Hans-Jörg Schulz

Aarhus University | AU · Department of Computer Science

Visualization has become an important ingredient of data analysis, supporting users in exploring data and confirming hypotheses. At the beginning of a visual data analysis process, data characteristics are often assessed in an initial data profiling step. These include, for example, statistical properties of the data and information on the data’s w...

The process of identifying visualization requirements is an important part of every visualization researcher's and practitioner's job. Nevertheless, the scientific literature is rather sparse on this topic, usually resorting to some form of user-centered design that is rarely further detailed. In this paper, we give an account of our procedure, our...

Understanding large multidimensional datasets is one of the most challenging problems in visual data exploration. One key challenge that increases the size of the exploration space is the number of views that one can generate from a single dataset, based on the use of multiple parameter values and exploration paths. Often, no such single view conta...

The visualization of categorical datasets is an open field of research. While a number of standard diagramming techniques exist to investigate data distributions across multiple properties, these are rarely geared to take advantage of additional data properties -- either given or derived. As a result, the data display is not as expressive as it cou...

The field of tools for data visualization has been growing in recent years, with each tool contributing new ways to create and work with visualizations, and each offering a specialized set of features, interaction metaphors and user interfaces. This means on one hand that users have a wide choice in visualization tools. On the other hand, though, t...

Partial digestion of milk proteins leads to the formation of numerous bioactive peptides. Previously, our research team thoroughly examined the decades of existing literature on milk bioactive peptides across species to construct the milk bioactive peptide database (MBPDB). Herein, we provide a comprehensive update to the data within the MBPDB and...

Progressive visual analytics (PVA) allows analysts to maintain their flow during otherwise long-running computations by producing early, incomplete results that refine over time, for example, by running the computation over smaller partitions of the data. These partitions are created using sampling, whose goal it isto draw samples of the dataset su...

Guidance in visual analytics aims to support users in accomplishing their analytical goals and generating insights. Different approaches for guidance are widely adopted in many tools and frameworks for various purposes – from helping to focus on relevant data subspaces to selecting suitable visualization techniques. With each of these different pur...

The visual analysis of retinal data contributes to the understanding of a wide range of eye diseases. For the evaluation of cross-sectional studies, ophthalmologists rely on workflows and toolsets established in their work environment. That is, they know what tools and data are needed at each step of their workflow. Yet, manually operating the vari...

Progressive Visual Analytics enables analysts to interactively work with partial results from long-running computations early on instead of forcing them to wait. For very large datasets, the first step is to divide that input data into smaller chunks using sampling, which are then passed down the progressive analysis pipeline all the way to their p...

One of the challenges that GIS users in diverse, distributed teams face these days is being able to efficiently collaborate, both across workspaces and tools. To that end, we present MapBlender, a cybercartographic application that fosters geocollaboration by adapting a collaboration-first approach, placing users and their GIS tools on equal footin...

Progressive visual analytics allows users to interact with early, partial results of long-running computations on large datasets. In this context, computational steering is often brought up as a means to prioritize the progressive computation. This is meant to focus computational resources on data subspaces of interest, so as to ensure their comput...

Visual analysis of unknown data requires the combined use of various functions that are often part of standalone visual analytics (VA) tools. Performing cross-tool visual analysis with standalone VA tools, however, is a challenging and cumbersome endeavor. Some dedicated frameworks address this issue, yet in order to utilize any of them, a visual a...

GPU-FAST-PROCLUS is a GPU-parallelized algorithm for projected clustering based on the-medoids approach. It speeds up clustering to allow for real-time interaction-even for datasets of millions of items. Interactivity allows users to quickly determine sensible clustering parameters such as the number of clusters , provided a suitable visualization...

Stacked area charts are a common visualisation type for sets of time series. Yet, they are also known to be challenging to read, in particular if the time series exhibit much fluctuation or even abrupt changes. In this paper, we introduce a novel approach to improving the layout of stacked area charts by means of reordering the time series in the s...

While it is common to use multiple independent analysis tools in combination, it is still cumbersome to carry out a cross-tool visual analysis. Some dedicated frameworks addressing this issue exist, yet in order to use them, a Visual Analytics tool must support their API or architecture. In this paper, we do not rely on a single predetermined excha...

In visualization, user guidance has become an essential concept to aid users in making informed decisions ranging from what subsets to focus on in the data space to which regions to explore in the view space. To guide users, predominantly visual cues like colors or arrows are used to indicate particular targets or directions. In this paper, we expl...

Trust is a fundamental factor in how users engage in interactions with Visual Analytics (VA) systems. While the importance of building trust to this end has been pointed out in research, the aspect that trust can also be misplaced is largely ignored in VA so far. This position paper addresses this aspect by putting trust calibration in focus – i.e....

Over the past years, the visualization of large and complex data sets brought up various Visual Analytics (VA) tools in order to solve domain-specific tasks. These VA tools are typically implemented as individual software components in data-flow-oriented models, meaning that data is transferred from one component to the next. While most VA framewor...

Cartographic maps have been shown to provide cognitive benefits when interpreting data in relation to a geographic location. In visualization, the term map‐like describes techniques that incorporate characteristics of cartographic maps in their representation of abstract data. However, the field of map‐like visualization is vast and currently lacks...

As time series datasets are growing in size, data reduction approaches like PAA and SAX are used to keep them storable and analyzable. Yet, finding the right trade-off between data reduction and remaining utility of the data is a challenging problem. So far, it is either done in a user-driven way and offloaded to the analyst, or it is determined in...

Some visualizations have a more regular visual appearance than others. For example, while stream graphs or force-directed network layouts feature a unique, almost organic look&feel, matrices or unit treemaps can become rather bland, grid-like visualizations in which one data item is hard to tell apart from the next. In this paper, we investigate th...

The ongoing proliferation and differentiation of Visual Analytics to various application domains and usage scenarios has also resulted in a fragmentation of the software landscape for data analysis. Highly specialized tools are available that focus on one particular analysis task in one particular application domain. The interoperability of these t...

The amount of generated and analyzed data is ever increasing, and processing such large data sets can take too long in situations where time-to-decision or fluid data exploration are critical. Progressive visual analytics (PVA) has recently emerged as a potential solution that allows users to analyze intermediary results during the computation with...

A key component in using Progressive Visual Analytics (PVA) is to be able to gauge the quality of intermediate analysis outcomes. This is necessary in order to decide whether a current partial outcome is already good enough to cut a long-running computation short and to proceed. To aid in this process, we propose ten fundamental quality indicators...

Data comparison in various domains can be effectively supported by visual analytics solutions combining interactive visualization and algorithmic analysis. The design of such solutions should match the comparison problem at hand: the input data and the task specification. This requires several choices from algorithm to visual design and interaction...

Progressive Visual Analytics (PVA) has gained increasing attention over the past years. It brings the user into the loop during otherwise long-running and non-transparent computations by producing intermediate partial results. These partial results can be shown to the user for early and continuous interaction with the emerging end result even while...

At VAST 2016, a characterization of guidance has been presented. It includes a definition of guidance and a model of guidance based on van Wijk's model of visualization. This note amends the original characterization of guidance in two aspects. First, we provide a clarification of what guidance actually is (and is not). Second, we insert into the m...

Visual analytics (VA) is typically applied in scenarios where complex data has to be analyzed. Unfortunately, there is a natural correlation between the complexity of the data and the complexity of the tools to study them. An adverse effect of complicated tools is that analytical goals are more difficult to reach. Therefore, it makes sense to consi...

We introduce a simple and useful view for observing graph streams. They are viewed as collections of edge events where each edge has associated a set of time-dependent statistics that include firing rate, recency, and persistence. The activity rate of any subgraph is expressed as an aggregation of its corresponding edge statistics. Salient subgraph...

With today's technical possibilities, a stable visualization scenario can no longer be assumed as a matter of course, as underlying data and targeted display setup are much more in flux than in traditional scenarios. Incremental visualization approaches are a means to address this challenge, as they permit the user to interact with, steer, and chan...

Generating the "right" visual representation for the data and task at hand remains a standing challenge in visualization research and practice. A variety of different approaches to produce visual representations have been proposed in the past, including such noteworthy instances as visualization by example and visualization by analogy. With this pa...

The research field of Visual Computing encompasses everything graphical in computer science - from the synthesis and processing of graphical content to its human consumption. This broad spectrum includes multiple other fields that constitute research disciplines in their own right, such as perception, visualization, multimedia, virtual and augmente...

Network analysis has become an important approach in studying complex spatiotemporal behaviour within geophysical observation and simulation data. This new field produces increasing numbers of large geo-referenced networks to be analysed. Particular focus lies currently on the network analysis of the complex statistical interrelationship structure...

The visual analysis of complex geo-spatial data is a challenging task. Typically, different views are used to communicate different aspects. With changing topics of interest, however, novel views are required. This leads to dynamically changing presentations of multiple views. This paper introduces a novel approach to support such scenarios. It all...

Network analysis has become an important approach in studying complex spatiotemporal behaviour within geophysical observation and simulation data. This new field produces increasing amounts of large geo-referenced networks to be analysed. Particular focus lies currently on the network analysis of the complex statistical interrelationship structure...

Ongoing research on information visualization has produced an ever-increasing number of visualization designs. Despite this activity, limited progress has been made in categorizing this large number of information visualizations. This makes understanding their common design features challenging, and obscures the yet unexplored areas of novel design...

Graph visualization is an important field in information visualization that is centered on the graphical display of graph-structured data. Yet real world data is rarely just graph-structured, but instead exhibits multiple facets, such as multivariate attributes, or spatial and temporal frames of reference. In an effort to display different facets o...

With today's technical possibilities, a stable visualization scenario can no longer be assumed, as underlying data and operations are much more in flux than in traditional scenarios. We term such dynamic visualization scenarios online visualization. In contrast to traditional offline visualization that rely on monolithic visualization operators and...

Interaction (or human-computer interaction/HCI) is a key ingredient of modern visualization and visual analysis systems. It allows the user to manage the data and to explore its different aspects, as well as to shape its visual representation and to observe it from different perspectives-ultimately to pursue the user's analytical goal. Yet, so far...

Due to the spread of mobile handheld devices, new application fields like technical maintenance and mechanical inspections open up to their use. Yet, to fully benefit from their modern technical capabilities, standard interaction techniques need to be revised and new ways of accessing the abundance of technical documentation on the handheld need to...

The visualization of simulation trajectories is a well-established approach to analyze simulated processes. Likewise, the visualization of the parameter space that configures a simulation is a well-known method to get an overview of possible parameter combinations. This paper follows the premise that both of these approaches are actually two sides...

Exact order-independent transparency (OIT) rendering is memory demanding because it requires per-pixel blending of an unknown number of fragments that need to be stored and sorted before compositing. In this paper, we describe the Dynamic Fragment Buffer ...

Knowledge about visualization tasks plays an important role in choosing or building suitable visual representations to pursue them. Yet, tasks are a multi-faceted concept and it is thus not surprising that the many existing task taxonomies and models all describe different aspects of tasks, depending on what these task descriptions aim to capture....

In many domains, it becomes more and more common that an analysis spans various interlinked data sources that we collectively term data landscape. Yet for the selection of appropriate data sources from the wide range of available ones, current approaches and systems rarely offer more support than a File-Open dialog. This paper presents a visualizat...

Every visualization researcher and practitioner knows the painful experience of a beautifully designed network layout breaking down once the input graph scales up to realistic node and edge counts. The resulting "hairball" suffers from cluttering and over-plotting to an extreme that renders it unusable for any practical purposes. Since researchers...

Expression analysis of ~omics data using microarrays has become a standard procedure in the life sciences. However, microarrays are subject to technical limitations and errors, which render the data gathered likely to be uncertain. While a number of approaches exist to target this uncertainty statistically, it is hardly ever even shown when the dat...

Spatiotemporal data often relates to different levels of granularity in space, time, and data. Yet, bringing these levels together for an integrated visual exploration across levels poses a challenge up to this day. With this paper, we aim to provide a first solution approach to this challenge, which decomposes the data in its various levels to be...

The π-calculus, in particular its stochastic version the stochastic π-calculus, is a common modeling formalism to concisely describe the chemical reactions occurring in biochemical systems. However, it remains largely unexplored how to transform a biochemical model expressed in the stochastic π-calculus back into a set of meaningful reactions. To t...

Large dynamic networks are targets of analysis in many fields. Tracking temporal changes at scale in these networks is challenging due in part to the fact that small changes can be missed or drowned-out by the rest of the network. For static networks, current approaches allow the identification of specific network elements within their context. How...

Visual analysis spanning multiple data sources usually requires the integration of multiple specialized applications to handle their heterogeneity. This is also true in manufacturing, where data about orders, personnel, workloads, maintenance, etc. must be analyzed together to make well-founded management decisions. Yet, the orchestration of multip...

In response to the large number of existing tree layouts, generic "meta-layouts" have recently been proposed. These generic ap-proaches utilize layout design spaces to pinpoint a tree drawing with desired characteristics in the wealth of available drawing options and parameters. While design-space-based generic layouts work well for the confined se...

Applying and parameterizing advanced visualization tools for solving different problems can be difficult for users, who are not necessarily visualization experts. The visualization community has begun to address this problem by developing assistive approaches under different labels. In this work, we propose an initial version of a characterization...

Relationships are omnipresent in data, views and in how we interact with visualization tools. This tutorial discusses how such relationships can be visually expressed, a process we call linking. The tutorial addresses the three questions, what, how and when to link in three separate parts. The first part-what to link-explains that not only data, bu...

In systems biology, analyzing simulation trajectories at multiple scales is a common approach when subtle, detailed behavior and fundamental, overall behavior of a modeled system are to be investigated at the same time. A variety of multiscale visualization techniques provide solutions to handle and depict data at different scales. Yet the mere exi...

For multilevel data, levels are not the result of a hierarchical aggregation, but contain independently produced data. While first visualization techniques for this kind of data exist, suitable interactive exploration techniques have rarely been investigated so far. In this paper, we introduce means for the analysis, representation, and exploration...

Identification and characterization of cancer subtypes are important areas of research that are based on the integrated analysis of multiple heterogeneous genomics datasets. Since there are no tools supporting this process, much of this work is done using ad-hoc scripts and static plots, which is inefficient and limits visual exploration of the dat...

Understanding how data evolves in space and time is an essential task in many application domains. Despite the numerous visual methods that have been proposed to facilitate this task (e.g., showing the data on a map or plotting a time graph), the exploration of data with references to space and time still remains challenging. In this work, we prese...

Large volumes of real-world data often exhibit inhomogeneities: vertically in the form of correlated or independent dimensions and horizontally in the form of clustered or scattered data items. In essence, these inhomogeneities form the patterns in the data that researchers are trying to find and understand. Sophisticated statistical methods are av...