This article aims to foster a collaborative environment between the visual analytics and process mining communities by bringing together analysis methods, techniques, and tools from the process mining and visual analytics domains to devise a new knowledge-assisted, human-in-the-loop approach to process mining. Building on recent advances in methods emphasizing the role of human knowledge in analysis, we introduce knowledge-assisted interactive visual process mining (KAVA-PM) as a framework where analysts’ tacit knowledge and the externalizations of this knowledge play a key role. To achieve this, we extend an established conceptual model of KAVA that combines interactive visualizations and automated methods to support a richer process mining analysis practice that has human experts and their knowledge at its core. The paper outlines the key components of KAVA-PM as a conceptual model and its relations, proposes key analytical patterns, and demonstrates the use and validity of the patterns through usage scenarios. We then present challenges and open problems which we validate through a survey with experts. We anticipate that along with the conceptual model, these challenges will bring the VA and PM communities together along a shared research agenda where the role of humans and their knowledge is better established. • Conceptual model adapted to process mining for distinguishing between tacit and explicit knowledge. • Key research challenges validated by the visual analytics and process mining communities through an international survey.

Interpreting data visualizations is an essential skill in today's education, yet students often struggle with understanding unfamiliar formats. This study investigates how four learning materials - textbook, comic, video, and game - affect middle- and high school students' ability to interpret line charts, area charts, stacked area charts, and stream graphs. We conducted a comparative classroom study with 68 students, using pre- and post-tests, worksheet activities, and group discussions to assess learning outcomes and understanding. Our results show statistically significant improvement in students' understanding of stacked area charts and stream graphs, while no significant differences between the learning materials were found. This suggests that more factors than initially anticipated - such as engagement, motivation and active learning strategies - influence the learning outcome. The analysis of the worksheets revealed that while students could infer surface-level insights from charts, over 70% struggled to identify underlying patterns or relationships. Additionally, a common challenge across all learning materials was reading fatigue, which often led students to skim content, disengage, or misinterpret key information. These findings highlight the need for educational tools and approaches that foster deeper understanding of unfamiliar visualizations, reduce cognitive load, and encourage active engagement.

Abstract In this paper, we introduce instructional comics to explain concepts and routines in data visualization tools. As tools for visual data exploration proliferate, there is a growing need for tailored training and onboarding demonstrating interfaces, concepts, and interactions. Building on recent research in visualization education, we detail our iterative process of designing instructional comics for four different types of instructional content. Through a mixed‐method eye‐tracking study involving 20 participants, we analyze how people engage with these comics when using a new visualization tool, and validate our design choices. We interpret observed behaviors as unique affordances of instructional comics, supporting their use during tasks and complementing traditional instructional methods like video tutorials and workshops, and formulate six guidelines to inform the design of future instructional comics for visualization.

Abstract Physicalizations, which combine perceptual and sensorimotor interactions, offer an immersive way to comprehend complex data visualizations by stimulating active construction and manipulation. This study investigates the impact of personal construction on the comprehension of physicalized networks. We propose a physicalization toolkit— NODKANT —for constructing modular node‐link diagrams consisting of a magnetic surface, 3D printable and stackable node labels, and edges of adjustable length. In a mixed‐methods between‐subject lab study with 27 participants, three groups of people used NODKANT to complete a series of low‐level analysis tasks in the context of an animal contact network. The first group was tasked with freely constructing their network using a sorted edge list, the second group received step‐by‐step instructions to create a predefined layout, and the third group received a pre‐constructed representation. While free construction proved on average more time‐consuming, we show that users extract more insights from the data during construction and interact with their representation more frequently, compared to those presented with step‐by‐step instructions. Interestingly, the increased time demand cannot be measured in users' subjective task load. Finally, our findings indicate that participants who constructed their own representations were able to recall more detailed insights after a period of 10–14 days compared to those who were given a pre‐constructed network physicalization. All materials, data, code for generating instructions, and 3D printable meshes are available on https://osf.io/tk3g5/ .

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.

Abstract The research communities studying visualization and sonification for data display and analysis share exceptionally similar goals, essentially making data of any kind interpretable to humans. One community does so by using visual representations of data, and the other community employs auditory (non‐speech) representations of data. While the two communities have a lot in common, they developed mostly in parallel over the course of the last few decades. With this STAR, we discuss a collection of work that bridges the borders of the two communities, hence a collection of work that aims to integrate the two techniques into one form of audiovisual display, which we argue to be “more than the sum of the two.” We introduce and motivate a classification system applicable to such audiovisual displays and categorize a corpus of 57 academic publications that appeared between 2011 and 2023 in categories such as reading level, dataset type, or evaluation system, to mention a few. The corpus also enables a meta‐analysis of the field, including regularly occurring design patterns such as type of visualization and sonification techniques, or the use of visual and auditory channels, showing an overall diverse field with different designs. An analysis of a co‐author network of the field shows individual teams without many interconnections. The body of work covered in this STAR also relates to three adjacent topics: audiovisual monitoring, accessibility, and audiovisual data art. These three topics are discussed individually in addition to the systematically conducted part of this research. The findings of this report may be used by researchers from both fields to understand the potentials and challenges of such integrated designs while hopefully inspiring them to collaborate with experts from the respective other field.

Abstract One of the commonly used visualization techniques for multivariate data is the parallel coordinates plot. It provides users with a visual overview of multivariate data and the possibility to interactively explore it. While pattern recognition is a strength of the human visual system, it is also a strength of the auditory system. Inspired by the integration of the visual and auditory perception in everyday life, we introduce an audio-visual analytics design named Parallel Chords combining both visual and auditory displays. Parallel Chords lets users explore multivariate data using both visualization and sonification through the interaction with the axes of a parallel coordinates plot. To illustrate the potential of the design, we present (1) prototypical data patterns where the sonification helps with the identification of correlations, clusters, and outliers, (2) a usage scenario showing the sonification of data from non-adjacent axes, and (3) a controlled experiment on the sensitivity thresholds of participants when distinguishing the strength of correlations. During this controlled experiment, 35 participants used three different display types, the visualization, the sonification, and the combination of these, to identify the strongest out of three correlations. The results show that all three display types enabled the participants to identify the strongest correlation — with visualization resulting in the best sensitivity. The sonification resulted in sensitivities that were independent from the type of displayed correlation, and the combination resulted in increased enjoyability during usage.

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.

Visualization onboarding supports users in reading, interpreting, and extracting information from visual data representations. General-purpose onboarding tools and libraries are applicable for explaining a wide range of graphical user interfaces but cannot handle specific visualization requirements. This paper describes a first step towards developing an onboarding library called VisAhoi, which is easy to integrate, extend, semi-automate, reuse, and customize . VisAhoi supports the creation of onboarding elements for different visualization types and datasets. We demonstrate how to extract and describe onboarding instructions using three well-known high-level descriptive visualization grammars — Vega-Lite, Plotly.js, and ECharts. We show the applicability of our library by performing two usage scenarios that describe the integration of VisAhoi into a VA tool for the analysis of high-throughput screening (HTS) data and, second, into a Flourish template to provide an authoring tool for data journalists for a treemap visualization. We provide a supplementary website ( https://datavisyn.github.io/visAhoi/ ) that demonstrates the applicability of VisAhoi to various visualizations, including a bar chart, a horizon graph, a change matrix/heatmap, a scatterplot, and a treemap visualization.

Abstract Both sonification and visualization convey information about data by effectively using our human perceptual system, but their ways to transform the data differ. Over the past 30 years, the sonification community has demanded a holistic perspective on data representation, including audio-visual analysis, several times. A design theory of audio-visual analysis would be a relevant step in this direction. An indispensable foundation for this endeavor is a terminology describing the combined design space. To build a bridge between the domains, we adopt three of the established theoretical constructs from visualization theory for the field of sonification. The three constructs are the spatial substrate, the visual mark, and the visual channel. In our model, we choose time to be the temporal substrate of sonification. Auditory marks are then positioned in time, such as visual marks are positioned in space. Auditory channels are encoded into auditory marks to convey information. The proposed definitions allow discussing visualization and sonification designs as well as multi-modal designs based on a common terminology. While the identified terminology can support audio-visual analytics research, it also provides a new perspective on sonification theory itself.

In their daily lives, people use more than one sense to perceive and interpret their environment. Likewise, audio-visual interfaces can support human data analysts better than interfaces relying on just one sense. While the research communities of sonification and visualization have both carried out extensive research on the auditory and visual representation of data, comparatively little is known about their systematic and complementary combination for data analysis. After two workshops at Audio Mostly 2021 and IEEE VIS, this 3rd workshop on audio-visual analytics continues building a community of researchers interested in combining visualization and sonification.

Visual exploration of large and complex data is becoming increasingly important in different domains. However, non-experts in the field of visual data analysis often have problems with correctly reading and interpreting information from visualization idioms that are new to them. To make new forms of visualizations understandable and interpretable for a broad range of audiences, visualization onboarding methods can support users. However, it is unclear whether concrete or abstract materials yield better results to foster learning. In order to answer this question, we conducted a within-subject study with 40 students to compare abstract and concrete onboarding messages for a treemap visualization. The results show that (1) concrete onboarding messages are more helpful than abstract, whereas the length of the abstract messages is ranked higher; (2) abstract onboarding messages lead to more valuable descriptions; and (3) either concrete or abstract onboarding messages can lead to high valuable insights.

A typical problem in Visual Analytics (VA) is that users are highly trained experts in their application domains, but have mostly no experience in using VA systems. Thus, users often have difficulties interpreting and working with visual representations. To overcome these problems, user assistance can be incorporated into VA systems to guide experts through the analysis while closing their knowledge gaps. Different types of user assistance can be applied to extend the power of VA, enhance the user’s experience, and broaden the audience for VA. Although different approaches to visualization onboarding and guidance in VA already exist, there is a lack of research on how to design and integrate them in effective and efficient ways. Therefore, we aim at putting together the pieces of the mosaic to form a coherent whole. Based on the Knowledge-Assisted Visual Analytics model, we contribute a conceptual model of user assistance for VA by integrating the process of visualization onboarding and guidance as the two main approaches in this direction. As a result, we clarify and discuss the commonalities and differences between visualization onboarding and guidance, and discuss how they benefit from the integration of knowledge extraction and exploration. Finally, we discuss our descriptive model by applying it to VA tools integrating visualization onboarding and guidance, and showing how they should be utilized in different phases of the analysis in order to be effective and accepted by the user.

Comprehending and exploring large and complex data is becoming increasingly important for a diverse population of users in a wide range of application domains. Visualization has proven to be well-suited in supporting this endeavor by tapping into the power of human visual perception. However, non-experts in the field of visual data analysis often have problems with correctly reading and interpreting information from visualization idioms that are new to them. To support novices in learning how to use new digital technologies, the concept of onboarding has been successfully applied in other fields and first approaches also exist in the visualization domain. However, empirical evidence on the effectiveness of such approaches is scarce. Therefore, we conducted three studies with Amazon Mechanical Turk (MTurk) workers and students investigating visualization onboarding at different levels: (1) Firstly, we explored the effect of visualization onboarding, using an interactive step-by-step guide, on user performance for four increasingly complex visualization techniques with time-oriented data: a bar chart, a horizon graph, a change matrix, and a parallel coordinates plot. We performed a between-subject experiment with 596 participants in total. The results showed that there are no significant differences between the answer correctness of the questions with and without onboarding. Particularly, participants commented that for highly familiar visualization types no onboarding is needed. However, for the most unfamiliar visualization type — the parallel coordinates plot — performance improvement can be observed with onboarding. (2) Thus, we performed a second study with MTurk workers and the parallel coordinates plot to assess if there is a difference in user performances on different visualization onboarding types: step-by-step, scrollytelling tutorial, and video tutorial. The study revealed that the video tutorial was ranked as the most positive on average, based on a sentiment analysis, followed by the scrollytelling tutorial and the interactive step-by-step guide. (3) As videos are a traditional method to support users, we decided to use the scrollytelling approach as a less prevalent way and explore it in more detail. Therefore, for our third study, we gathered data towards users’ experience in using the in-situ scrollytelling for the VA tool Netflower. The results of the evaluation with students showed that they preferred scrollytelling over the tutorial integrated in the Netflower landing page. Moreover, for all three studies we explored the effect of task difficulty. In summary, the in-situ scrollytelling approach works well for integrating onboarding in a visualization tool. Additionally, a video tutorial can help to introduce interaction techniques of visualization.

Comprehending and exploring large and complex data is becoming increasingly important for a diverse population of users in a wide range of application domains. Visualization has proven to be well-suited in supporting this endeavor by tapping into the power of human visual perception. However, non-experts in the field of visual analysis often have difficulties in correctly reading and interpreting information from novel visualization idioms. Visualization onboarding can support novices in learning how to use new digital technologies. Therefore, we developed an interactive step-by-step guide and applied the method to four visualization techniques—a bar chart, a horizon graph, a change matrix, and a parallel coordinates plot. Results using Amazon Mechanical Turk workers show that there is a need for onboarding, especially for more complex visualization techniques. We further investigated the perception and rating of a scrollytelling and a video tutorial for the most unfamiliar visualization—the parallel coordinates plot. A comparison between the three onboarding methods indicates that participants appreciated the easy-to-understand examples, the precise wording of the onboarding messages in a step-by-step manner, and the introduction of interaction concepts by highlighting the most relevant information over all onboarding methods. The video tutorial supported the introduction of unknown interaction techniques best.

Abstract Journalists need visual interfaces that cater to the exploratory nature of their investigative activities. In this paper, we report on a four‐year design study with data journalists. The main result is netflower, a visual exploration tool that supports journalists in investigating quantitative flows in dynamic network data for story‐finding. The visual metaphor is based on Sankey diagrams and has been extended to make it capable of processing large amounts of input data as well as network change over time. We followed a structured, iterative design process including requirement analysis and multiple design and prototyping iterations in close cooperation with journalists. To validate our concept and prototype, a workshop series and two diary studies were conducted with journalists. Our findings indicate that the prototype can be picked up quickly by journalists and valuable insights can be achieved in a few hours. The prototype can be accessed at: http://netflower.fhstp.ac.at/

In 2014, more than 10 million people in the US were affected by an ambulatory disability. Thus, gait rehabilitation is a crucial part of health care systems. The quantification of human locomotion enables clinicians to describe and analyze a patient's gait performance in detail and allows them to base clinical decisions on objective data. These assessments generate a vast amount of complex data which need to be interpreted in a short time period. We conducted a design study in cooperation with gait analysis experts to develop a novel Knowledge-Assisted Visual Analytics solution for clinical Gait analysis (KAVAGait). KAVAGait allows the clinician to store and inspect complex data derived during clinical gait analysis. The system incorporates innovative and interactive visual interface concepts, which were developed based on the needs of clinicians. Additionally, an explicit knowledge store (EKS) allows externalization and storage of implicit knowledge from clinicians. It makes this information available for others, supporting the process of data inspection and clinical decision making. We validated our system by conducting expert reviews, a user study, and a case study. Results suggest that KAVAGait is able to support a clinician during clinical practice by visualizing complex gait data and providing knowledge of other clinicians.

Multivariate, tabular data is one of the most common data structures used in many different domains. Over time, tables can undergo changes in both structure and content, which results in multiple versions of the same table. A challenging task when working with such derived tables is to understand what exactly has changed between versions in terms of additions/deletions, reorder, merge/split, and content changes. For textual data, a variety of commonplace "diff" tools exist that support the task of investigating changes between revisions of a text. Although there are some comparison tools which assist users in inspecting differences between multiple table instances, the resulting visualizations are often difficult to interpret or do not scale to large tables with thousands of rows and columns. To address these challenges, we developed TACO, an interactive comparison tool that visualizes the differences between multiple tables at various levels of detail. With TACO we show (1) the aggregated differences between multiple table versions over time, (2) the aggregated changes between two selected table versions, and (3) detailed changes between the selected tables. To demonstrate the effectiveness of our approach, we show its application by means of two usage scenarios.

Mobile devices are more and more used in parallel, esp. in the field of TV viewing as second screen devices. Such scenarios aim to enhance the viewers' user experience while watching TV. We designed and implemented a second screen prototype intended to be used in parallel to watching a TV documentary. It allows to interactively explore a combination of spatial and time-oriented data to extend and enrich the TV content. We evaluated our proto-type in a twofold approach, consisting of expert reviews and user evaluation. We identified different interaction habits in a second screen scenario and present its benefits in relation to documentaries.

Almost every visitor brings their own mobile device (e.g., smartphone or tablet) to the museum. Although, many museums include interactive exhibits (e.g., multi-touch tables), the visitors' own devices are rarely used as part of a device ecology. Currently, there is no suitable infrastructure to seamlessly link different devices in museums. Our approach is to integrate the visitor's own device in a multi-device ecology (MDE) in the museum to enhance the visitor's exhibition experience. Thus, we present a technical concept to set up such MDEs integrating the well-established TUIO framework for multi-touch interaction on and between devices.

IT-security experts engage in behavior-based malware analysis in order to learn about previously unknown samples of malicious software (malware) or malware families. For this, they need to find and categorize suspicious patterns from large collections of execution traces. Currently available systems do not meet the analysts' needs which are described as: visual access suitable for complex data structures, visual representations appropriate for IT-security experts, provision of workflow-specific interaction techniques, and the ability to externalize knowledge in the form of rules to ease the analysis process and to share with colleagues. To close this gap, we designed and developed KAMAS, a knowledge-assisted visualization system for behavior-based malware analysis. This paper is a design study that describes the design, implementation, and evaluation of the prototype. We report on the validation of KAMAS with expert reviews, a user study with domain experts and focus group meetings with analysts from industry. Additionally, we reflect on the acquired insights of the design study and discuss the advantages and disadvantages of the applied visualization methods. An interesting finding is that the arc-diagram was one of the preferred visualization techniques during the design phase but did not provide the expected benefits for finding patterns. In contrast, the seemingly simple looking connection line was described as supportive in finding the link between the rule overview table and the rule detail table which are playing a central role for the analysis in KAMAS.

Today many different devices and operating systems can be used for InfoVis systems. On the one hand, web-based visualizations can be used to be compatible with several systems, but the performance depends on optimized browser engines. On the other hand, it is possible to build a native system which supports all the benefits for just one device. However, transferring the code to another system means parts of the code or the programming language have to be adapted. To close this gap, we present a proof of concept based on the Unity3D game engine. We implemented a prototype following the InfoVis reference model and basic interactions for interactive data exploration. A major advantage is that we have now the ability to deploy native code to over 20 different devices. Additionally, this proof of concept opens new possibilities for a future InfoVis framework which benefits from Unity3D.

While quantified-self applications and wearable sensors for running, cycling or strength training are receiving broad interest from science and industry, little attention has been paid to the increasingly popular climbing sport, so far. To fill this gap, specialized wrist-worn sensor devices for tracking climbers have been developed recently. To support climbers and make the best of the available sensor data use possible, we designed a set of interactive visual interfaces which provide detailed insights into training data and support self-assessments of various aspects of the climbing technique. Our approach consists of a mobile web application to be used during the training and a desktop tool for presentation and analysis. In our design study we conducted semi-structured interviews with climbers, developed a scenario-based prototype in D3.js and evaluated our prototype. The initial interviews, a formative expert review and a summative usability study indicate the importance of providing manual input possibilities in addition to the automatically detected data and visualization techniques showing an overview of their training data. The findings of this design study provide an understanding of how climbers will interact with quantified-self applications and what the individual requirements for such a system are.

Abstract Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non‐trivial problem due to the large number of possible relations between them. We provide a systematic overview of state‐of‐the‐art techniques for visualizing different kinds of set relations. We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address these challenges. Further resources on set visualization are available at http://www.setviz.net .

Multi-attribute time-series data plays a vital role in many different domains, such as economics, sensor networks, and biology. An important task when making sense of such data is to provide users with an overview to identify items that show an interesting development over time, including both absolute and relative changes in multiple attributes simultaneously. However, this is not well supported by existing visualization techniques. To address this issue, we present ThermalPlot, a visualization technique that summarizes combinations of multiple attributes over time using an items position, the most salient visual variable. More precisely, the x-position in the ThermalPlot is based on a user-defined degree-of-interest (DoI) function that combines multiple attributes over time. The y-position is determined by the relative change in the DoI value ( ∆DoI) within a user-specified time window. Animating this mapping via a moving time window gives rise to circular movements of items over time-as in thermal systems. To help the user to identify important items that match user-defined temporal patterns and to increase the technique's scalability, we adapt the level of detail of the items' representation based on the DoI value. Furthermore, we present an interactive exploration environment for multi-attribute time-series data that ties together a carefully chosen set of visualizations, designed to support analysts in interacting with the ThermalPlot technique. We demonstrate the effectiveness of our technique by means of two usage scenarios that address the visual analysis of economic development data and of stock market data.

In several application fields, the joint visualization of quantitative data and qualitative abstractions can help analysts make sense of complex time series data by associating precise numeric values with corresponding domain-specific interpretations, such as good, bad, high, low, normal. At the same time, the need to analyse large multivariate time-oriented datasets often calls for keeping visualizations as compact as possible. In this paper, we introduce Qualizon Graphs, a compact visualization that combines quantitative data and qualitative abstractions. It is based on the well known Horizon Graphs, but instead of a predefined number of equally sized bands, it uses as many bands as qualitative categories with corresponding different sizes. In this way, Qualizon Graphs increase the data density of visualized quantitative values and inherently integrate qualitative abstractions. A user study shows that Qualizon Graphs are as fast and accurate as Horizon Graphs for quantitative data, and are an alternative to state-of-the-art visualizations for both quantitative and qualitative data, enabling a trade-off between speed and accuracy.

Behavior-based analysis of emerging malware families involves finding suspicious patterns in large collections of execution traces. This activity cannot be automated for previously unknown malware families and thus malware analysts would benefit greatly from integrating visual analytics methods in their process. However existing approaches are limited to fairly static representations of data and there is no systematic characterization and abstraction of this problem domain. Therefore we performed a systematic literature study, conducted a focus group as well as semi-structured interviews with 10 malware analysts to elicit a problem abstraction along the lines of data, users, and tasks. The requirements emerging from this work can serve as basis for future design proposals to visual analytics-supported malware pattern analysis.

Business Intelligence (BI) provides methods for analyzing business-critical information and supports decision-making processes. Interactivity or the possibility to engage in an active discourse with the representation lies at the core of Information Visualization and Visual BI. However, current work practice and users' perspectives on visualization and interactivity in BI have not been investigated yet. As a first step in this direction, a qualitative empirical study was conducted among six IT-managers working in the field of BI. Main results are that interactive visual methods in BI are not used very often in practice. One of the main reasons reported is the fact that visualization is still two steps ahead and at the moment mostly more basic problems concerning data gathering, data modeling and data quality prevail. Moreover, most users are used to work with numbers and tables and are not aware of the possibilities in terms of visualization. However, the interviewed IT-managers acknowledged that using more interactive visual methods in BI would be beneficial for users.

Abstract It is generally acknowledged in visualization research that it is necessary to evaluate visualization artifacts in order to provide empirical evidence on their effectiveness and efficiency as well as their usability and utility. However, the difficulties of conducting such evaluations still remain an issue. Apart from the required know‐how to appropriately design and conduct user studies, the necessary implementation effort for evaluation features in visualization software is a considerable obstacle. To mitigate this, we present EvalBench, an easy‐to‐use, flexible, and reusable software library for visualization evaluation written in Java. We describe its design choices and basic abstractions of our conceptual architecture and demonstrate its applicability by a number of case studies. EvalBench reduces implementation effort for evaluation features and makes conducting user studies easier. It can be used and integrated with third‐party visualization prototypes that need to be evaluated via loose coupling. EvalBench supports both, quantitative and qualitative evaluation methods such as controlled experiments, interaction logging, laboratory questionnaires, heuristic evaluations, and insight diaries.

In many applications, data tables contain multi-valued attributes that often store the memberships of the table entities to multiple sets such as which languages a person masters, which skills an applicant documents, or which features a product comes with. With a growing number of entities, the resulting element-set membership matrix becomes very rich of information about how these sets overlap. Many analysis tasks targeted at set-typed data are concerned with these overlaps as salient features of such data. This paper presents Radial Sets, a novel visual technique to analyze set memberships for a large number of elements. Our technique uses frequency-based representations to enable quickly finding and analyzing different kinds of overlaps between the sets, and relating these overlaps to other attributes of the table entities. Furthermore, it enables various interactions to select elements of interest, find out if they are over-represented in specific sets or overlaps, and if they exhibit a different distribution for a specific attribute compared to the rest of the elements. These interactions allow formulating highly-expressive visual queries on the elements in terms of their set memberships and attribute values. As we demonstrate via two usage scenarios, Radial Sets enable revealing and analyzing a multitude of overlapping patterns between large sets, beyond the limits of state-of-the-art techniques.

Model selection in time series analysis is a challenging task for domain experts in many application areas such as epidemiology, economy, or environmental sciences. The methodology used for this task demands a close combination of human judgement and automated computation. However, statistical software tools do not adequately support this combination through interactive visual interfaces. We propose a Visual Analytics process to guide domain experts in this task. For this purpose, we developed the TiMoVA prototype that implements this process based on user stories and iterative expert feedback on user experience. The prototype was evaluated by usage scenarios with an example dataset from epidemiology and interviews with two external domain experts in statistics. The insights from the experts' feedback and the usage scenarios show that TiMoVA is able to support domain experts in model selection tasks through interactive visual interfaces with short feedback cycles.

Time-oriented data play an essential role in many Visual Analytics scenarios such as extracting medical insights from collections of electronic health records or identifying emerging problems and vulnerabilities in network traffic. However, many software libraries for Visual Analytics treat time as a flat numerical data type and insufficiently tackle the complexity of the time domain such as calendar granularities and intervals. Therefore, developers of advanced Visual Analytics designs need to implement temporal foundations in their application code over and over again. We present TimeBench, a software library that provides foundational data structures and algorithms for time-oriented data in Visual Analytics. Its expressiveness and developer accessibility have been evaluated through application examples demonstrating a variety of challenges with time-oriented data and long-term developer studies conducted in the scope of research and student projects.

The visualization and analysis of dynamic social networks are challenging problems, demanding the simultaneous consideration of relational and temporal aspects. In order to follow the evolution of a network over time, we need to detect not only which nodes and which links change and when these changes occur, but also the impact they have on their neighbourhood and on the overall relational structure. Aiming to enhance the perception of structural changes at both the micro and the macro level, we introduce the change centrality metric. This novel metric, as well as a set of further metrics we derive from it, enable the pair wise comparison of subsequent states of an evolving network in a discrete-time domain. Demonstrating their exploitation to enrich visualizations, we show how these change metrics support the visual analysis of network dynamics.

A well-designed visualization of dynamic networks has to support the analysis of both temporal and relational features at once. In particular to solve complex synoptic tasks, the users need to understand the topological structure of the network, its evolution over time, and possible interdependencies. In this paper, we introduce the application of the vertigo zoom interaction technique, derived from filmmaking, to information visualizations. When applied to a two-and-a-half-dimensional view, this interaction technique enables smooth transitions between the relational perspective (node-link diagrams and scatter plots) and the time perspective (trajectories and line charts), supporting a seamless visual analysis and preserving the user's mental map.

Abstract In many application areas, analysts have to make sense of large volumes of multivariate time‐series data. Explorative analysis of this kind of data is often difficult and overwhelming at the level of raw data. Temporal data abstraction reduces data complexity by deriving qualitative statements that reflect domain‐specific key characteristics. Visual representations of abstractions and raw data together with appropriate interaction methods can support analysts in making their data easier to understand. Such a visualization technique that applies smooth semantic zooming has been developed in the context of patient data analysis. However, no empirical evidence on its effectiveness and efficiency is available. In this paper, we aim to fill this gap by reporting on a controlled experiment that compares this technique with another visualization method used in the well‐known KNAVE‐II framework. Both methods integrate quantitative data with qualitative abstractions whereas the first one uses a composite representation with color‐coding to display the qualitative data and spatial position coding for the quantitative data. The second technique uses juxtaposed representations for quantitative and qualitative data with spatial position coding for both. Results show that the test persons using the composite representation were generally faster, particularly for more complex tasks that involve quantitative values as well as qualitative abstractions.

Contingency tables summarize the relations between categorical variables and arise in both scientific and business domains. Asymmetrically large two-way contingency tables pose a problem for common visualization methods. The Contingency Wheel has been recently proposed as an interactive visual method to explore and analyze such tables. However, the scalability and readability of this method are limited when dealing with large and dense tables. In this paper we present Contingency Wheel++, new visual analytics methods that overcome these major shortcomings: (1) regarding automated methods, a measure of association based on Pearson's residuals alleviates the bias of the raw residuals originally used, (2) regarding visualization methods, a frequency-based abstraction of the visual elements eliminates overlapping and makes analyzing both positive and negative associations possible, and (3) regarding the interactive exploration environment, a multi-level overview+detail interface enables exploring individual data items that are aggregated in the visualization or in the table using coordinated views. We illustrate the applicability of these new methods with a use case and show how they enable discovering and analyzing nontrivial patterns and associations in large categorical data.

In recent years, sophisticated visualization methods have been developed to support both, the logical structure and the time-oriented aspects of computer-executable clinical treatment plans. However, visualizing the effects of applying treatment plans as well as supporting the exploration of effects on the patient's condition are still largely unresolved tasks. To fill this gap, we have developed a prototype that enhances known visualization methods to communicate the processes of treatment plan application together with their effects on a patient's condition in an easily understandable way. Our prototype combines the advantages of enhanced visual recognition of patterns with traditional information of parameters' development. Thus, it provides means (1) to assess success or failure of previously applied treatment plans, (2) to explore the effects of each applied clinical action on the patient's condition, and (3) to identify sub-optimal treatment choices. These means help physicians to optimize their treatment choices and enable developers of clinical practice guidelines (CPGs) to investigate and readjust these treatment plans.

Abstract Line plots are very well suited for visually representing time‐series. However, several difficulties arise when multivariate heterogeneous time‐series data is displayed and compared visually. Especially, if the developments and trends of time‐series of different units or value ranges need to be compared, a straightforward overlay could be visually misleading. To mitigate this, visualization pioneer Jacques Bertin presented a method called indexing that transforms data into comparable units for visual representation. In this paper, we want to provide empirical evidence for this method and present a comparative study of the three visual comparison methods linear scale with juxtaposition, log scale with superimposition and indexing. Although for task completion times, indexing only shows slight advantages, the results support the assumption that the indexing method enables the user to perform comparison tasks with a significantly lower error rate. Furthermore, a post‐test questionnaire showed that the majority of the participants favour the indexing method over the two other comparison methods.

We show how to divide the edge graph of a Voronoi diagram into a tree that corresponds to the medial axis of an (augmented) planar domain. Division into base cases is then possible, which, in the bottom-up phase, can be merged by trivial concatenation. The resulting construction algorithm--similar to Delaunay triangulation methods--is not bisector-based and merely computes dual links between the sites, its atomic steps being inclusion tests for sites in circles. This guarantees computational simplicity and numerical stability. Moreover, no part of the Voronoi diagram, once constructed, has to be discarded again. The algorithm works for polygonal and curved objects as sites and, in particular, for circular arcs which allows its extension to general free-form objects by Voronoi diagram preserving and data saving biarc approximations. The algorithm is randomized, with expected runtime O(n log n) under certain assumptions on the input data. Experiments substantiate an efficient behavior even when these assumptions are not met. Applications to offset computations and motion planning for general objects are described.

Many real-world problems involve time-oriented data. Time data is different from other kinds of data--explicitly harnessing the structures of time in visualizations can guide and support users’ visual analysis processes. State-of-the-art visualizations hardly take advantage of the structures of time to aid users in understanding and exploring the data. To bring more flexibility to the analysis process, we have developed interactive visual methods incorporating the structures of time within a pixel-based visualization called GROOVE (granular overview overlay). GROOVE uses different techniques to visualize time-oriented data by overlaying several time granularities in one visualization and provides interactive operators, which utilize the structures of time in different ways to capture and explore time-oriented data.

Recently, web browser based applications have become very popular in many domains. However, the specific requirements of interactive Information Visualization (InfoVis) applications in terms of graphics performance and interactivity have not yet been investigated systematically in this context. In order to assess browser-based application platforms, we provide a systematic comparison of server-based rendering, Java applets, Flash, and Silverlight from several points of view. We aim to aid InfoVis developers in choosing the appropriate technology for their needs.

Empirical comparisons and categorizations of information visualization tools lack important considerations: the former undervalue the need for a theoretical background, and the latter tend to have too much distance from the user because they do not consider definite user tasks. Therefore, our work combines these approaches and presents the results of both a qualitative evaluation and a recently published categorization. We focus on the visualization of temporal data and reveal that current tools realize only a small part of the visualization possibilities in this field.

Providing appropriate methods to facilitate the analysis of time-oriented data is a key issue in many application domains. In this paper, we focus on the unique role of the parameter time in the context of visually driven data analysis. We will discuss three major aspects - visualization, analysis, and the user. It will be illustrated that it is necessary to consider the characteristics of time when generating visual representations. For that purpose we take a look at different types of time and present visual examples. Integrating visual and analytical methods has become an increasingly important issue. Therefore, we present our experiences in temporal data abstraction, principal component analysis, and clustering of larger volumes of time-oriented data. The third main aspect we discuss is supporting user-centered visual analysis. We describe event-based visualization as a promising means to adapt the visualization pipeline to needs and tasks of users.

Dealing with temporal uncertainties is a key issue in domains like project management or medical treatment planning. However, support for temporal indeterminacies is not very well integrated in current methods, techniques, and tools. In this paper, we present a visualization technique called PlanningLines that allows for representing temporal uncertainties and aims at supporting project managers in their difficult planning and controlling tasks. We conducted a controlled experiment to gather empirical evidence on the strengths and limitations of our approach. Main results are that PlanningLine users make fewer mistakes and are faster in conducting tasks than users of a traditional visualization technique.