FWF Project, 2025

Visual Interactive Space-Time Segmentation

Markus Bögl, Nikolaus Piccolotto, Silvia Miksch

The Austrian Science Fund (FWF) accepted our proposal to research knowledge-assisted segmentations of spatiotemporal data! The funding amount is about 450,000 Euro over 3 years.

DOI: 10.55776/PAT6437224

TU Wien Bibliothek, 2024

Ph.D. Thesis

Nikolaus Piccolotto

I defended my Ph.D.!

DOI: 10.34726/hss.2025.128662

IEEE Transactions on Visualization and Computer Graphics, 2025

UnDRground Tubes: Exploring Spatial Data With Multidimensional Projections and Set Visualization

Nikolaus Piccolotto, Markus Wallinger, Silvia Miksch, Markus Bögl

In various scientific and industrial domains, analyzing multivariate spatial data, i.e., vectors associated with spatial locations, is common practice. To analyze those datasets, analysts may turn to methods such as Spatial Blind Source Separation (SBSS). Designed explicitly for spatial data analysis, SBSS finds latent components in the dataset and is superior to popular non-spatial methods, like PCA. However, when analysts try different tuning parameter settings, the amount of latent components complicates analytical tasks. Based on our years-long collaboration with SBSS researchers, we propose a visualization approach to tackle this challenge. The main component is UnDRground Tubes (UT), a general-purpose idiom combining ideas from set visualization and multidimensional projections. We describe the UT visualization pipeline and integrate UT into an interactive multiple-view system. We demonstrate its effectiveness through interviews with SBSS experts, a qualitative evaluation with visualization experts, and computational experiments. SBSS experts were excited about our approach. They saw many benefits for their work and potential applications for geostatistical data analysis more generally. UT was also well received by visualization experts. Our benchmarks show that UT projections and its heuristics are appropriate.

DOI: 10.1109/TVCG.2024.3456314

VIS 2024 Short Papers, 2024

On Combined Visual Cluster and Set Analysis

Nikolaus Piccolotto, Markus Wallinger, Silvia Miksch, Markus Bögl

Real-world datasets often consist of quantitative and categorical variables. The analyst needs to focus on either kind separately or both jointly. We proposed a visualization technique tackling these challenges that supports visual cluster and set analysis. In this paper, we investigate how its visualization parameters affect the accuracy and speed of cluster and set analysis tasks in a controlled experiment. Our findings show that, with the proper settings, our visualization can support both task types well. However, we did not find settings suitable for the joint task, which provides opportunities for future research.

DOI: 10.1109/VIS55277.2024.00034

Journal of Data Science, Statistics, and Visualization, 2024

Visual Interactive Parameter Selection for Temporal Blind Source Separation

Claudia Capello, Nikolaus Piccolotto, Christoph Muehlmann, Markus Bögl, Peter Filzmoser, Silvia Miksch, Klaus Nordhausen

Many fields of science and industry collect and analyze multivariate time-varying measurements, e.g., healthcare, geophysics, or finance. Such data is often high-dimensional, correlated, and noisy. Experts are interested in latent components of the dataset, but due to the properties above, these are difficult to obtain. Temporal Blind Source Separation (TBSS) is a suitable and well-established framework for these data. However, the wide choice of methods and their tuning parameters impede the effective use of TBSS in practice. Visual Analytics (VA) aims to create powerful analytic tools by combining the strengths of humans and computers. We designed, developed, and evaluated VA contributions in previous work to support TBSS-related analysis tasks. This paper highlights the benefits and opportunities of VA concepts for statistics-oriented problems. We demonstrate how their analysis workflow can be supported using an important TBSS application example with a real-world dataset of meteorological measurements in Italy.

DOI: 10.52933/jdssv.v4i3.82

IEEE Transactions on Visualization and Computer Graphics, 2024

Data Type Agnostic Visual Sensitivity Analysis

Nikolaus Piccolotto, Markus Bögl, Christoph Muehlmann, Klaus Nordhausen, Peter Filzmoser, Johanna Schmidt, Silvia Miksch

Modern science and industry rely on computational models for simulation, prediction, and data analysis. Spatial blind source separation (SBSS) is a model used to analyze spatial data. Designed explicitly for spatial data analysis, it is superior to popular non-spatial methods, like PCA. However, a challenge to its practical use is setting two complex tuning parameters, which requires parameter space analysis. In this paper, we focus on sensitivity analysis (SA). SBSS parameters and outputs are spatial data, which makes SA difficult as few SA approaches in the literature assume such complex data on both sides of the model. Based on the requirements in our design study with statistics experts, we developed a visual analytics prototype for data type agnostic visual sensitivity analysis that fits SBSS and other contexts. The main advantage of our approach is that it requires only dissimilarity measures for parameter settings and outputs. We evaluated the prototype heuristically with visualization experts and through interviews with two SBSS experts. In addition, we show the transferability of our approach by applying it to microclimate simulations. Study participants could confirm suspected and known parameter-output relations, find surprising associations, and identify parameter subspaces to examine in the future. During our design study and evaluation, we identified challenging future research opportunities.

DOI: 10.1109/TVCG.2023.3327203

Computer Graphics Forum, 2023

Visual Parameter Space Exploration in Time and Space

Nikolaus Piccolotto, Markus Bögl, Silvia Miksch

Computational models, such as simulations, are central to a wide range of fields in science and industry. Those models take input parameters and produce some output. To fully exploit their utility, relations between parameters and outputs must be understood. These include, for example, which parameter setting produces the best result (optimization) or which ranges of parameter settings produce a wide variety of results (sensitivity). Such tasks are often difficult to achieve for various reasons, for example, the size of the parameter space, and supported with visual analytics. In this paper, we survey visual parameter space exploration (VPSE) systems involving spatial and temporal data. We focus on interactive visualizations and user interfaces. Through thematic analysis of the surveyed papers, we identify common workflow steps and approaches to support them. We also identify topics for future work that will help enable VPSE on a greater variety of computational models.

DOI: 10.1111/cgf.14785

EuroVis Workshop on Visual Analytics (EuroVA), 2023

Multi-Ensemble Visual Analytics via Fuzzy Sets

Nikolaus Piccolotto, Markus Bögl, Silvia Miksch

Analysis of ensemble datasets, i.e., collections of complex elements such as geochemical maps, is widespread in science and industry. The elements' complexity arises from the data they capture, which are often multivariate or spatio-temporal. We speak of multi-ensemble datasets when the analysis pertains to multiple ensembles. While many visualization approaches were suggested for ensemble datasets, multi-ensemble datasets remain comparatively underexplored. Our years-long collaboration with statisticians and geochemists taught us that they frame many questions about multi-ensemble data as set operations. E.g., what are the most common members (intersection of ensembles), or what features exist in one member but not another (difference of members)? As classical crisp set relations cannot account for the elements' complexity, we propose to model multi-ensembles as fuzzy relations. We provide examples of fuzzy set-based queries on a multi-ensemble of geochemical maps and integrate this approach into an existing ensemble visualization pipeline. We evaluated two visualizations obtained by applying this pipeline with experts in geochemistry and statistics. The experts confirmed known information and got directions for further research, which is one Visual Analytics (VA) goal. Hence, our proposal is highly promising for an interactive VA approach.

DOI: 10.2312/eurova.20231092

Computer Graphics Forum, 2022

Visual Parameter Selection for Spatial Blind Source Separation

Nikolaus Piccolotto, Markus Bögl, Christoph Muehlmann, Klaus Nordhausen, Peter Filzmoser, Silvia Miksch

Analysis of spatial multivariate data, i.e., measurements at irregularly-spaced locations, is a challenging topic in visualization and statistics alike. Such data are integral to many domains, e.g., indicators of valuable minerals are measured for mine prospecting. Popular analysis methods, like PCA, often by design do not account for the spatial nature of the data. Thus they, together with their spatial variants, must be employed very carefully. Clearly, it is preferable to use methods that were specifically designed for such data, like spatial blind source separation (SBSS). However, SBSS requires two tuning parameters, which are themselves complex spatial objects. Setting these parameters involves navigating two large and interdependent parameter spaces, while also taking into account prior knowledge of the physical reality represented by the data. To support analysts in this process, we developed a visual analytics prototype. We evaluated it with experts in visualization, SBSS, and geochemistry. Our evaluations show that our interactive prototype allows to define complex and realistic parameter settings efficiently, which was so far impractical. Settings identified by a non-expert led to remarkable and surprising insights for a domain expert. Therefore, this paper presents important first steps to enable the use of a promising analysis method for spatial multivariate data.

DOI: 10.1111/cgf.14530

Visual Informatics, 2022

TBSSvis: Visual Analytics for Temporal Blind Source Separation

Nikolaus Piccolotto, Markus Bögl, Theresia Gschwandtner, Christoph Muehlmann, Klaus Nordhausen, Peter Filzmoser, Silvia Miksch

Temporal Blind Source Separation (TBSS) is used to obtain the true underlying processes from noisy temporal multivariate data, such as electrocardiograms. TBSS has similarities to Principal Component Analysis (PCA) as it separates the input data into univariate components and is applicable to suitable datasets from various domains, such as medicine, finance, or civil engineering. Despite TBSS’s broad applicability, the involved tasks are not well supported in current tools, which offer only text-based interactions and single static images. Analysts are limited in analyzing and comparing obtained results, which consist of diverse data such as matrices and sets of time series. Additionally, parameter settings have a big impact on separation performance, but as a consequence of improper tooling, analysts currently do not consider the whole parameter space. We propose to solve these problems by applying visual analytics (VA) principles. Our primary contribution is a design study for TBSS, which so far has not been explored by the visualization community. We developed a task abstraction and visualization design in a user-centered design process. Task-specific assembling of well-established visualization techniques and algorithms to gain insights in the TBSS processes is our secondary contribution. We present TBSSvis, an interactive web-based VA prototype, which we evaluated extensively in two interviews with five TBSS experts. Feedback and observations from these interviews show that TBSSvis supports the actual workflow and combination of interactive visualizations that facilitate the tasks involved in analyzing TBSS results.

DOI: 10.1016/j.visinf.2022.10.002

Computer Graphics Forum, 2020

Guide Me in Analysis: A Framework for Guidance Designers

Davide Ceneda, Natalia Andrienko, Gennady Andrienko, Theresia Gschwandtner, Silvia Miksch, Nikolaus Piccolotto, Tobias Schreck, Marc Streit, Josef Suschnigg, Christian Tominski

Guidance is an emerging topic in the field of visual analytics. Guidance can support users in pursuing their analytical goals more efficiently and help in making the analysis successful. However, it is not clear how guidance approaches should be designed and what specific factors should be considered for effective support. In this paper, we approach this problem from the perspective of guidance designers. We present a framework comprising requirements and a set of specific phases designers should go through when designing guidance for visual analytics. We relate this process with a set of quality criteria we aim to support with our framework, that are necessary for obtaining a suitable and effective guidance solution. To demonstrate the practical usability of our methodology, we apply our framework to the design of guidance in three analysis scenarios and a design walk-through session. Moreover, we list the emerging challenges and report how the framework can be used to design guidance solutions that mitigate these issues.

DOI: 10.1111/cgf.14017