection of spatial estimates, especially if a different (but possibly correlated) estimate is produced for a sequence of time epochs. For a small collection of epochs, the connections or trends between estimates and the prominent or common features can be found via inspection of the spatial estimates. As the number of spatial estimates grows, this task becomes much more difficult. We present a method of summarizing a sequence of estimates using an image recognition technique called NonNegative Matrix Factorization which results in a meaningful decomposition of the source images into basis functions and coefficients. This visualization technique allows for investigation of trends over time as well as common spatial features of the estimates without needing to fit a temporal model or use pre-specified spatial regions. We apply this technique to a sequence of models that jointly model the spatial location of wildland fires with the total burn area of each of the fires. We discuss the extensions of the visualization technique to the joint modelling framework and are able to draw new insights about the connection between the location and size of the fires.
Download
Paper Nr: 42
Title:

Towards Visual Sociolinguistic Network Analysis
Authors:

Kostiantyn Kucher, Masoud Fatemi and Mikko Laitinen
Abstract: Investigation of social networks formed by individuals in various contexts provides numerous interesting and important challenges for researchers and practitioners in multiple disciplines. Within the field of variationist sociolinguistics, social networks are analyzed in order to reveal the patterns of language variation and change while taking the social, cultural, and geographical aspects into account. In this field, traditional approaches usually focusing on small, manually collected data sets can be complemented with computational methods and large digital data sets extracted from online social network and social media sources. However, increasing data size does not immediately lead to the qualitative improvement in the understanding of such data. In this position paper, we propose to address this issue by a joint effort combining variationist sociolinguistics and computational network analyses with information visualization and visual analytics. In order to lay the foundation for this interdisciplinary collaboration, we analyse the previous relevant work and discuss the challenges related to operationalization, processing, and exploration of such social networks and associated data. As the result, we propose a roadmap towards realization of visual sociolinguistic network analysis.
Download
Paper Nr: 44
Title:

Word-sized Visualizations for Exploring Discussion Diversity in Social Media
Authors:

Franziska Huth, Tanja Blascheck, Steffen Koch, Sonja Utz and Thomas Ertl
Abstract: In this paper, we explore the design space of word-sized visualizations—small graphics, usually the same size as a word, that visualize data in or related to a text—for displaying and exploring categories in social media feeds such as Twitter streams. Social media contributions are typically microposts, which allow us to attach word-sized visualizations to show category assignment, diversity, or development. We consider and combine word-sized visualizations made up of basic marks and visual variables, existing word-sized visualization concepts, as well as large text visualizations. In an application example we show how word-sized visualizations can evince context changes within a discussion on Twitter and reveal topic diversity.
Download
Paper Nr: 20
Title:

Cloud Cost City: A Visualization of Cloud Costs using the City Metaphor
Authors:

Veronika Dashuber and Michael Philippsen
Abstract: Many companies are in the process of migrating their entire IT infrastructure into the cloud in order to benefit from its high elasticity and scalability. While cloud providers offer basic cost visualizations such as line, bar or pie charts, companies lose track of which part of their infrastructure causes which costs. We adapt the city metaphor to visualize both the architecture and the costs. We offer a flexible framework to structure and tailor the visualization as desired. Using the Goal-Question-Metric approach, we identify cost savings potential and demonstrate for an example cloud infrastructure that the defined metrics are easier to grasp in our visualization compared to a standard cost dashboard.
Download
Paper Nr: 35
Title:

A Statement Report on the Use of Multiple Embeddings for Visual Analytics of Multivariate Networks
Authors:

Daniel Witschard, Ilir Jusufi, Rafael M. Martins and Andreas Kerren
Abstract: The visualization of large multivariate networks (MVN) continues to be a great challenge and will probably remain so for a foreseeable future. The field of Multivariate Network Embedding seeks to meet this challenge by providing MVN-specific embedding technologies that targets different properties such as network topology or attribute values for nodes or links. Although many steps forward have been taken, the goal of efficiently embedding all aspects of a MVN remains distant. This position paper contrasts the current trend of finding new ways of jointly embedding several properties with the alternative strategy of instead using, and combining, already existing state-of-the-art single scope embedding technologies. From this comparison, we argue that the latter strategy provides a more generic and flexible approach with several advantages. Hence, we hope to convince the visual analytics community to invest more work in resolving some of the key issues that would make this methodology possible.
Download
Paper Nr: 45
Title:

Cybersecurity for SMEs: Introducing the Human Element into Socio-technical Cybersecurity Risk Assessment
Authors:

Costas Boletsis, Ragnhild Halvorsrud, J. B. Pickering, Stephen Phillips and Mike Surridge
Abstract: Small and medium-sized enterprises (SMEs) rarely conduct a thorough cyber-risk assessment and they may face various internal issues when attempting to set up cyber-risk strategies. In this work, we apply a user journey approach to model human behaviour and visually map SMEs’ practices and threats, along with a visualisation of the socio-technical actor network, targeted specifically at the risks highlighted in the user journey. By using a combination of cybersecurity-related visualisations, our goals are: i) to raise awareness about cybersecurity, and ii) to improve communication among IT personnel, security experts, and non-technical personnel. To achieve these goals, we combine two modelling languages: Customer Journey Modelling Language (CJML) is a visual language for modelling and visualisation of work processes in terms of user journeys. System Security Modeller (SSM) is an asset-based risk-analysis tool for socio-technical systems. By demonstrating the languages’ supplementary nature through a threat scenario and considering related theories, we believe that there is a sound basis to warrant further validation of CJML and SSM together to raise awareness and handle cyber threats in SMEs.
Download

Area 2 - General Data Visualization

Full Papers
Paper Nr: 3
Title:

On Order-preserving, Gap-avoiding Rectangle Packing
Authors:

Sören Domrös, Daniel Lucas, Reinhard von Hanxleden and Klaus Jansen
Abstract: We present 2D rectangle packing heuristics that preserve the initial ordering of the rectangles while maintaining a left-to-right reading direction. Furthermore, rectangles are placed such that inner whitespace (“gaps”) can be eliminated by enlarging and repositioning them without enlarging the drawing. This is achieved by initially approximating the required width and using a strip packing algorithm to pack the rectangles. The algorithms are suitable for interactive scenarios and can also be applied to strip packing problems to maintain the reading direction.
Download
Paper Nr: 8
Title:

PLEADES: Population Level Observation of Smartphone Sensed Symptoms for In-the-wild Data using Clustering
Authors:

Hamid Mansoor, Walter Gerych, Abdulaziz Alajaji, Luke Buquicchio, Kavin Chandrasekaran, Emmanuel Agu and Elke Rundensteiner
Abstract: Smartphones are increasingly being used for health monitoring. Training of machine learning health models require studies in which smartphone sensor data is gathered passively on subjects’ phones. Subjects live their lives ’In-the-wild” and periodically annotate data with ground truth health labels. While computational approaches such as machine learning produce accurate results, they lack explanations about the complex factors behind the manifestation of health-related symptoms. Additionally, population-level insights are desirable for scalability. We propose Population Level Exploration and Analysis of smartphone DEtected Symptoms (PLEADES), a framework to present smartphone sensed data in linked panes using intuitive data visualizations. PLEADES utilizes clustering and dimension reduction techniques for discovery of groupings of similar days based on smartphone sensor values, across users for population level analyses. PLEADES allows analysts to apply different clustering and projection algorithms to a given dataset and then overlays human-provided contextual and symptom information gathered during data collection studies, which empower the analyst in interpreting findings. Such overlays enable analysts to contextualize the symptoms that manifest in smartphone sensor data. We visualize two real world smartphone-sensed datasets using PLEADES and validate it in an evaluation study with data visualization and human context recognition experts.
Download
Paper Nr: 22
Title:

Real-time Web-based Remote Interaction with Active HPC Applications
Authors:

Tim Dykes, Ugo Varetto, Claudio Gheller and Mel Krokos
Abstract: Real-time interaction is a necessary part of the modern high performance computing (HPC) environment, used for tasks such as development, debugging, visualization, and experimentation. However, HPC systems are remote by nature, and current solutions for remote user interaction generally rely on remote desktop software or bespoke client-server implementations combined with an existing user interface. This can be an inhibiting factor for a domain scientist looking to incorporate simple remote interaction to their research software. Furthermore, there are very few solutions that allow the user to interact via the web, which is fast becoming a crucial platform for accessible scientific HPC software. To address this, we present a framework to support remote interaction with HPC software through web-based technologies. This lightweight framework is intended to allow HPC developers to expose remote procedure calls and data streaming to application users through a web browser, and allow real-time interaction with the application while executing on a HPC system. We present a classification scheme for remote applications, detail our framework, and present an example use case within a HPC visualization application and real world performance for remote interaction with a HPC system over a Wide Area Network.
Download
Paper Nr: 43
Title:

A Replication Study on Glanceable Visualizations: Comparing Different Stimulus Sizes on a Laptop Computer
Authors:

Tanja Blascheck and Petra Isenberg
Abstract: We replicated a smartwatch perception experiment on the topic of glanceable visualizations. The initial study used a setup that involved showing stimuli on an actual smartwatch attached to a wooden stand and a laptop to run and log the experiment and communicate with the smartwatch. In our replication we wanted to test whether a much simpler setup that involved showing the same stimuli on a laptop screen with similar pixel size and density would lead to similar results. We also extended the initial study by testing to what extent the size of the stimulus played a role for the results. Our results indicate that the general trends observed in the original study mostly held also on the larger display, with only a few differences in certain conditions. Yet, participants were slower on the large display. We also found no evidence of a difference for the two different stimulus display sizes we tested. Our study, thus, gives evidence that simulating smartwatch displays on laptop screens with similar resolution and pixel size might be a viable alternative for smartwatch perception studies with visualizations.
Download
Short Papers
Paper Nr: 29
Title:

Categorizing Quantities using an Interactive Fuzzy Membership Function
Authors:

Liqun Liu and Romain Vuillemot
Abstract: In this paper, we investigate how an interactive version of the membership function from the Fuzzy Logic Theory can be used to categorize quantitative data. This function is simple and similar to a line chart, and provides an explicit mapping of the categorization process. We first review the requirements for such quantitative values partitioning process and provide the Fuzzy Logic mathematical foundations related to the membership function. We then report on the implementation of the interactive function for several quantitative datasets case studies (e. g., age, temperature, speed). We expect this interactive function to provide more control over the categorization process, as well as way to make the categorization more explicit.
Download
Paper Nr: 15
Title:

On Glyph Design for Wind Information in En-Route Air Traffic Control
Authors:

Linda Pfeiffer, Michelle Martinussen and Paul Rosenthal
Abstract: Information about the wind situation is crucial for en-route air traffic controllers. In this paper, we compare several glyph designs for showing wind direction and speed by the means of an empirical study. The different designs are based on arrows, wind barbs, and text. During the study, we are measuring response times and accuracy. Moreover, we collect evidence of the applicability of those designs in en-route air traffic control by qualitative feedback from air traffic controllers. Our findings suggest, that the often-used wind barbs are less suited for assessing wind speed and direction. Instead, a combination of arrow and text should be favored.
Download
Paper Nr: 21
Title:

ImmVis: Bridging Data Analytics and Immersive Visualisation
Authors:

Felipe A. Pedroso and Paula P. Costa
Abstract: One of the significant issues from the visualisation field is choosing the appropriate tool to conduct a research project or experiment. The Immersive Analytics (IA) field is no different but found support on game engines and web technologies to create their solutions, frameworks and toolkits. While these technologies solve problems like rendering and interaction, they do not offer functionalities to enable data analysis inside immersive environments. This paper presents ImmVis, a novel open-source framework that enables IA applications to benefit from the data analysis capabilities from Python programming language well-established libraries. The framework is enabled to work with different platforms and programming languages and can be used to complement the capabilities of existing IA tools, empowering them to offer more sophisticated data analysis functionalities.
Download
Paper Nr: 25
Title:

Graph Design: The Data-ink Ratio and Expert Users
Authors:

Kevin McGurgan, Elena Fedoroksaya, Tina M. Sutton and Andrew M. Herbert
Abstract: Graphical depictions of data are common but there is little empirical work that has examined how graph design principles are instantiated by graph makers. The data-ink ratio is one popular measure of graphical information content, where the “ink” related to data is divided by the total amount of “ink” in the graph. Expert interviews were conducted to examine graph use, creation, and opinions about the data-ink ratio concept. Interviewees had a variety of opinions and preferences with regard to graph design, many of which were dependent upon the specific circumstances of presentation. Most interviewees did not believe that high data-ink graph designs were superior. The results suggest that arguments regarding the data-ink ratio deal with the subjective issue of graph aesthetics.
Download
Paper Nr: 31
Title:

Applying Uncommon Visualizations to Government Dashboards
Authors:

Puripant Ruchikachorn
Abstract: Many governments provide data dashboards to present the state of the countries or administrative activities. Their main target audience is typically the citizens but the dashboard design process is usually top-down and leads to formulaic results. Developing three data dashboard projects for the government of Thailand, we successfully applied two uncommon data visualizations, grid map and connected scatterplot, despite initial resistance from the government agencies. We documented the design process including feedback on the two visualizations and solutions to alleviate their concerns. Academic studies had little success in convincing stakeholders. In both visualizations, animations helped to frame the concept of the uncommon visualizations.
Download
Paper Nr: 34
Title:

Similarity Measures for Visual Comparison and Retrieval of Test Data in Aluminum Production
Authors:

Nikolina Jekic, Belgin Mutlu, Manuela Schreyer, Steffen Neubert and Tobias Schreck
Abstract: Monitoring, analyzing and determining the production quality in a complex and long-running process such as in the aluminum production is a challenging task. The domain experts are often overwhelmed by the flood of data being generated and collected and have difficulties to analyze and interpret the results. Likewise, experts find it difficult to identify patterns in their data that may indicate deviations and anomalies that lead to unstable processes and lower product quality. We aim to support domain experts in the production data exploration and identifying meaningful patterns. The existing research covers a broad spectrum of pattern recognition methodologies that can be potentially applied to elicit patterns in data collected from industrial production. Hence, in this paper, we further analyze the applicability of different similarity measures to effectively recognize specific ultrasonic patterns which may indicate critical process deviations in aluminum production.
Download
Paper Nr: 36
Title:

VisNLP: A Visual-based Educational Support Platform for Learning Statistical NLP Analytics
Authors:

Amorn Chokchaisiripakdee and Chun-Kit Ngan
Abstract: We develop and implement a web-based, interactive visual NLP learning platform that enables novice learners to study the core processing components of statistical NLP analytics in sequence. More specifically, the contributions of this work are three-fold: (1) the ease of learners to access and use our platform through any web browser at no cost; (2) the interactive and dynamic visuals (e.g., mouseover events, collapsible tree diagrams, and animations) that enhance the study environment and learners’ engagement; and (3) the in-focus step-by-step process, using the job posting classification as an example, to demonstrate the core processing components of statistical NLP approaches.
Download
Paper Nr: 41
Title:

Visual Data Story Protocol: Internal Communications from Domain Expertise to Narrative Visualization Implementation
Authors:

Apiwan Duangphummet and Puripant Ruchikachorn
Abstract: Data stories play an important role in effectively and intuitively communicating data insights as well as enabling the audience to understand important social issues. Crafting a data story needs several sets of skills, we propose a five-phase data story protocol in order to guide data story design and development, and promote interdisciplinary team collaboration. This protocol was developed from our working team reflection on four data story projects and researching the related work. We hope that this protocol could be one potential way for non-journalism organizations to conduct data stories for their target audience.
Download

Area 3 - Spatial Data Visualization

Full Papers
Paper Nr: 10
Title:

Non-linear Monte Carlo Ray Tracing for Visualizing Warped Spacetime
Authors:

Avirup Mandal, Kumar Ayush and Parag Chaudhuri
Abstract: General relativity describes the curvature of spacetime. Rays of light follow geodesic paths in curved space-time. Visualizing scenes containing spacetime regions with pronounced curvature requires tracing of these light ray paths. We present a Monte Carlo approach for non-linear raytracing to render scenes in curved space-time. In contrast to earlier work, we can accurately resolve ray-object interactions. This allows us to create plausible visualizations of what happens when a black hole appears in a more known environment, like a room with regular specular and diffuse surfaces. We demonstrate that our solution is correct at cosmological scales by showing how spacetime warps around a stationary Schwarzschild black hole and a non-stationary Kerr black hole. We verify that the solution is consistent with the predictions of general relativity. In the absence of any curvature in spacetime, our renderer behaves like a normal linear ray tracer. Our method has the potential to create rich, physically plausible visualizations of complex phenomena that can be used for a range of purposes, from creating visual effects to making pedagogical aids to understand the behaviour of spacetime as predicted by general relativity.
Download
Paper Nr: 23
Title:

Geovisto: A Toolkit for Generic Geospatial Data Visualization
Authors:

Jiří Hynek, Jakub Kachlík and Vít Rusňák
Abstract: Today’s requirements for visualization of geospatial data are continually rising. Visualization authoring tools provide only limited support for this purpose. The ability to create geovisualizations by non-programmers is often reliant on template editing or visualization authoring tools. However, these tools are often limited either in configuring visual parameters or interaction capabilities. In our work, we identify the main limitations of current tools. Then, we propose design requirements and describe the implementation of Geovisto—a toolkit combining capabilities of the React, Leaflet, and D3.js frameworks in order to provide tools for processing generic geospatial data and creating multilayered reusable map widgets. We demonstrate our approach on two usage scenarios from conceptually different application areas (DDoS attacks from a network monitoring system and COVID-19 pandemics open data). Finally, we discuss the pros and cons of our approach and outline our future work.
Download