Generative AI for Visualization: Opportunities and Challenges.

Recent developments in artificial intelligence (AI) and machine learning (ML) have led to the creation of powerful generative AI methods and tools capable of producing text, code, images, and other media in response to user prompts. Significant interest in the technology has led to speculation about what fields, including visualization, can be augmented or replaced by such approaches. However, there remains a lack of understanding about which visualization activities may be particularly suitable for the application of generative AI. Drawing on examples from the field, we map current and emerging capabilities of generative AI across the different phases of the visualization lifecycle and describe salient opportunities and challenges.

The Curse of Performative User Studies.

Computer graphics research frequently evaluates research outputs with user studies, often through online crowdworking platforms. When performed carefully and thoughtfully, studies on human behavior and preferences provide valuable insights, useful for both developing and evaluating new tools. Yet, I argue that many of the current studies are performative: they result from reviewers' expectation that "papers should have some evaluation," not from careful thought about the value and usefulness of the studies themselves. These casually done studies are often uninformative or misleading, while putting undue burden on authors and reviewers. The expectation of positive user evaluation results can also inhibit creative new work. I call for reviewers to be more thoughtful about asking for user studies, for authors to be more thoughtful when they perform studies, and for our field to conduct new research and create new guidelines on when and how user studies are genuinely useful.

How to Evaluate If Collaborative Augmented Reality Speaks to Its Users.

Augmented reality (AR) is increasingly considered to support scenarios of co-located and remote collaboration. Thus far, the core goal has been advancing the supporting technologies and assessing how they perform to inform design and development, thus providing support toward their maturity. Nevertheless, while understanding the performance and impact of supporting technology is indisputable groundwork, we argue that the field needs to adopt a framework that moves from answering questions about the proposed methods and technologies to a more holistic view, also encompassing collaboration. However, moving toward this goal challenges how evaluations are designed, adding complexity and raising several questions about what needs to be considered. In this article, we briefly examine the different dimensions entailed in collaborative AR and argue in favor of a distinctive evaluation framework that goes beyond current practice and sets its eyes on the elements that allow judging how collaboration unfolds while informing the role of the supporting technology.

Toward a Holistic Virtual Environment for Graph Visualization and Analysis.

Interactive data visualization plays a crucial role in the interpretability of large datasets. Virtual reality offers unique advantages in exploring data, beyond traditional 2-D views. This article presents a set of interaction artifacts designed for analyzing and interpreting complex datasets through immersive 3-D graph visualization and interaction. Our system makes complex datasets easier to work with by offering a wide range of visual customization tools and intuitive methods for selection, manipulation, and filtering. It also provides a cross-platform, collaborative environment that can be accessed by remote users through traditional computers, drawing tablets, and touchscreen devices.

Can Image Data Facilitate Reproducibility of Graphics and Visualizations? Toward a Trusted Scientific Practice.

Reproducibility is a cornerstone of good scientific practice; however, the ongoing "reproducibility crisis" shows that we still need to improve the way we are doing research currently. Reproducibility is crucial because it enables both the comparison to existing techniques as well as the composition and improvement of existing approaches. It can also increase trust in the respective results, which is paramount for adoption in further research and applications. While there are already many initiatives and approaches with different complexity aimed at enabling reproducible research in the context of visualization, we argue for an alternative, lightweight approach that documents the most relevant parameters with minimal overhead. It still complements complex approaches well, and integration with any existing tool or system is simple. Our approach uses the images produced by visualizations and seamlessly piggy-backs everyday communication and research collaborations, publication authoring, public outreach, and internal note-taking. We exemplify how our approach supports day-to-day work and discuss limitations and how they can be countered.

Notebooks for Data Analysis and Visualization: Moving Beyond the Data.

Notebooks are a relatively new way of analyzing data and creating visualizations. They differ from the common graphical user interfaces used for visualization tools in many ways, and have their own strengths and weaknesses. In particular, they allow easy sharing, experimentation, and collaboration, and provide context about the data for different kinds of users. They also integrate modeling, forecasting, and complex analyses directly with the visualization. We believe that notebooks provide a unique and fundamentally new way of working with and understanding data. By laying out their unique properties, we hope to inspire both researchers and practitioners to investigate their many uses, explore their pros and cons, and share their findings.

Affective Virtual Reality: How to Design Artificial Experiences Impacting Human Emotions.

Computer graphics is-in many cases-about visualizing what you cannot see. However, virtual reality (VR), from its beginnings, aimed at stimulating all human senses: not just the visual channel. Moreover, this set of multisensory stimuli allows users to feel present and able to interact with the virtual environment. In this way, VR aims to deliver experiences that are comparable to real-life ones in their level of detail and stimulation, intensity, and impact. Hence, VR is not only a means to see, but also to feel differently. With the spreading of VR technologies, there is a growing interest in using VR to evoke emotions, including positive and negative ones. This article discusses the current possibilities and the authors' experience collected in the field in trying to elicit emotions through VR. It explores how different design aspects and features can be used, describing their contributions and benefits in the development of affective VR experiences. This work aims at raising awareness of the necessity to consider and explore the full design space that VR technology provides in comparison to traditional media. Additionally, it provides possible tracks of VR affective applications, illustrating how they could impact our emotions and improve our life, and providing guidelines for their development.

Looking Into a Future Which Hopefully Will Not Become Reality: How Computer Graphics Can Impact Our Behavior-A Study of the Potential of VR.

Humans tendency to engage in behaviors that are harmful to themselves, the environment, and the society has always been present on a personal and collective level. However, the concern for this kind of phenomena is increasing, as demographic and economic growth is amplifying its impact on people health, economies, and ecosystems. As a consequence, we have seen the rise of research fields as design for behavior change, with a growing interest in the use of tools as persuasive technologies, serious games and interactive systems to affect people awareness, attitude, and behavior. To these purposes, computer graphics and especially virtual reality (VR) has great potential since it can provide experiences to deepen users' understanding and emotional involvement regarding a variety of social and environmental issues. Here, we discuss the use of VR as a powerful, versatile, and cost-effective tool to deliver virtual experiences that inform and motivate users to change behavior. We describe and relate different aspects regarding sustainable behavior and VR experience design with respect to their potential to support behavior change.

Understanding Failure Mode Effect Analysis Data Using Interactive Visual Analytics.

Providing actionable insights through interactive visual analytics is essential to effective decision making. Yet, many complex systems engineering (SE) domains still lack such tools. Design reviews are often still based on static snapshots of data, without any dynamic interaction, data curation, and view creation capabilities to answer salient analysis questions. In this study, we report on a tool called DataHawk that helps answer common questions associated with one prominent SE context, namely failure mode and effect analysis (FMEA). The tool provides powerful exploration capabilities that enable system engineers, designers, and managers to probe FMEA data from multiple starting points, build questions dynamically, and find triangulated answers using multiple views rapidly. Field results are illustrated through a usage scenario from the automotive industry and show that the tool demonstrates the needed versatility, scalability, and effectiveness for real-world engineering data.

MAQUI: Interweaving Queries and Pattern Mining for Recursive Event Sequence Exploration.

Exploring event sequences by defining queries alone or by using mining algorithms alone is often not sufficient to support analysis. Analysts often interweave querying and mining in a recursive manner during event sequence analysis: sequences extracted as query results are used for mining patterns, patterns generated are incorporated into a new query for segmenting the sequences, and the resulting segments are mined or queried again. To support flexible analysis, we propose a framework that describes the process of interwoven querying and mining. Based on this framework, we developed MAQUI, a Mining And Querying User Interface that enables recursive event sequence exploration. To understand the efficacy of MAQUI, we conducted two case studies with domain experts. The findings suggest that the capability of interweaving querying and mining helps the participants articulate their questions and gain novel insights from their data.

Graphicle: Exploring Units, Networks, and Context in a Blended Visualization Approach.

Many real-world datasets are large, multivariate, and relational in nature and relevant associated decisions frequently require a simultaneous consideration of both attributes and connections. Existing visualization systems and approaches, however, often make an explicit trade-off between either affording rich exploration of individual data units and their attributes or exploration of the underlying network structure. In doing so, important analysis opportunities and insights are potentially missed. In this study, we aim to address this gap by (1) considering visualizations and interaction techniques that blend the spectrum between unit and network visualizations, (2) discussing the nature of different forms of contexts and the challenges in implementing them, and (3) demonstrating the value of our approach for visual exploration of multivariate, relational data for a real-world use case. Specifically, we demonstrate through a system called Graphicle how network structure can be layered on top of unit visualization techniques to create new opportunities for visual exploration of physician characteristics and referral data. We report on the design, implementation, and evaluation of the system and effectiveness of our blended approach.

Duet: Helping Data Analysis Novices Conduct Pairwise Comparisons by Minimal Specification.

Data analysis novices often encounter barriers in executing low-level operations for pairwise comparisons. They may also run into barriers in interpreting the artifacts (e.g., visualizations) created as a result of the operations. We developed Duet, a visual analysis system designed to help data analysis novices conduct pairwise comparisons by addressing execution and interpretation barriers. To reduce the barriers in executing low-level operations during pairwise comparison, Duet employs minimal specification: when one object group (i.e. a group of records in a data table) is specified, Duet recommends object groups that are similar to or different from the specified one; when two object groups are specified, Duet recommends similar and different attributes between them. To lower the barriers in interpreting its recommendations, Duet explains the recommended groups and attributes using both visualizations and textual descriptions. We conducted a qualitative evaluation with eight participants to understand the effectiveness of Duet. The results suggest that minimal specification is easy to use and Duet's explanations are helpful for interpreting the recommendations despite some usability issues.

Graphiti: Interactive Specification of Attribute-Based Edges for Network Modeling and Visualization.

Network visualizations, often in the form of node-link diagrams, are an effective means to understand relationships between entities, discover entities with interesting characteristics, and to identify clusters. While several existing tools allow users to visualize pre-defined networks, creating these networks from raw data remains a challenging task, often requiring users to program custom scripts or write complex SQL commands. Some existing tools also allow users to both visualize and model networks. Interaction techniques adopted by these tools often assume users know the exact conditions for defining edges in the resulting networks. This assumption may not always hold true, however. In cases where users do not know much about attributes in the dataset or when there are several attributes to choose from, users may not know which attributes they could use to formulate linking conditions. We propose an alternate interaction technique to model networks that allows users to demonstrate to the system a subset of nodes and links they wish to see in the resulting network. The system, in response, recommends conditions that can be used to model networks based on the specified nodes and links. In this paper, we show how such a demonstration-based interaction technique can be used to model networks by employing it in a prototype tool, Graphiti. Through multiple usage scenarios, we show how Graphiti not only allows users to model networks from a tabular dataset but also facilitates updating a pre-defined network with additional edge types.

Understanding variations in pediatric asthma care processes in the emergency department using visual analytics.

Health care delivery processes consist of complex activity sequences spanning organizational, spatial, and temporal boundaries. Care is human-directed so these processes can have wide variations in cost, quality, and outcome making systemic care process analysis, conformance testing, and improvement challenging. We designed and developed an interactive visual analytic process exploration and discovery tool and used it to explore clinical data from 5784 pediatric asthma emergency department patients.

A Visual Analytics Approach to Understanding Care Process Variation and Conformance.

With greater pressures of providing high-quality care at lower cost due to a changing financial and policy environment, the ability to understand variations in care delivery and associated outcomes and act upon this understanding is of critical importance. Building on prior work in visualizing health-care event sequences and in collaboration with our clinical partner, we describe our process in developing a multiple, coordinated visualization system that helps identify and analyze care processes and their conformance to existing care guidelines. We demonstrate our system using data of 5,784 pediatric emergency department visits over a 13-month period for which asthma was the primary diagnosis.

Visual business ecosystem intelligence: lessons from the field.

Macroscopic insight into business ecosystems is becoming increasingly important. With the emergence of new digital business data, opportunities exist to develop rich, interactive visual-analytics tools. Georgia Institute of Technology researchers have been developing and implementing visual business ecosystem intelligence tools in corporate settings. This article discusses the challenges they faced, the lessons learned, and opportunities for future research.