Domain-specific Visualization Research Articles

IF-City: Intelligible Fair City Planning to Measure, Explain and Mitigate Inequality.

With the increasing pervasiveness of Artificial Intelligence (AI), many visual analytics tools have been proposed to examine fairness, but they mostly focus on data scientist users. Instead, tackling fairness must be inclusive and involve domain experts with specialized tools and workflows. Thus, domain-specific visualizations are needed for algorithmic fairness. Furthermore, while much work on AI fairness has focused on predictive decisions, less has been done for fair allocation and planning, which require human expertise and iterative design to integrate myriad constraints. We propose the Intelligible Fair Allocation (IF-Alloc) Framework that leverages explanations of causal attribution (Why), contrastive (Why Not) and counterfactual reasoning (What If, How To) to aid domain experts to assess and alleviate unfairness in allocation problems. We apply the framework to fair urban planning for designing cities that provide equal access to amenities and benefits for diverse resident types. Specifically, we propose an interactive visual tool, Intelligible Fair City Planner (IF-City), to help urban planners to perceive inequality across groups, identify and attribute sources of inequality, and mitigate inequality with automatic allocation simulations and constraint-satisfying recommendations (IF-Plan). We demonstrate and evaluate the usage and usefulness of IF-City on a real neighborhood in New York City, US, with practicing urban planners from multiple countries, and discuss generalizing our findings, application, and framework to other use cases and applications of fair allocation.

Generating interactive documents for domain-specific validation of formal models

Especially in industrial applications of formal modeling, validation is as important as verification. Thus, it is important to integrate the stakeholders’ and the domain experts’ feedback as early as possible. In this work, we propose two approaches to enable this: (1) a static export of an animation trace into a single HTML file, and (2) a dynamic export of a classical B model as an interactive HTML document, both based on domain-specific visualizations. For the second approach, we extend the high-level code generator B2Program by JavaScript and integrate VisB visualizations alongside SimB simulations with timing, probabilistic and interactive elements. An important aspect of this work is to ease communication between modelers and domain experts. This is achieved by implementing features to run simulations, sharing animated traces with descriptions and giving feedback to each other. This work also evaluates the performance of the generated JavaScript code compared with existing approaches with Java and C++ code generation as well as the animator, constraint solver, and model checker ProB.

Knowledge Graphs in Practice: Characterizing their Users, Challenges, and Visualization Opportunities.

This study presents insights from interviews with nineteen Knowledge Graph (KG) practitioners who work in both enterprise and academic settings on a wide variety of use cases. Through this study, we identify critical challenges experienced by KG practitioners when creating, exploring, and analyzing KGs that could be alleviated through visualization design. Our findings reveal three major personas among KG practitioners - KG Builders, Analysts, and Consumers - each of whom have their own distinct expertise and needs. We discover that KG Builders would benefit from schema enforcers, while KG Analysts need customizable query builders that provide interim query results. For KG Consumers, we identify a lack of efficacy for node-link diagrams, and the need for tailored domain-specific visualizations to promote KG adoption and comprehension. Lastly, we find that implementing KGs effectively in practice requires both technical and social solutions that are not addressed with current tools, technologies, and collaborative workflows. From the analysis of our interviews, we distill several visualization research directions to improve KG usability, including knowledge cards that balance digestibility and discoverability, timeline views to track temporal changes, interfaces that support organic discovery, and semantic explanations for AI and machine learning predictions.

GEViTRec: Data Reconnaissance Through Recommendation Using a Domain-Specific Visualization Prevalence Design Space.

Genomic Epidemiology (genEpi) is a branch of public health that uses many different data types including tabular, network, genomic, and geographic, to identify and contain outbreaks of deadly diseases. Due to the volume and variety of data, it is challenging for genEpi domain experts to conduct data reconnaissance; that is, have an overview of the data they have and make assessments toward its quality, completeness, and suitability. We present an algorithm for data reconnaissance through automatic visualization recommendation, GEViTRec. Our approach handles a broad variety of dataset types and automatically generates visually coherent combinations of charts, in contrast to existing systems that primarily focus on singleton visual encodings of tabular datasets. We automatically detect linkages across multiple input datasets by analyzing non-numeric attribute fields, creating a data source graph within which we analyze and rank paths. For each high-ranking path, we specify chart combinations with positional and color alignments between shared fields, using a gradual binding approach to transform initial partial specifications of singleton charts to complete specifications that are aligned and oriented consistently. A novel aspect of our approach is its combination of domain-agnostic elements with domain-specific information that is captured through a domain-specific visualization prevalence design space. Our implementation is applied to both synthetic data and real Ebola outbreak data. We compare GEViTRec's output to what previous visualization recommendation systems would generate, and to manually crafted visualizations used by practitioners. We conducted formative evaluations with ten genEpi experts to assess the relevance and interpretability of our results. Code, Data, and Study Materials Availability: https://github.com/amcrisan/GEVitRec.

Syphilis Trigram: a domain-specific visualisation to combat syphilis epidemic and improve the quality of maternal and child health in Brazil

BackgroundThe Brazilian healthcare system is a large and complex system, specially considering its mixed public and private funding. The incidence of syphilis has increased in the last four years, in spite of the presence of an effective and available treatment. Furthermore, syphilis takes part in a group of disorders of compulsory notification to the public health surveillance. The epidemiological implications are especially important during pregnancy since it can lead to complications, related to prematurity stillbirth and miscarriage, in addition to congenital syphilis, characterized by multisystem involved in the newborn.MethodsThe Action Research methodology was applied to address the complexity of the syphilis surveillance scenario in Pernambuco, Brazil. Iterative learning cycles were used, resulting in six cycles, followed by a formal validation of an operational version of the syphilis Trigram visualisation at the end of the process. The original data source was analyzed and prepared to be used without any new data or change in the ordinary procedure of the current system.ResultsThe main result of this work is the production of a Syphilis Trigram: a domain-specific infographic for presenting gestational data and birth data. The second contribution of this work is the Average Trigram, an organized pie chart which synthesizes the Syphilis Trigram relationship in an aggregated way. The visualization of both graphics is presented in an Infographic User Interface, a tool that gathers an infographic broad visualization aspect to data visualization. These interfaces also gather selections and filters tools to assist and refine the presented information. The user can experience a specific case-by-case view, in addition to an aggregated perspective according to the cities monitored by the system.ConclusionsThe proposed domain-specific visualization amplifies the understanding of each syphilis case and the overall characteristics of cases of a chosen city. This new information produced by the Trigram can help clarify the reinfection/relapse cases, optimize resource allocation and enhance the syphilis healthcare policies without the need of new data. Thus, this enables the health surveillance professionals to see the broad tendency, understand the key patterns through visualization, and take action in a feasible time.

Measuring Effects of Spatial Visualization and Domain on Visualization Task Performance: A Comparative Study.

Understanding one's audience is foundational to creating high impact visualization designs. However, individual differences and cognitive abilities influence interactions with information visualization. Different user needs and abilities suggest that an individual's background could influence cognitive performance and interactions with visuals in a systematic way. This study builds on current research in domain-specific visualization and cognition to address if domain and spatial visualization ability combine to affect performance on information visualization tasks. We measure spatial visualization and visual task performance between those with tertiary education and professional profile in business, law & political science, and math & computer science. We conducted an online study with 90 participants using an established psychometric test to assess spatial visualization ability, and bar chart layouts rotated along Cartesian and polar coordinates to assess performance on spatially rotated data. Accuracy and response times varied with domain across chart types and task difficulty. We found that accuracy and time correlate with spatial visualization level, and education in math & computer science can indicate higher spatial visualization. Additionally, we found that motivational differences between domains could contribute to increased levels of accuracy. Our findings indicate discipline not only affects user needs and interactions with data visualization, but also cognitive traits. Our results can advance inclusive practices in visualization design and add to knowledge in domain-specific visual research that can empower designers across disciplines to create effective visualizations.

A Comprehensive State-of-the-Art Survey on Data Visualization Tools: Research Developments, Challenges and Future Domain Specific Visualization Framework

Data visualization is a powerful skill for the demonstration of meaningful data insights in an interactive and effective way. In this survey article, we collected 70 articles from last five years (2017-2022) to identify, classify, and investigate the various scopes, aspects and theories of data visualization. We also investigated the powerful applications of data visualization in various domains and fields such as visualization apps for health sector, Internet of things (IoTs), business dashboards, urban traffic management, smart buildings and environmental data visualization. However, after thorough investigation and classification, we conclude that, a comprehensive study is still missing about interactive, effective and efficient data visualization survey explaining basic current state-of-the-art best interactive visualization techniques, web-based tools and platforms, best performance theories, data structures and algorithms. In this survey article, we perform a thorough investigation to fill the gap on theoretical, analytical, statistical models and techniques for improving the performance of visualization. Current primary and domain specific future challenges are also reviewed, and related future research directions and opportunities are recommended.

Improving Access to Scientific Literature with Knowledge Graphs

AbstractThe transfer of knowledge has not changed fundamentally for many hundreds of years: It is usually document-based-formerly printed on paper as a classic essay and nowadays as PDF. With around 2.5 million new research contributions every year, researchers drown in a flood of pseudo-digitized PDF publications. As a result research is seriously weakened. In this article, we argue for representing scholarly contributions in a structured and semantic way as a knowledge graph. The advantage is that information represented in a knowledge graph is readable by machines and humans. As an example, we give an overview on the Open Research Knowledge Graph (ORKG), a service implementing this approach. For creating the knowledge graph representation, we rely on a mixture of manual (crowd/expert sourcing) and (semi-)automated techniques. Only with such a combination of human and machine intelligence, we can achieve the required quality of the representation to allow for novel exploration and assistance services for researchers. As a result, a scholarly knowledge graph such as the ORKG can be used to give a condensed overview on the state-of-the-art addressing a particular research quest, for example as a tabular comparison of contributions according to various characteristics of the approaches. Further possible intuitive access interfaces to such scholarly knowledge graphs include domain-specific (chart) visualizations or answering of natural language questions.

Extending Explicitly Modelled Simulation Debugging Environments with Dynamic Structure

The widespread adoption of Modelling and Simulation (M8S) techniques hinges on the availability of tools supporting each phase in the M8S-based workflow. This includes tasks such as specifying, implementing, experimenting with, as well as debugging, simulation models. We have previously developed a technique where advanced debugging environments are generated from an explicit behavioural model of the user interface and the simulator. These models are extracted from the code of existing modelling environments and simulators and instrumented with debugging operations. This technique can be reused for a large family of modelling formalisms but was not yet considered for dynamic-structure formalisms; debugging models in these formalisms is challenging, as entities can appear and disappear during simulation. In this article, we adapt and apply our approach to accommodate dynamic-structure formalisms. To this end, we present a modular, reusable approach, which includes an architecture and a workflow. We observe that to effectively debug dynamic-structure models, domain-specific visualizations developed by the modeller should be (re)used for debugging tasks. To demonstrate our technique, we use Dynamic-Structure DEVS (a formalism that includes the characteristics of discrete-event and agent-based modelling paradigms) and an implementation of its simulation semantics in the PythonPDEVS tool as a running example. We apply our technique on NetLogo, a popular multi-agent simulation tool, to demonstrate the generality of our approach.

An IoT Based Mobile Augmented Reality Application for Energy Visualization in Buildings Environments

Augmented reality (AR) improves how we acquire, understand, and display information without distracting us from the real world. These technologies can be used in different applications and industries as they can incorporate domain-specific visualizations on a real-world screen. Mobile augmented reality (MAR) essentially consists of superimposing virtual elements over real objects on the screen, to give added value and enrich the interaction with reality. In numerous plants, it is being used for maintenance and repair tasks, as well as training. The Internet of Things (IoT) is increasingly pervading every aspect of our lives, including the power infrastructure of our buildings. IoT-enabled devices offer many connectivity options for helping supervise all-important energy assets. Aggregating data to cloud-based platforms enables operations teams to have on-time information access to make fast decisions and have a fast response regarding energy use, while maintenance teams keep on top of the appliance power quality and reliability needed by using MAR. This paper presents a novel approximation for visualizing appliance-related power quality to enhance awareness about the consumed electricity. A combined solution of MAR with IoT technologies is employed. Engineered solutions’ hands-free way to get data about surrounding appliances reduces the complexity, saves energy, and speeds up the operations. An innovative way to measure things at the right time leads to a competitive advantage.

VAPOR: A Visualization Package Tailored to Analyze Simulation Data in Earth System Science

Visualization is an essential tool for analysis of data and communication of findings in the sciences, and the Earth System Sciences (ESS) are no exception. However, within ESS, specialized visualization requirements and data models, particularly for those data arising from numerical models, often make general purpose visualization packages difficult, if not impossible, to use effectively. This paper presents VAPOR: a domain-specific visualization package that targets the specialized needs of ESS modelers, particularly those working in research settings where highly-interactive exploratory visualization is beneficial. We specifically describe VAPOR’s ability to handle ESS simulation data from a wide variety of numerical models, as well as a multi-resolution representation that enables interactive visualization on very large data while using only commodity computing resources. We also describe VAPOR’s visualization capabilities, paying particular attention to features for geo-referenced data and advanced rendering algorithms suitable for time-varying, 3D data. Finally, we illustrate VAPOR’s utility in the study of a numerically- simulated tornado. Our results demonstrate both ease-of-use and the rich capabilities of VAPOR in such a use case.

Distributed Fast Self-Organized Maps for Massive Spectrophotometric Data Analysis †.

Analyzing huge amounts of data becomes essential in the era of Big Data, where databases are populated with hundreds of Gigabytes that must be processed to extract knowledge. Hence, classical algorithms must be adapted towards distributed computing methodologies that leverage the underlying computational power of these platforms. Here, a parallel, scalable, and optimized design for self-organized maps (SOM) is proposed in order to analyze massive data gathered by the spectrophotometric sensor of the European Space Agency (ESA) Gaia spacecraft, although it could be extrapolated to other domains. The performance comparison between the sequential implementation and the distributed ones based on Apache Hadoop and Apache Spark is an important part of the work, as well as the detailed analysis of the proposed optimizations. Finally, a domain-specific visualization tool to explore astronomical SOMs is presented.

Visual Exploration of Semantic Relationships in Neural Word Embeddings.

Constructing distributed representations for words through neural language models and using the resulting vector spaces for analysis has become a crucial component of natural language processing (NLP). However, despite their widespread application, little is known about the structure and properties of these spaces. To gain insights into the relationship between words, the NLP community has begun to adapt high-dimensional visualization techniques. In particular, researchers commonly use t-distributed stochastic neighbor embeddings (t-SNE) and principal component analysis (PCA) to create two-dimensional embeddings for assessing the overall structure and exploring linear relationships (e.g., word analogies), respectively. Unfortunately, these techniques often produce mediocre or even misleading results and cannot address domain-specific visualization challenges that are crucial for understanding semantic relationships in word embeddings. Here, we introduce new embedding techniques for visualizing semantic and syntactic analogies, and the corresponding tests to determine whether the resulting views capture salient structures. Additionally, we introduce two novel views for a comprehensive study of analogy relationships. Finally, we augment t-SNE embeddings to convey uncertainty information in order to allow a reliable interpretation. Combined, the different views address a number of domain-specific tasks difficult to solve with existing tools.

Linked Geoscience Data in practice: where W3C standards meet domain knowledge, data visualization and OGC standards

The geoscience community is now facing both the challenge and the opportunity caused by the vast amount of datasets that can be made available on the Web. An efficient “data environment” on the Web has the potential to enable geoscientists to conduct their research in ways that never existed before. Standards developed by the Open Geospatial Consortium have already been used widely to build data services among different subjects in geosciences. In recent years, the Linked Open Data approach initiated by the World Wide Web Consortium has received increasing attention. In this paper, the author presents a pilot study that uses a domain specific knowledge base and data visualization techniques to leverage the functionality of geoscience data services in the Web of Data. The study focuses on the topic of the geologic time scale. Detailed works such as semantic modeling and encoding, multilingual vocabularies, exploratory data visualization, web map service and processing, and the query of linked data are introduced through real-world datasets. This study faces a broad perspective of the Linked Geoscience Data and leverages the functionalities of existing standards into a new level for geoscience applications.

Multi-objective optimization and visualization for analog design automation

The automated design of analog and mixed-signal circuits is a well-known subject of increasing technical and economical significance, e.g., sensory circuits for internet of things, cyber-physical systems, and Industry 4.0.The demand for rapid solution achievement under constraints, as, e.g., robustness, in established and emerging technologies as well as the migration between technologies gives incentive to automation activities. Existing approaches and tools still show improvement potential with regard to multi-variate modeling, efficient and multi-objective optimization, as well as transparence and user interaction options during the design. This paper presents new approaches applied within an emerging design environment, denoted as ABSYNTH, with an evolving self-learning architecture for efficient hierarchical optimization in a cascade, which includes function approximators and simulators trained by proven evolutionary optimization algorithms, as well as a novel domain-specific visualization of the optimization space and the trajectory of the design process. Nominal schematic-level sizing of the commonly used Miller, buffer, and folded-cascode amplifier circuits has been studied with our approach. For Miller, buffer, and folded-cascode, a cascade of harmony search and particle swarm optimization on SVR, ngspice, and cadence simulators was found to be roughly 4 times, 2.5 times, and 2.5 times faster, respectively, than the flat approach with equal or better results. In future work, we will improve the approach by including more demanding circuits, statistical deviations, circuit breeding, advanced optimization, and layout generation.

Online Collaborative Environment for Designing Complex Computational Systems

Developers of information systems have always utilized various visual formalisms during the design process, albeit in an informal manner. Architecture diagrams, finite state machines, and signal flow graphs are just a few examples. Model Integrated Computing (MIC) is an approach that considers these design artifacts as first class models and uses them to generate the system or subsystems automatically. Moreover, the same models can be used to analyze the system and generate test cases and documentation. MIC advocates the formal definition of these formalisms, called domain-specific modeling languages (DSML), via metamodeling and the automatic configuration of modeling tools from the metamodels. However, current MIC infrastructures are based on desktop applications that support a limited number of platforms, discourage concurrent design collaboration and are not scalable. This paper presents WebGME, a cloud- and web-based cyberinfrastructure to support the collaborative modeling, analysis, and synthesis of complex, large-scale scientific and engineering information systems. It facilitates interfacing with existing external tools, such as simulators and analysis tools, it provides custom domain-specific visualization support and enables the creation of automatic code generators.

Cataloging the biomedical world of pain through semi-automated curation of molecular interactions

The vast collection of biomedical literature and its continued expansion has presented a number of challenges to researchers who require structured findings to stay abreast of and analyze molecular mechanisms relevant to their domain of interest. By structuring literature content into topic-specific machine-readable databases, the aggregate data from multiple articles can be used to infer trends that can be compared and contrasted with similar findings from topic-independent resources. Our study presents a generalized procedure for semi-automatically creating a custom topic-specific molecular interaction database through the use of text mining to assist manual curation. We apply the procedure to capture molecular events that underlie ‘pain’, a complex phenomenon with a large societal burden and unmet medical need. We describe how existing text mining solutions are used to build a pain-specific corpus, extract molecular events from it, add context to the extracted events and assess their relevance. The pain-specific corpus contains 765 692 documents from Medline and PubMed Central, from which we extracted 356 499 unique normalized molecular events, with 261 438 single protein events and 93 271 molecular interactions supplied by BioContext. Event chains are annotated with negation, speculation, anatomy, Gene Ontology terms, mutations, pain and disease relevance, which collectively provide detailed insight into how that event chain is associated with pain. The extracted relations are visualized in a wiki platform (wiki-pain.org) that enables efficient manual curation and exploration of the molecular mechanisms that underlie pain. Curation of 1500 grouped event chains ranked by pain relevance revealed 613 accurately extracted unique molecular interactions that in the future can be used to study the underlying mechanisms involved in pain. Our approach demonstrates that combining existing text mining tools with domain-specific terms and wiki-based visualization can facilitate rapid curation of molecular interactions to create a custom database.Database URL: •••

The effects of inspecting and constructing part-task-specific visualizations on team and individual learning

This study examined whether inspecting and constructing different part-task-specific visualizations differentially affects learning. To this end, a complex business-economics problem was structured into three phase-related part-tasks: (1) determining core concepts, (2) proposing multiple solutions, and (3) coming to a single solution. Each phase was foreseen with a part-task-specific representational tool facilitating visualization of the domain-content (i.e., a conceptual, causal and simulation tool respectively for the subsequent phases). Whereas all teams of learners (N = 17) were scripted to carry out the part-tasks in the predefined order, teams were instructed to (1) inspect expert visualizations (n = 8) or (2) construct their own domain-specific visualizations (n = 9). Results indicate that constructing visualizations, in comparison to inspecting them, evokes more meaningful discussion of the domain-content beneficially affecting team complex learning-task performance and individual learning gains (i.e., higher post-test score).

Using end-user visualization environments to mediate conversations: a ‘Communicative Dimensions’ framework

An end-user visualization environment aims to empower end users to create graphical representations of phenomena within a scientific domain of interest. Research into end-user visualization environments has traditionally focused on developing the human–computer interaction necessary to enable the quick and easy construction of domain-specific visualizations. That traditional focus has left open the question of how such environments might support human–human interaction. Especially in situations in which end-user visualization environments are enlisted to facilitate learning and to build design consensus, we hypothesize that a key benefit is their ability to mediate conversations about a scientific domain of interest. In what ways might end-user visualization environments support human communication, and what design features make them well-suited to do so? Drawing both on a theory of communication, and on empirical studies in which end-user environments were enlisted to support human communication, we propose a provisional framework of six ‘Communicative Dimensions’ of end-user visualization environments: programming salience, provisionality, story content, modifiability, controllability, and referencability. To illustrate the value of these dimensions as an analytic and design tool, we use them to map a sample of publicly available end-user visualization environments into the ‘Communicative’ design space. By characterizing those aspects of end-user visualization environments that impact social interaction, our framework provides an important extension to Green and Petre's (J. Visual Lang. Comput. 7 (1996) 131–174) ‘Cognitive Dimensions’.

Using component technologies for web based wavelet enhanced mammographic image visualization.

The poor contrast detectability of mammography can be dealt with by domain specific software visualization tools. Remote desktop client access and time performance limitations of a previously reported visualization tool are addressed, aiming at more efficient visualization of mammographic image resources existing in web or PACS image servers. This effort is also motivated by the fact that at present, web browsers do not support domain-specific medical image visualization. To deal with desktop client access the tool was redesigned by exploring component technologies, enabling the integration of stand alone domain specific mammographic image functionality in a web browsing environment (web adaptation). The integration method is based on ActiveX Document Server technology. ActiveX Document is a part of Object Linking and Embedding (OLE) extensible systems object technology, offering new services in existing applications. The standard DICOM 3.0 part 10 compatible image-format specification Papyrus 3.0 is supported, in addition to standard digitization formats such as TIFF. The visualization functionality of the tool has been enhanced by including a fast wavelet transform implementation, which allows for real time wavelet based contrast enhancement and denoising operations. Initial use of the tool with mammograms of various breast structures demonstrated its potential in improving visualization of diagnostic mammographic features. Web adaptation and real time wavelet processing enhance the potential of the previously reported tool in remote diagnosis and education in mammography.