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.

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.

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.

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.

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.