Dimensionality reduction (DR) reveals clusters and trends in multivariate data but often weakens the link between observed structures and the underlying attributes. Data glyphs can re-introduce attribute-level evidence directly in the projection, yet practical end-to-end tools that combine DR, glyph enrichment, and multiscale interaction remain limited. We present Glyphspace, a browser-based visual analytics system that integrates guided dataset import and preprocessing, multiple DR projections, linked multi-canvas comparison, histogram-based filtering, and multiscale inspection via semantic zoom and a Magic Lens. Our contribution is primarily one of integration and usability: Glyphspace packages established techniques into a cohesive, fully client-side workflow designed for rapid exploration and education without programming.

Recommender systems are a ubiquitous part of the online experience, yet their interfaces often appear as black boxes, making it difficult for users to understand, explore, and influence recommendations. Prior work has shown that interactive visualizations can improve transparency, trust, and user experience, but existing solutions are highly diverse and lack a unifying structure. In this paper, we present a design space for visual interfaces in recommender systems that captures recurring interface properties across WHY, WHAT, and HOW dimensions. Derived through a multi-stage empirical process, it was applied to deconstruct 58 visual recommender interfaces into 96 distinct views. Based on this design space, we further introduce an interactive exploration and analysis tool that enables users to search, filter, and compare interface solutions and to analyze recurring combinations of building blocks across challenges, data characteristics, and visualization and interaction techniques.

Text-to-image generation systems are increasingly capable of producing professional-looking infographics from natural language prompts. While these systems offer substantial efficiency gains for data communication, their outputs risk being adopted uncritically by users who lack the data visualization literacy needed to identify misleading elements — a competency that the systems themselves do not possess.

User interface (UI) design patterns are a well-established means for capturing reusable solutions to recurring interaction problems. However, existing pattern repositories are largely expert-curated, fragmented across sources, and organized around terminologies that presuppose prior knowledge. This creates a mismatch with the mental models of junior designers, who often approach design from problem- and goal-driven perspectives rather than from canonical pattern names. In this paper, we present a human-centered design space for web interaction patterns that is grounded in the categorization strategies of novice practitioners. Instead of imposing a top-down taxonomy, we conducted a two-stage card sorting study with junior designers (open: N = 10; hybrid: N = 8) to elicit latent organizational principles. Building on these empirical insights and a corpus of 49 verified UI patterns drawn from academic and web-based repositories, we derived a three-dimensional design space structured along the dimensions of What (functional scope), Why (heuristic goal), and How (interaction behavior). We validated this user-derived structure through closed card sorting and tree testing (N = 16). While the functional (What) and behavioral (How) dimensions proved intuitive, the heuristic (Why) dimension revealed a notable mismatch between abstract usability principles and practical pattern application among novice designers.

Design spaces are powerful conceptual frameworks for analyzing solutions within a problem domain. We provide an exploration and analysis tool to examine the underlying data collection, compare different solutions and analyze commonly used building blocks. The web-based tool is based on javascript and vue.js and is avaibable online at: https://ives.kwmhgb.at

Participatory data physicalizations allow people to physically participate in the creation of visualizations. In this work, we present four categories of data physicalization concepts that were tested with public event visitors in a university context. The goal was to create data physicalizations from simple, affordable materials to engage visitors in data collection and visualization in a playful way.

As visualization literacy and its implications gain prominence, we need effective methods to prepare students for the variety of visualizations in an increasingly data-driven world. Recently, the potential of comics has been recognized in various data visualization contexts, including educational settings. We describe the development of a workshop in which we use our "comic construction kit" as a tool for students to understand various data visualization techniques through an interactive creative approach of creating explanatory comics. We report on our insights from holding eight workshops with high school students and teachers, university students, and lecturers, aiming to enhance the landscape of handson visualization activities that can enrich the visualization classroom. The comic construction kit and all supplemental materials are open source under a CC-BY license and available at https://fhstp.github.io/comixplain/vis4schools.html.

This paper is a call to action for research and discussion on data visualization education. As visualization evolves and spreads through our professional and personal lives, we need to understand how to support and empower a broad and diverse community of learners in visualization. Data Visualization is a diverse and dynamic discipline that combines knowledge from different fields, is tailored to suit diverse audiences and contexts, and frequently incorporates tacit knowledge. This complex nature leads to a series of interrelated challenges for data visualization education. Driven by a lack of consolidated knowledge, overview, and orientation for visualization education, the 21 authors of this paper-educators and researchers in data visualization-identify and describe 19 challenges informed by our collective practical experience. We organize these challenges around seven themes People, Goals & Assessment, Environment, Motivation, Methods, Materials, and Change. Across these themes, we formulate 43 research questions to address these challenges. As part of our call to action, we then conclude with 5 cross-cutting opportunities and respective action items: embrace DIVERSITY+INCLUSION, build COMMUNITIES, conduct RESEARCH, act AGILE, and relish RESPONSIBILITY. We aim to inspire researchers, educators and learners to drive visualization education forward and discuss why, how, who and where we educate, as we learn to use visualization to address challenges across many scales and many domains in a rapidly changing world: viseducationchallenges.github.io.

When designing the interface of a recommender system, interactive visualizations can be used to support challenges such as transparency and controllability, and thus increasing the user’s trust. In this paper, we propose a construction kit for visual recommender systems that can be used to construct new solutions or deconstruct existing approaches to identify and analyze key aspects.

In this paper, we introduce Sparkle Glyphs - a glyph design that can be used to represent temporal multivariate data in a compact yet expressive way. One application scenario involves the analysis of multivariate audio features. These features are compared with sketch-based associations of sounds to identify and derive dominant perceived audio features.

Currently, both understanding and developing interactive visual interfaces become ever more challenging, since different visual solutions exist that involve large interdisciplinary teams and deal with massive amounts of data, a wide range of interaction techniques, and domain-specific aspects. At the same time, traditional design methods become obsolete with more and more resources and knowledge that needs to be acquired. The proposed workshop provides a forum for discussing experimental and theoretical techniques, frameworks, and prototyping methods to design visual interfaces in different domains such as data visualization, tangible and embedded interaction, extended and mixed reality, multi-modal interfaces, and others.

Rigorous data science is interdisciplinary at its core. In order to make sense of high-dimensional data, data scientists need to enter into a dialogue with domain experts. We present Glyphboard, a visualization tool that aims to support this dialogue. Glyphboard is a zoomable user interface that combines well-known methods such as dimensionality reduction and glyph-based visualizations in a novel, seamless, and integrated tool. While the dimensionality reduction affords a quick overview over the data, glyph-based visualizations are able to show the most relevant dimensions in the data set at one glance. We contribute an open-source prototype of Glyphboard, a general exchange format for high-dimensional data, and a case study with nine data scientists and domain experts from four exemplary domains in order to evaluate how the different visualization and interaction features of Glyphboard are used.

Industrial applications can benefit considerably from the overwhelming amount of still growing resources such as websites, images, texts, and videos that the internet offers today. The resulting Big Data Problem does not only consist of handling this immense volume of data. Moreover, data needs to be processed, cleaned, and presented in a user-friendly, intuitive, and interactive way. This workshop addresses visualization and user interaction challenges posed by the four V's: Volume (huge data amounts in the range of tera and petabytes), Velocity (the speed in which data is created, processed, and analysed), Variety (the different heterogeneous data types, sources, and formats), and Veracity (authenticity and validity of data). Big Data driven interfaces combine suitable backend and frontend technologies as well as automatic and semi-automatic approaches in order to analyze data in various business contexts. An important aspect is human intervention in developing and training data-driven applications (human in the loop). Our focus is on Visual Big Data Interfaces in industrial contexts such as e-commerce, e-learning and business intelligence. We address interfaces for three important user groups: data scientists, data workers and end users.

With the internet, massively heterogeneous data sources need to be understood and classified to provide suitable services to users such as content observation, data exploration, e-commerce, or adaptive learning environments. The key to providing these services is applying machine learning (ML) in order to generate structures via clustering and classification. Due to the intricate processes involved in ML, visual tools are needed to support designing and evaluating the ML pipelines. In this contribution, we propose a comprehensive tool that facilitates the analysis and design of ML-based clustering algorithms using multiple visualization features such as semantic zoom, glyphs, and histograms.

In recent years, many novel approaches have been propsed to exploring complex data sets. However, little guidance is available for designers to create similar solutions and to reuse established patterns. This paper is building upon our previous work that covers the development of a construction kit to support designers in creating new visual search interfaces. It provides a set of elements and patterns that can be easily combined with each other. In this paper, we present different application scenarios for using the construction kit within the design process.

Data Analysts have to deal with an ever-growing amount of data resources. One way to make sense of this data is to extract features and use clustering algorithms to group items according to a similarity measure. Algorithm developers are challenged when evaluating the performance of the algorithm since it is hard to identify features that influence the clustering. Moreover, many algorithms can be trained using a semi-supervised approach, where human users provide ground truth samples by manually grouping single items. Hence, visualization techniques are needed that help data analysts achieve their goal in evaluating Big data clustering algorithms. In this context, Multidimensional Scaling (MDS) has become a prominent visualization tool. In this paper, we propose a combination with glyphs that can provide a detailed view of specific features involved in MDS. In consequence, human users can understand, adjust, and ultimately improve clustering algorithms. We present a thorough glyph design, which is founded in a comprehensive survey of related work and report the results of a controlled experiments, where participants solved data analysis tasks with both glyphs and a traditional textual display of data values.

Business Intelligence applications often handle data sets that contain uncertain values. In this work we focus on product costing, which deals with the average costs of product components - that vary significantly based on many factors such as inflation, exchange rates, and commodity prices. After experts provide the uncertainty information for single items, decision makers need to quickly understand the cost uncertainties within the hierarchical data structure of the complete product.

TagStar is designed to support users during the classification of data items based on a multidimensional classification scheme. We also intend to support experts when analyzing the database. Analytic features of the presented visual indexing system allow experts to discover insufficiently described resources with little effort. The resulting interface concept is based on star glyphs to visualize the multivariate data.

Interaction design for mobile applications is challenging due to the diversity of technologies and devices. In addition, the now ubiquitous multi-touch screens demand novel and engaging interface metaphors. In this paper, we report insights and three practical results from a workshop with undergraduate students. The aim was to experiment with new technologies by providing a set of creativity techniques for ideation. By tackling interaction design both for tablets and smartphones, flexible interface metaphors were developed.

Finding new and compelling approaches to interaction design for natural user interfaces, is challenging. The Natural Interface Exploration studio will offer participants the opportunity to explore interaction design for natural user interfaces based on physical substances that are used in everyday life. Studio organizers will present an overview of their methodology, providing examples of their experience [1, 2] and comparing it to other approaches. They will demonstrate how they analyze natural substances regarding the aspects of visualization and interaction and what kind of interfaces resulted [3, 4, 5] from these findings in initial workshops (see Figure 3 and 4).

In this paper we describe DepthTouch, an installation which explores future interactive surfaces and features elastic feedback, allowing the user to go deeper than with regular multi-touch surfaces. DepthTouch's elastic display allows the user to create valleys and ascending slopes by depressing or grabbing its textile surface. We describe the experimental approach for eliciting appropriate interaction metaphors from interaction with real materials and the resulting digital prototype.

Accustomed to traditional user experiences with mouse and keyboard, designers are challenged to break free and find new and compelling approaches to interaction design for natural user interfaces. Tangible and embodied interaction works in parallel, is quick, and allows cooperative work. This exploration serves to inspire and provoke critical reflection on interaction design for natural user interfaces based on physical substances that are used in everyday life -- like eggs, soap bubbles, and magnets.