Guido Reina

In many graphics and visualization frameworks, directed acyclic graphs are a popular way to model visualization pipelines. Pipeline editors provide a visual interface to modify the underlying graph and use, e.g., a node-link diagram to visualize the data and program flow. This paper proposes an interactive tool for post-mortem performance analysis and comparison of pipeline variants. We extend the node-link representation of a visualization pipeline and enrich it with fine-grained runtime performance metrics. Annotating this static structure with dynamic performance information lets the developer evaluate performance characteristics in depth. Our approach further supports a visual comparison of two user-selected states of the graph. The comparison allows us to identify the impact of (topological) changes on the graph’s performance. We demonstrate the utility of our approach with different scientific visualization use cases and report expert feedback.

Understanding how dynamic patterns vary across large spatiotemporal ensembles is essential in many scientific domains. In fluid dynamics, for instance, researchers analyze how splash patterns in droplet impact experiments change with physical parameters such as fluid type or impact velocity. These experiments produce large volumes of data where patterns differ in size, shape, and duration, making manual analysis tedious and error-prone. Recently, interactive visualization approaches have been developed to assist analysis using learned similarity models for pattern-based querying. However, they assume fixed size inputs and only support single-pattern queries, thus limiting their effectiveness for multiscale, multi-pattern analysis and exploration of ensembles. In this paper, we present a visual anal ysis approach for the interactive exploration of spatiotemporal ensembles through multiscale pattern querying. Our approach extends an existing similarity model to support variable-sized patterns, allowing users to define queries by selecting examples directly on visualized data. Coordinated views enable interactive querying, comparison, and analysis of pattern occurrences and relate pattern occurrences to ensemble parameters. A guidance mechanism supports the user in finding underexplored regions. We demonstrate the utility of our approach on synthetic and real-world datasets. Domain expert feedback confirms that the approach is intuitive, easy to use, and effective for revealing parameter-pattern relationships.

The explosion of data in astronomy has resulted in an era of unprecedented opportunities for discovery. The GAIA mission's catalog, containing a large number of light sources (mostly stars) with several parameters such as sky position and proper motion, is playing a significant role in advancing astronomy research and has been crucial in various scientific breakthroughs over the past decade. In its current release, more than 200 million stars contain a calibrated continuous spectrum, which is essential for characterizing astronomical information such as effective temperature and surface gravity, and enabling complex tasks like interstellar extinction detection and narrow‐band filtering. Even though numerous studies have been conducted to visualize and analyze the data in the SciVis and AstroVis communities, no work has attempted to leverage spectral information for visualization in real‐time. Interactive exploration of such complex, massive data presents several challenges for visualization. This paper introduces a novel multi‐resolution, spectrum‐preserving data structure and a progressive, real‐time visualization algorithm to handle the sheer volume of the data efficiently, enabling interactive visualization and exploration of the whole catalog's spectra. We show the efficiency of our method with our open‐source, interactive, web‐based tool for exploring the GAIA catalog, and discuss astronomically relevant use cases of our system.

Varying the input parameters of simulations or experiments often leads to different classes of results. Parameter sensitivity analysis in this context includes estimating the sensitivity to the individual parameters, that is, to understand which parameters contribute most to changes in output classifications and for which parameter ranges these occur. We propose a novel visual parameter sensitivity analysis approach based on Voronoi cell interfaces between the sample points in the parameter space to tackle the problem. The Voronoi diagram of the sample points in the parameter space is first calculated. We then extract Voronoi cell interfaces which we use to quantify the sensitivity to parameters, considering the class label information of each sample's corresponding output. Multiple visual encodings are then utilized to represent the cell interface transitions and class label distribution, including stacked graphs for local parameter sensitivity. We evaluate the approach's expressiveness and usefulness with case studies for synthetic and real‐world datasets.

In many disciplines, the findable, accessible, interoperable, and reusable (FAIR) principles are a gold standard for data management and handling. While these disciplines often require FAIR principles to be at least partially implemented in their scientific work to avoid desk rejects, the visualization community is still working on a proper way to deal with the FAIR principles. In this article, we aim to show the relation between the visualization community and the FAIR community. This will be accomplished by a showcase of other communities that are incorporating the FAIR principles successfully. Based on these investigations, we aim to provide suggestions on how to implement FAIR principles in the visualization community.

Gaming consoles, whether stationary or handheld, are designed to provide a reasonably high level of computing power to run contemporary video games at an attractive price point, a compact form factor and modest energy consumption. While consoles have traditionally been closed-off systems, recent versions of the Xbox allow the development of apps for the Universal Windows Platform (UWP) on retail devices, making it potentially a suitable platform for bringing scientific visualisation (SciVis) applications to the masses. We describe how to run such applications, namely volume rendering and ray casting of spherical glyphs, on commodity gaming systems, not only on the Xbox Series X/S, but also on handheld devices like the Steam Deck. We detail the challenges and limitations we encountered during the implementation and provide the results of an extensive study of rendering performance, not only proving the viability of the approach but also allowing for a cost and benefit evaluation compared to standard desktop computers. Graphical

Visualization as a discipline has to investigate its practical implications in a world steadily moving toward greener computing methods. Quantifying the power consumption of visualization algorithms is thus essential, given the ever-increasing energy needs of GPUs. Previous approaches rely on integrated sensors or invasive methods that require modifications and special test setups. However, they still suffer from imprecision from low sampling rates and integration over time. Using a high-precision, high-frequency setup via steerable oscilloscopes, we can objectively measure the resulting quality of previous approaches. This is essential to establish a ground truth, pave the way for improved modeling of power consumption in general, and enable better estimates based on the output of lower-quality sensors. We finally discuss benefits that can be drawn from the additional insight of the higher-precision setup and which additional use cases can justify the incurred costs. Graphical abstract

We present an image-based approach to integrate state-of-the-art scientific visualization into virtual reality (VR) environments: the mint visualization/VR inter-operation system. We enable the integration of visualization algorithms from within their software frameworks directly into VR without the need to explicitly port visualization implementations to the underlying VR framework—thus retaining their capabilities, specializations, and optimizations. Consequently, our approach also facilitates enriching VR-based scientific data exploration with established or novel VR immersion and interaction techniques available in VR authoring tools. The separation of concerns enables researchers and users in different domains, like virtual immersive environments, immersive analytics, and scientific visualization, to independently work with existing software suitable for their domain while being able to interface with one another easily. We present our system architecture and inter-operation protocol (mint), an example of a collaborative VR environment implemented in the Unity engine (VRAUKE), as well as the integration of the protocol for the visualization frameworks Inviwo, MegaMol, and ParaView. Our implementation is publicly available as open-source software. Graphical