Sense making is one of the biggest challenges in data analysis faced by both the industry and the research community. It involves understanding the data and uncovering its model, generating a hypothesis, selecting analysis methods, creating novel solutions, designing evaluation, and also critical thinking and learning wherever needed. The research and development for such sense making tasks lags far behind the fast-changing user needs. As a result, sense making is often performed manually and the limited human cognition capability becomes the bottleneck of sense making in data analysis and decision making.

A recent advance in sense making research is the capture, visualization, and analysis of provenance information. Provenance is the history and context of sense making, including the data/analysis used and the users’ critical thinking process. It has been shown that provenance can effectively support many sense making tasks. For instance, provenance can provide an overview of what has been examined and reveal gaps such as unexplored information or solution possibilities. Besides, provenance can support collaborative sense making and communication by sharing the rich context of the sense making process.

Besides data analysis and decision making, provenance has been studied in many other fields, sometimes under different names, for different types of sense making. For example, the Human-Computer Interaction community relies on the analysis of logging to understand user behaviors and intentions; the WWW and database community has been working on data lineage to understand uncertainty and trustworthiness; and finally, reproducible science heavily relies on provenance to improve the reliability and efficiency of scientific research.

For this special issue, we are soliciting papers that describe innovative research, design, system/tools, and viewpoints regarding the collection, analysis, and summarization of provenance information to support the design and evaluation of novel techniques for sense making across different application domains:

Use cases of provenance and logging information, such as:
- Supporting sense making;
- Understanding user sense making activities and/or evaluation of sense making tools;
- Supporting collaborative sense making;
- Providing sense making transparency and reproducibility

Research related to the challenges in capturing the required provenance information, such as:
- The complex provenance information required for different use cases;
- Automatic capture of high-level provenance such as human thinking and reasoning;
- Software architecture for provenance capture for both new and existing systems.

Research related to the analysis and visualization of provenance data, such as:
- Visualization and summarization of provenance information;
- Machine learning and Nature Language Processing techniques that can help analysis of provenance data.

Special Issue on Next-generation Mixed-Reality User Experiences
提出日: 2024-04-24

The future is bright and challenging: we envision a very complex combination of mixed reality (MR), artificial intelligence (AI) and spatial computing as a smart integration of interconnected systems – seamlessly bridging both physical and virtual spaces. The upcoming generation of everyday wearable, embedded, and ubiquitous technology allows the ideation and validation of a wide range of next-generation context-aware, or intelligent MR user experiences. Realization of this vision is promising especially given recent interdisciplinary advancements in key areas and growing interest in applying these novel approaches in real-world application domains.

This special issue aims to attract ambitious contributions addressing this intelligent vision for mixed-reality leveraging, new computational interfaces, spatial computing, rich and diverse data sources, smart devices’ extensive computation power, artificial intelligence, and sensing capabilities, as well as ubiquitous connectivity, computation resources, and storage offered by the existing communication and network infrastructure.

We ask for contributions that address new methods, technologies, approaches, applications, case studies, and industrial showcases, paving the way for novel insights, breakthrough designs, discussions, and developments in this rapidly evolving domain. Specifically, we invite contributions that explore innovations in the following topics of interest, but not limited to (order is alphabetical and does not indicate importance):
- Artificial intelligence, computer vision, machine learning
- Avatars, conversational and speech interfaces
- Context-aware computing, semantic scene understanding
- Diversity, inclusion, equity, and ethical issues
- Ergonomics, human factors, human-computer interaction
- Extended senses (visual, aural, haptic)
- Localization, spatial registration, and tracking
- Presence, body ownership, and agency
- Spatial-, mixed-, mediated- and diminished- reality
- Spatial computing
- Sensation, perception, attention, and cognition
- System architectures, distributed and online systems
- Next-generation user interfaces, user interaction, and visualization techniques ● Technology acceptance and social implications
- User-centered design approaches for spatial computing
- User experience and performances, usability studies, and human-subjects experiments 

Special Issue on Critical Data Visualization
提出日: 2024-10-31

Critical data visualization generally refers to the practice of examining and representing data with an awareness of the cultural, social, and ethical implications. These methods support consideration of the power structures embedded into visualization designs, inspection of the politics latent to research methods, and reflection on visualization content and context more broadly. Coming to prominence in information visualization from Dörk et al. (2013), the term critical visualization originally described principles for authoring visualizations that expose embedded values and support empowerment for readers. This field has since grown to encompass a range of topics—for instance, integrating considerations of how data and graphical practices might embrace feminist methods. Works in this area strive to challenge existing visualization dogmas and identify overlooked assumptions as a way to push conventional visualization practice toward more inclusive and reflective practices.

Despite growing usage, however, there is a lack of a shared definition of what ‘criticality’ refers to for visualization and how those ideas can be applied in a rigorous and useful manner. While this plural understanding of criticality has afforded a vast array of creative applications, it has precluded critical methods and goals from being broadly understood or adopted. Through this special issue, we aim to both complicate and coalesce the visualization research community’s understanding and definition of critical visualizations, as well as the challenges associated with it. For instance, answering questions like: How to design ‘critical’ visualizations? What are the goals of critical visualization? Or even, what is critical visualization? To address these concerns we invite essays, explorations, and empirical work on topics including:
- Examinations of how critical theory intersects with data visualization (theoretical or empirical)
- Critical methods that have led to new visualization methods, theories, or designs
- Investigations of the relationship between criticality and feminism in visualization
- Reflections on the ethical implications of visualization research
- Challenges to appropriateness of critical theory for visualization research
- Case studies of critical methods applied to visualization (for example, speculative design/auto-ethnography/diffraction)
- New means of critically evaluating visualizations
- Grand challenges for critical visualization.