The increasing complexity and volume of network data demand effective analysis approaches, with visual exploration proving particularly beneficial. Immersive technologies, such as augmented reality, virtual large display walls, have enabled the emerging field immersive analytics, offering new opportunities to enhance user engagement, spatial awareness, problem-solving. A growing body work explores environments for visualisation, ranging from design studies fully integrated applications...

Many processes, from gene interaction in biology to computer networks social media, can be modeled more precisely as temporal hypergraphs than by regular graphs. This is because generalize graphs extending edges connect any number of vertices, allowing complex relationships described accurately and predict their behavior over time. However, the interactive exploration seamless refinement such hypergraph-based prediction models still pose a major challenge. We contribute Hyper-Matrix, novel...

Leveraging hypergraph structures to model advanced processes has gained much attention over the last few years in many areas, ranging from protein-interaction computational biology image retrieval using machine learning. Hypergraph models can provide a more accurate representation of underlying while reducing overall number links compared regular representations. However, interactive visualization methods for hypergraphs and hypergraph-based have rarely been explored or systematically...

With the increasingly detailed investigation of game play and tactics in invasive team sports such as soccer, it becomes ever more important to present causes, actions findings a meaningful manner. Visualizations, especially when augmenting relevant information directly inside video recording match, can significantly improve simplify soccer match preparation tactic planning. However, while many visualization techniques for have been developed recent years, few applied video-based analysis...

AI-driven models are increasingly deployed in operational analytics solutions, for instance, investigative journalism or the intelligence community. Current approaches face two primary challenges: ethical and privacy concerns, as well difficulties efficiently combining heterogeneous data sources multimodal analytics. To tackle challenge of analytics, we present MULTI-CASE, a holistic visual framework tailored towards ethics-aware exploration, designed collaboration with domain experts. It...

Categorical data does not have an intrinsic definition of distance or order, and therefore, established visualization techniques for categorical only allow a set-based frequency-based analysis, e.g., through Euler diagrams Parallel Sets, do support similarity-based analysis. We present novel dimensionality reduction-based data, which is based on defining the two items as number varying attributes. Our technique enables users to pre-attentively detect groups similar observe properties...

Digital systems for analyzing human communication data have become prevalent in recent years. This may be related to the increasing abundance of that can harnessed but hardly managed manually. Intelligence analysis communications investigative journalism, criminal intelligence, and law present particularly interesting cases, as they must take into account often highly sensitive properties underlying operations data. At same time, these are areas where increasingly automated, sophisticated...

Communication consists of both meta-information as well content. Currently, the automated analysis such data often focuses either on network aspects via social or content, utilizing methods from text-mining. However, first category approaches does not leverage rich content information, while latter ignores conversation environment and temporal evolution, evident in meta-information. In contradiction to communication research, which stresses importance a holistic approach, are rarely applied...

Visual network exploration is essential in numerous disciplines, including biology, digital humanities, and cyber security. Prior research has shown that immersive, stereoscopic 3D can enhance spatial comprehension accuracy exploring node-link diagrams. However, graphs present challenges, node occlusion edge crossings, which necessitate continual manual perspective adjustments. We introduce a virtual reality (VR) framework assists users navigating to optimal viewing points based on their...

The visual exploration of trajectory data is crucial in domains such as animal behavior, molecular dynamics, and transportation. With the emergence immersive technology, data, which often inherently three-dimensional, can be analyzed stereoscopic 3D, providing new opportunities for perception, engagement, understanding. However, interaction with presented remains a key challenge. While most applications depend on hand tracking, we see eye tracking promising yet under-explored modality, while...

Abstract While there are many visualization techniques for exploring numeric data, only a few work with categorical data. One prominent example is Parallel Sets, showing data frequencies instead of points ‐ analogous to parallel coordinates numerical As nominal does not have an intrinsic order, the design Sets sensitive visual clutter due overlaps, crossings, and subdivision ribbons hindering readability pattern detection. In this paper, we propose set quality metrics, called ParSetgnostics...

With the ongoing emergence of smart and distributed grids, it becomes increasingly important to understand as well improve legacy infrastructure while operating a much more interconnected fragile architecture. To support this endeavor, detailed simulation real-life analysis are required. Leveraging advanced visualization analytics methods can significantly simplify tasks such network analysis, maintenance, planning, also enabling operators spot critical issues which hard detect otherwise. In...

Deep learning has revolutionized multimedia retrieval, yet effectively searching within large video collections remains a complex challenge. This paper focuses on the design and evaluation of known-item search systems, leveraging strengths CLIP-based deep neural networks for ranking. At events like Video Browser Showdown, these models have shown promise in ranking frames. While can be pre-selected automatically based benchmark collection, selection an optimal browsing interface, crucial...

Manual melody detection is a tedious task requiring high expertise level, while automatic often not expressive or powerful enough. Thus, we present MelodyVis, visual application designed in collaboration with musicology experts to explore melodic patterns digital sheet music. MelodyVis features five connected views, including Melody Operator Graph and Voicing Timeline. The system utilizes eight atomic operators, such as transposition mirroring, capture repetitions variations. Users can start...

Exploring, analyzing, and interpreting law can be tedious challenging, even for legal scholars, since texts contain domain-specific language, require knowledge of tacit concepts, are sometimes intentionally ambiguous. In related, text-based domains, Visual Analytics (VA) large language models (LLMs) have become essential working with documents as they support data navigation, representation, analytical reasoning. However, scholars must simultaneously manage hierarchical information sources,...

Object-centric architectures can learn to extract distinct object representations from visual scenes, enabling downstream applications on the level. Similarly autoencoder-based image models, object-centric approaches have been trained unsupervised reconstruction loss of images encoded by RGB color spaces. In our work, we challenge common assumption that are optimal space for learning in computer vision. We discuss conceptually and empirically other spaces, such as HSV, bear essential...

Large-scale interaction networks of human communication are often modeled as complex graph structures, obscuring temporal patterns within individual conversations. To facilitate the understanding such conversational dynamics, episodes with low or high activity well breaks in need to be detected enable identification patterns. Traditional episode detection approaches highly dependent on choice parameters, window-size binning-resolution. In this paper, we present a novel technique for relevant...

<div>The automated analysis of digital human communication data often focuses on specific aspects like content or network structure in isolation, while classical research stresses the importance a holistic approach. This work aims to formalize and investigate how results can be leveraged as part visually interactive systems, which offers new opportunities allow for less biased, skewed, incomplete results. For this, we construct conceptual framework design space based existing...

The automated analysis of digital human communication data often focuses on specific aspects such as content or network structure in isolation. This can provide limited perspectives while making cross-methodological analyses, occurring domains like investigative journalism, difficult. Communication research psychology and the humanities instead stresses importance a holistic approach to overcome these limiting factors. In this work, we conduct an extensive survey properties over forty...

<div>The automated analysis of digital human communication data often focuses on specific aspects like content or network structure in isolation, while classical research stresses the importance a holistic approach. This work aims to formalize and investigate how results can be leveraged as part visually interactive systems, which offers new opportunities allow for less biased, skewed, incomplete results. For this, we construct conceptual framework design space based existing...