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The word ‘pattern’ frequently appears in the visualisation and visual analytics literature, but what do we mean when we talk about patterns? We propose a practicable definition of the concept of a pattern in a data distribution as a combination of multiple interrelated elements of two or more data components that can be represented and treated as a unified whole. Our theoretical model describes how patterns are made by relationships existing between data elements. Knowing the types of these relationships, it is possible to predict what kinds of patterns may exist. We demonstrate how our model underpins and refines the established fundamental principles of visualisation. The model also suggests a range of interactive analytical operations that can support visual analytics workflows where patterns, once discovered, are explicitly involved in further data analysis.

In the application domain of digital humanities network visualization is increasingly being used to conduct research as the main interests of the domain experts lie in exploring and analyzing relationships between entities and their changes over time. Visualizing the dynamics and different perspectives of such data is a non-trivial task but it enables researchers to explore connections between disparate entities and investigate historical narratives that emerge. In this paper we present Circular, an interactive exploration environment to visualize event-based networks and support research in digital humanities through visualization of historical subjects in space and time. Our radial design is the result of iterative collaboration with domain experts, and we discuss the process of collaborative development and exploration of public music festivities in Vienna as an example of immersive development methodology. We validate our approach by means of both domain and visualization expert interviews and show the potential of this approach in supporting the visual exploration of historical subjects. We discuss our design rationales, visual encodings, and interactions as to allow the reproducibility of this approach within a framework of transdisciplinary collaboration with digital humanities.

The economy of a country can be modeled as a complex system in which several players buy and sell goods from each other. By analyzing the investment flows, it is possible to reconstruct the supply chain for the production of most goods, whose understanding is important to analysts and public officials interested in creating and evaluating strategies for informed and strategic decision making, for instance, adjusting tax policies. Those networks of players and investments, however, tend to be complex and very dense, which leads to over-plotted visualizations that obfuscate precious information such as the dependencies between productive sectors and regions. In this paper, we propose Hermes, a guidance-enriched Visual Analytics environment (named after the Greek God of Commerce) for the exploration of complex economic networks, to uncover supply chains, regions’ productivity, and sector-to-sector relationships. With practical knowledge regarding guidance, we designed and implemented a visual sub-graph querying approach to extract patterns from such complex investment graphs obtained from real-world data. We present a three-fold evaluation of the system: we perform a qualitative evaluation of our approach with three domain experts, a separate assessment of the proposed guidance features with an expert researcher in this field, and a case study of Hermes using a bank account network dataset to demonstrate the generalizability of our approach.

Conventional noise control solutions used in the transportation industry have proven to be effective in minimizing structure-borne sound at mid to high frequencies; however, a lightweight means to ...

Trust‐ability, reputation, security and quality are the main concerns for public and private financial institutions. To detect fraudulent behaviour, several techniques are applied pursuing different goals. For well‐defined problems, analytical methods are applicable to examine the history of customer transactions. However, fraudulent behaviour is constantly changing, which results in ill‐defined problems. Furthermore, analysing the behaviour of individual customers is not sufficient to detect more complex structures such as networks of fraudulent actors. We propose NEVA (Network dEtection with Visual Analytics), a Visual Analytics exploration environment to support the analysis of customer networks in order to reduce false‐negative and false‐positive alarms of frauds. Multiple coordinated views allow for exploring complex relations and dependencies of the data. A guidance‐enriched component for network pattern generation, detection and filtering support exploring and analysing the relationships of nodes on different levels of complexity. In six expert interviews, we illustrate the applicability and usability of NEVA.

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.

Cultural object collections attract and delight spectators since ancient times. Yet, they also easily overwhelm visitors due to their perceptual richness and associated information. Similarly, digitized collections appear as complex, multifaceted phenomena, which can be challenging to grasp and navigate. Though visualizations can create various types of collection overviews for that matter, they do not easily assemble into a "big picture" or lead to an integrated understanding. We introduce coherence techniques to maximize connections between multiple views and apply them to the prototype PolyCube system of collection visualization: with map, set, and network visualizations it makes spatial, categorical, and relational collection aspects visible. For the essential temporal dimension, it offers four different views: superimposition, animation, juxtaposition, and space-time cube representations. A user study confirmed that better integrated visualizations support synoptic, cross-dimensional insights. An outlook is dedicated to the system's applicability within other arts and humanities data domains.

The phenomenon of a blockchain use case called initial coin offering (ICO) is drawing increasing attention as a novel funding mechanism. ICO is a crowdfunding type that utilizes blockchain tokens to allow for truly peer-to-peer investments. Although more than $7bn has been raised globally via ICOs as at 2018, the concept and its implications are not yet entirely understood. The research lags behind in providing in-depth analyses of ICO designs and their long-term success. We address this research gap by developing an ICO taxonomy, applying a cluster analysis to identify prevailing ICO archetypes, and providing an outlook on the token value market performance for individual archetypes. We identify five ICO design archetypes and display their secondary market development from both a short-term and a long-term perspective. We contribute to an in-depth understanding of ICOs and their implications. Further, we offer practitioners tangible design and success indications for future ICOs.

Die Informatik ist ein Fachgebiet, die für Kinder und Jugendliche heute und ganz besonders in deren zukünftigem Berufsleben eine wesentliche Rolle spielen wird. Entsprechend wichtig ist es für sie, dass der Unterricht für sie ansprechend, fordernd aber gleichzeitig motivierend gestaltet wird. In dieser Arbeit wird deshalb vorgestellt, welche Unterrichtsmethoden sich besonders für den Informatikunterricht eignen und wie diese Unterrichtsmethoden lernpsychologisch begründet sind. Die vorgestellten Unterrichtsmethoden werden anschließend verwendet um eine Unterrichtsplanung für das Themengebiet der Netzwerktechnik lehrplangetreu aufzubereiten, wobei der Lehrplan der österreichischen AHS im Vordergrund steht, jene der BHS mitbetrachtet werden. Darüber hinaus werden auch die Kompetenzmodelle der AHS und BHS in Bezug auf die Netzwerktechnik miteinbezogen. Netzwerktechnische Grundlagen, Analyse- und Problemlösungskompetenzen sind für die Schülerinnen und Schüler von Bedeutung, weil sie damit befähigt werden, selbstständig auftretende Probleme mit dem Internetzugang und Internetdiensten zu lösen. Das ist für diese Alters-gruppe immer wichtiger, weil ein wesentlicher Teil deren sozialen Lebens online stattfindet und sie besonders ihre Freizeit immer mehr online verbringen. Lehrerinnen und Lehrer, die in der Vergangenheit das Thema der Netzwerktechnik gemieden haben, finden in der vorliegen-den Arbeit ein Rahmenwerk für die Durchführung des Unterrichts mit Beispielen und Wiederholungsfragen als auch mit Vorschlägen zur Beurteilung. Computer science is a subject that will play an essential role for children and young people today and especially in their future professional lives. Accordingly, it is important for them that the lessons are designed to be appealing, challenging, but motivating at the same time. Therefore, this thesis presents which teaching methods are particularly suitable for computer science lessons and how these teaching methods are based on the psychology of learning. The presented teaching methods are then used to prepare a lesson planning for the subject area of network technology in accordance with the curriculum, whereby the curriculum of the Austrian general secondary schools (AHS) is in the foreground, those of the vocational secondary schools (BHS) are considered as well. In addition, the competence models of the AHS and BHS are also included in relation to network technology. Network technology basics, analysis and problem-solving skills are important for students because they enable them to independently solve problems that arise with Internet access and Internet services. This is increasingly important for this age group because a significant part of their social life takes place online and they especially spend more and more of their free time online. Teachers who have avoided the topic of network technology in the past will find in this paper a framework for conducting lessons with examples and review questions as well as suggestions for assessment. eingereicht von Harald Miksch, MA MSc Diplomarbeit Universität Linz 2022

Visual Analytics integrates the outstanding capabilities of humans in terms of visual information exploration with the enormous processing power of computers to form a powerful knowledge discovery environment. In other words, Visual Analytics is the science of analytical reasoning facilitated by interactive interfaces and captures the information discovery process keeping the human in the loop. Process Mining aims to extract information and knowledge from event logs to discover, monitor, and improve processes in a variety of application domains. Such event data or traces of activities often possess data quality issues as well as exhibit unexpected behavior and complex relations. Consequently, before and during the implementation of interactive (automated or semi-automated) analysis methods, such as Process Mining algorithms, the analyst needs to explore, investigate, and understand the data at hand in order to decide which analysis methods might be appropriate. The combination of interactive visual data analyses and exploration with Process Mining algorithms makes complex information structures more comprehensible and facilitates new insights. In this talk, I will illustrate the concepts of Visual Analytics, how Visual Analytics combined with Process Mining techniques supports to extract more insights from complex event data, and elaborate about the challenges and opportunities for analyzing process data with Visual Analytics methods. Various examples will illustrate what has been achieved so far and show possible future directions and challenges.

Providing guidance during a Visual Analytics session can support analysts in pursuing their goals more efficiently. However, the effectiveness of guidance depends on many factors: Determining the right timing to provide it is one of them. Although in complex analysis scenarios choosing the right timing could make the difference between a dependable and a superfluous guidance, an analysis of the literature suggests that this problem did not receive enough attention. In this paper, we describe a methodology to determine moments in which guidance is needed. Our assumption is that the need of guidance would influence the user state-of-mind, as in distress situations during the analytical process, and we hypothesize that such moments could be identified by analyzing the user's facial expressions. We propose a framework composed by a facial recognition software and a machine learning model trained to detect when to provide guidance according to changes of the user facial expressions. We trained the model by interviewing several analysts during their work and ranked multiple facial features based on their relative importance in determining the need of guidance. Finally, we show that by applying only minor modifications to its architecture, our prototype was able to detect a need of guidance on the fly and made our methodology well suited also for real-time analysis sessions. The results of our evaluations show that our methodology is indeed effective in determining when a need of guidance is present, which constitutes a prerequisite to providing timely and effective guidance in VA.

Since it can be challenging for users to effectively utilize interactive visualizations, guidance is usually provided to assist users in solving tasks. Guidance is mentioned as an effective mean to overcome stall situations occurring during the analysis. However, the effectiveness of a peculiar guidance solution usually varies for different analysis scenarios. The same guidance may have different effects on users with (1) different levels of expertise. The choice of the appropriate (2) degree of guidance and the type of (3) task under consideration also affect the positive or negative outcome of providing guidance. Considering these three factors, we conducted a user study to investigate the effectiveness of variable degrees of guidance with respect to the user’s previous knowledge in different analysis scenarios. Our results shed light on the appropriateness of certain degrees of guidance in relation to different tasks, and the overall influence of guidance on the analysis outcome in terms of user’s mental state and analysis performance. Keywords: Guidance, User study, Knowledge, Trust, Mixed-initiative, Visual data analysis

After decades of digitization, large cultural heritage collections have emerged on the web, which contain massive stocks of content from galleries, libraries, archives, and museums. This increase in digital cultural heritage data promises new modes of analysis and increased levels of access for academic scholars and casual users alike. Going beyond the standard representations of search-centric and grid-based interfaces, a multitude of approaches has recently started to enable visual access to cultural collections, and to explore them as complex and comprehensive information spaces by the means of interactive visualizations. In contrast to conventional web interfaces, we witness a widening spectrum of innovative visualization types specially designed for rich collections from the cultural heritage sector. This new class of information visualizations gives rise to a notable diversity of interaction and representation techniques while lending currency and urgency to a discussion about principles such as serendipity, generosity, and criticality in connection with visualization design. With this survey, we review information visualization approaches to digital cultural heritage collections and reflect on the state of the art in techniques and design choices. We contextualize our survey with humanist perspectives on the field and point out opportunities for future research.

Rapid ethnography and data mining approaches have been used individually to study clinical workflows, but have seldom been used together to overcome the limitations inherent in either type of method. For rapid ethnography, how reliable are the findings drawn from small samples? For data mining, how accurate are the discoveries drawn from automatic analysis of big data, when compared with observable data? This paper explores the combined use of rapid ethnography and process mining, aka ethno-mining, to study and compare metrics of a typical clinical documentation task, vital signs charting. The task was performed with different electronic health records (EHRs) used in three different hospital sites. The individual methods revealed substantial discrepancies in task duration between sites. Specifically, means of 159.6(78.55), 38.2(34.9), and 431.3(283.04) seconds were captured with rapid ethnography. When process mining was used, means of 518.6(3,808), 345.5(660.6), and 119.74(210.3) seconds were found. When ethno-mining was applied instead, outliers could be identified, explained and removed. Without outliers, mean task duration was similar between sites (78.1(66.7), 72.5(78.5), and 71.7(75) seconds). Results from this work suggest that integrating rapid ethnography and data mining into a single process may provide more meaningful results than a siloed approach when studying of workflow.

A temporal graph stores and reflects temporal information associated with its entities and relationships. Such graphs can be utilized to model a broad variety of problems in a multitude of domains. Re-searchers from different fields of expertise are increasingly applying graph visualization and analysis to explore unknown phenomena, complex emerging structures, and changes occurring over time in their data. While several empirical studies evaluate the benefits and drawbacks of different network representations, visualizing the temporal dimension in graphs still presents an open challenge. In this paper we propose an exploratory user study with the aim of evaluating different combinations of graph representations, namely node-link and adjacency matrix, and temporal encodings, such as superimposition, juxtaposition and animation, on typical temporal tasks. The study participants expressed positive feedback toward matrix representations, with generally quicker and more accurate responses than with the node-link representation.

Caused by a newly discovered coronavirus, COVID-19 is an infectious disease easily transmitted between people through close contacts that had exponential global growth in 2020 and became, in a very short time, a major health, and economic global issue. Real-world data concerning the spread of the disease was quickly made available by different global institutions and resulted in many works involving data visualizations and prediction models. In this paper, (1) we discuss the problem, data aspects, and challenges of COVID-19 data analysis; (2) We propose a Visual Analytics approach (called COVis) combining different temporal aspects of COVID-19 data with the output of a predictive model. This combination supports the estimation of the spread of the disease in different scenarios and allows correlating and monitoring the virus development in relation to different government response events; (3) We evaluate the approach with two domain experts to support the understanding of how our system can facilitate journalistic investigation tasks and (4) we discuss future works and a possible generalization of our solution.

Clinician task performance is significantly impacted by the navigational efficiency of the system interface. Here we propose and evaluate a navigational complexity framework useful for examining differences in electronic health record (EHR) interface systems and their impact on task performance. The methodological approach includes 1) expert-based methods—specifically, representational analysis (focused on interface elements), keystroke level modeling (KLM), and cognitive walkthrough; and 2) quantitative analysis of interactive behaviors based on video-captured observations. Medication administration record (MAR) tasks completed by nurses during preoperative (PreOp) patient assessment were studied across three Mayo Clinic regional campuses and three different EHR systems. By analyzing the steps executed within the interfaces involved to complete the MAR tasks, we characterized complexities in EHR navigation. These complexities were reflected in time spent on task, click counts, and screen transitions, and were found to potentially influence nurses’ performance. Two of the EHR systems, employing a single screen format, required less time to complete (mean 101.5, range 106–97 s), respectively, compared to one system employing multiple screens (176 s, 73% increase). These complexities surfaced through trade-offs in cognitive processes that could potentially influence nurses’ performance. Factors such as perceptual-motor activity, visual search, and memory load impacted navigational complexity. An implication of this work is that small tractable changes in interface design can substantially improve EHR navigation, overall usability, and workflow.

Preoperative care is a critical, yet complex, time-sensitive process. Optimization of workflow is challenging for many reasons, including a lack of standard workflow analysis methods. We sought to comprehensively characterize electronic health record-mediated preoperative nursing workflow. We employed a structured methodological framework to investigate and explain variations in the workflow. Video recording software captured 10 preoperative cases at Arizona and Florida regional referral centers. We compared the distribution of work for electronic health record tasks and off-screen tasks through quantitative analysis. Suboptimal patterns and reasons for variation were explored through qualitative analysis. Although both settings used the same electronic health record system, electronic health record tasks and off-screen tasks time distribution and patterns were notably different across two sites. Arizona nurses spent a longer time completing preoperative assessment. Electronic health record tasks occupied a higher proportion of time in Arizona, while off-screen tasks occupied a higher proportion in Florida. The contextual analysis helped to identify the variation associated with the documentation workload, preparation of the patient, and regional differences. These findings should seed hypotheses for future optimization efforts and research supporting standardization and harmonization of workflow across settings, post-electronic health record conversion.

In social networks, individuals' decisions are strongly influenced by recommendations from their friends and acquaintances. The influence maximization (IM) problem asks to select a seed set of users that maximizes the influence spread, i.e., the expected number of users influenced through a stochastic diffusion process triggered by the seeds. In this paper, we present VAIM, a visual analytics system that supports users in analyzing the information diffusion process determined by different IM algorithms. By using VAIM one can: (i) simulate the information spread for a given seed set on a large network, (ii) analyze and compare the effectiveness of different seed sets, and (iii) modify the seed sets to improve the corresponding influence spread., Appears in the Proceedings of the 28th International Symposium on Graph Drawing and Network Visualization (GD 2020)

Visualization envisions to intertwine the strengths of humans and computers for effective interactive visual and analytic data analysis and exploration. To this end, humans’ tacit/implicit knowledge from prior experience is an important asset that can be leveraged by both human and computer to improve the visual and analytic exploration processes. However, acquiring, structuring, formalizing, storing, and utilizing implicit and explicit knowledge within the whole visualization process are provocative and widely-discussed research challenge. This chapter elaborates on (1) knowledge-assisted visualization, which aims to incorporate implicit and explicit knowledge as well as information-theoretical considerations into the visualization process to support users for decision making and (2) guidance, which is a computer-assisted process that aims to actively resolve a knowledge gap encountered by users during an interactive visualization session. This chapter ends with critical reflections about applicability, usability, and utility of the proposed knowledge enhanced visualization processes.

The detection of anomalous events in huge amounts of data is sought in many domains. For instance, in the context of financial data, the detection of suspicious events is a prerequisite to identify and prevent attempts to defraud. Hence, various financial fraud detection approaches have started to exploit Visual Analytics techniques. However, there is no study available giving a systematic outline of the different approaches in this field to understand common strategies but also differences. Thus, we present a survey of existing approaches of visual fraud detection in order to classify different tasks and solutions, to identify and to propose further research opportunities. In this work, fraud detection solutions are explored through five main domains: banks, the stock market, telecommunication companies, insurance companies, and internal frauds. The selected domains explored in this survey were chosen for sharing similar time-oriented and multivariate data characteristics. In this survey, we (1) analyze the current state of the art in this field; (2) define a categorization scheme covering different application domains, visualization methods, interaction techniques, and analytical methods which are used in the context of fraud detection; (3) describe and discuss each approach according to the proposed scheme; and (4) identify challenges and future research topics. Keywords: Visual knowledge discovery, Time series data, Business and finance visualization, Financial fraud detection

Finding the shortest path through open spaces is a well-known challenge for pedestrian routing engines. A common solution is routing on the open space boundary, which causes in most cases an unnecessarily long route. A possible alternative is to create a subgraph within the open space. This paper assesses this approach and investigates its implications for routing engines. A number of algorithms (Grid, Spider-Grid, Visibility, Delaunay, Voronoi, Skeleton) have been evaluated by four different criteria: (i) Number of additional created graph edges, (ii) additional graph creation time, (iii) route computation time, (iv) routing quality. We show that each algorithm has advantages and disadvantages depending on the use case. We identify the algorithms Visibility with a reduced number of edges in the subgraph and Spider-Grid with a large grid size to be a good compromise in many scenarios.

During data preprocessing, analysts spend a significant part of their time and effort profiling the quality of the data along with cleansing and transforming the data for further analysis. While quality metrics—ranging from general to domain-specific measures—support assessment of the quality of a dataset, there are hardly any approaches to visually support the analyst in customizing and applying such metrics. Yet, visual approaches could facilitate users’ involvement in data quality assessment. We present MetricDoc , an interactive environment for assessing data quality that provides customizable, reusable quality metrics in combination with immediate visual feedback. Moreover, we provide an overview visualization of these quality metrics along with error visualizations that facilitate interactive navigation of the data to determine the causes of quality issues present in the data. In this article, we describe the architecture, design, and evaluation of MetricDoc , which underwent several design cycles, including heuristic evaluation and expert reviews as well as a focus group with data quality, human-computer interaction, and visual analytics experts.

To complement the currently existing definitions and conceptual frameworks of visual analytics, which focus mainly on activities performed by analysts and types of techniques they use, we attempt to define the expected results of these activities. We argue that the main goal of doing visual analytics is to build a mental and/or formal model of a certain piece of reality reflected in data. The purpose of the model may be to understand, to forecast or to control this piece of reality. Based on this model-building perspective, we propose a detailed conceptual framework in which the visual analytics process is considered as a goal-oriented workflow producing a model as a result. We demonstrate how this framework can be used for performing an analytical survey of the visual analytics research field and identifying the directions and areas where further research is needed.

Financial institutions are interested in ensuring security and quality for their customers. Banks, for instance, need to identify and stop harmful transactions in a timely manner. In order to detect fraudulent operations, data mining techniques and customer profile analysis are commonly used. However, these approaches are not supported by Visual Analytics techniques yet. Visual Analytics techniques have potential to considerably enhance the knowledge discovery process and increase the detection and prediction accuracy of financial fraud detection systems. Thus, we propose EVA, a Visual Analytics approach for supporting fraud investigation, fine-tuning fraud detection algorithms, and thus, reducing false positive alarms.

The cycle plot is an established and effective visualization technique for identifying and comprehending patterns in periodic time series, like trends and seasonal cycles. It also allows to visually identify and contextualize extreme values and outliers from a different perspective. Unfortunately, it is limited to univariate data. For multivariate time series, patterns that exist across several dimensions are much harder or impossible to explore. We propose a modified cycle plot using a distance-based abstraction Mahalanobis distance to reduce multiple dimensions to one overview dimension and retain a representation similar to the original. Utilizing this distance-based cycle plot in an interactive exploration environment, we enhance the Visual Analytics capacity of cycle plots for multivariate outlier detection. To enable interactive exploration and interpretation of outliers, we employ coordinated multiple views that juxtapose a distance-based cycle plot with Cleveland's original cycle plots of the underlying dimensions. With our approach it is possible to judge the outlyingness regarding the seasonal cycle in multivariate periodic time series.

Data quality management and assessment play a vital role for ensuring the trust in the data and its fitness-of-use for subsequent analysis. The transformation history of a data wrangling system is often insufficient for determining the usability of a dataset, lacking information how changes affected the dataset. Capturing workflow provenance along the wrangling process and combining it with descriptive information as data provenance can enable users to comprehend how these changes affected the dataset, and if they benefited data quality. We present DQProv Explorer, a system that captures and visualizes provenance from data wrangling operations. It features three visualization components: allowing the user to explore the provenance graph of operations and the data stream, the development of quality over time for a sequence of wrangling operations applied to the dataset, and the distribution of issues across the entirety of the dataset to determine error patterns.

Investment planning requires knowledge of the financial landscape on a large scale, both in terms of geo-spatial and industry sector distribution. There is plenty of data available, but it is scattered across heterogeneous sources (newspapers, open data, etc.), which makes it difficult for financial analysts to understand the big picture. In this paper, we present Sabrina, a financial data analysis and visualization approach that incorporates a pipeline for the generation of firm-to-firm financial transaction networks. The pipeline is capable of fusing the ground truth on individual firms in a region with (incremental) domain knowledge on general macroscopic aspects of the economy. Sabrina unites these heterogeneous data sources within a uniform visual interface that enables the visual analysis process. In a user study with three domain experts, we illustrate the usefulness of Sabrina, which eases their analysis process.

For the evaluation of infectious-diseases interventions, the transmissible nature of such diseases plays a central role. Agent-based models (ABM) allow for dynamic transmission modeling but publications are limited. We aim to provide an overview of important characteristics of ABM for decision-analytic modeling of infectious diseases. A case study of dengue epidemics illustrates model characteristics, conceptualization, calibration and model analysis. First, major characteristics of ABM are outlined and discussed based on ISPOR and ISPOR-SMDM Good Practice guidelines. Second, in our case study, we modeled a dengue outbreak in Cebu City (Philippines) to assess the impact interventions to control the relative growth of the mosquito population. Model outcomes include prevalence and incidence of infected persons. The modular ABM simulates persons and mosquitoes over an annual time horizon considering daily time steps. The model was calibrated and validated. ABM is a dynamic, individual-level modeling approach that is capable to reproduce direct and indirect effects of interventions for infectious diseases. The ability to replicate emerging behavior and to include human behavior or the behavior of other agents is a distinguishing modeling characteristic (e.g., compared to Markov models). Modeling behavior may, however, require extensive calibration and validation. The analyzed hypothetical effectiveness of dengue interventions showed that a reduced human-mosquito ratio of 1:2.5 during rainy seasons leads already to a substantial decrease of infected persons. ABM can support decision-analyses for infectious diseases including disease dynamics, emerging behavior, and providing a high level of reusability due to modularity.

This report documents the program and the outcomes of Dagstuhl Seminar 19192 "Visual Analytics for Sets over Time and Space", which brought together 29 researchers working on visualization (i) from a theoretical point of view (graph drawing, computational geometry, and cognition), (ii) from a temporal point of view (visual analytics and information visualization over time, HCI), and (iii) from a space-time point of view (cartography, GIScience). The goal of the seminar was to identify specific theoretical and practical problems that need to be solved in order to create dynamic and interactive set visualizations that take into account time and space, and to begin working on these problems. The first 1.5 days were reserved for overview presentations from representatives of the different communities, for presenting open problems, and for forming interdisciplinary working groups that will focus on some of the identified open problems as a group. There were three survey talks, ten short talks, and one panel with three contributors. The remaining three days consisted of open mic sessions, working-group meetings, and progress reports. Five working groups were formed that investigated several of the open research questions. Abstracts of the talks and a report from each working group are included in this report.

User tasks play a pivotal role in visualization design and evaluation. However, the term ‘task’ is used ambiguously within the visualization community. In this article, we critically analyze the relevant literature and systematically compare definitions of ‘task’ and the usage of related terminology. In doing so, we identify a three-dimensional conceptual space of user tasks in visualization, referred to as the task cube, and the more precise concepts ‘objective’ and ‘action’ for tasks. We illustrate the usage of the task cube’s dimensions in an objective-driven visualization process, in different scenarios of visualization design and evaluation, and for comparing categorizations of abstract tasks. Thus, visualization researchers can better formulate their contributions which helps advance visualization as a whole.

A number of studies have investigated different ways of visualizing uncertainty. However, in the temporal dimension, it is still an open question how to best represent uncertainty, since the special characteristics of time require special visual encodings and may provoke different interpretations. Thus, we have conducted a comprehensive study comparing alternative visual encodings of intervals with uncertain start and end times: gradient plots, violin plots, accumulated probability plots, error bars, centered error bars, and ambiguation. Our results reveal significant differences in error rates and completion time for these different visualization types and different tasks. We recommend using ambiguation - using a lighter color value to represent uncertain regions - or error bars for judging durations and temporal bounds, and gradient plots - using fading color or transparency - for judging probability values.

The use of lightweight materials is obligatory in the design of economic structures, but with decreasing mass, the vibro-acoustic properties of the structures become unfavorable. Acoustic metamaterials based on the concept of acoustic black holes (ABH) can improve the vibrational behavior by adding local thickness variations to the host structure. To make computational models of metamaterials also valid for high frequencies, a fine discretization has to be used. This leads to high computation times. A parametric model order reduction (PMOR) approach is presented, which is able to create an efficient reduced model of ABHs. In the first step, one structure preserving reduced model for each parameter is generated using the iterative rational Krylov algorithm (IRKA). The transfer functions of all reduced models are used in the second step to create a single parametric model using the Loewner framework. The resulting model can be used to efficiently evaluate the frequency response of a structure equipped with ABHs for different parameter sets.

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.

Travel sickness, which is a special kind of motion sickness or kinetosis, is unpleasant for many people while traveling. It is often caused by a mismatch between the perceived movement of the eyes and the movement sensed by the vestibular system. In a car, this condition is often caused by reading or staring at a mobile phone for a longer period of time. Looking outside the window at the trajectory of the vehicle helps most of those affected by kinetosis. But what if you want to read a book while traveling? In this paper, we present the Motion Sickness Prevention System (MSPS) --an approach that allows reading while driving easing the symptoms of kinetosis. It simply uses the live video stream of the road ahead as a background for reading. A preliminary study (N=12) shows that the MSPS, while not able to eliminate the symptoms of kinetosis, can still significantly decrease it.

We describe a novel method for automatic detection of errors in human-robot interactions. Our approach is to detect errors based on the classification of head and shoulder movements of humans who are interacting with erroneous robots. We conducted a user study in which participants interacted with a robot that we programmed to make two types of errors: social norm violations and technical failures. During the interaction, we recorded the behavior of the participants with a Kinect v1 RGB-D camera. Overall, we recorded a data corpus of 237,998 frames at 25 frames per second; 83.48% frames showed no error situation; 16.52% showed an error situation. Furthermore, we computed six different feature sets to represent the movements of the participants and temporal aspects of their movements. Using this data we trained a rule learner, a Naive Bayes classifier, and a k-nearest neighbor classifier and evaluated the classifiers with 10-fold cross validation and leave-one-out cross validation. The results of this evaluation suggest the following: (1) The detection of an error situation works well, when the robot has seen the human before; (2) Rule learner and k-nearest neighbor classifiers work well for automated error detection when the robot is interacting with a known human; (3) For unknown humans, the Naive Bayes classifier performed the best; (4) The classification of social norm violations does perform the worst; (5) There was no big performance difference between using the original data and normalized feature sets that represent the relative position of the participants.

Visual Analytics (VA) aims to combine the strengths of humans and computers for effective data analysis. In this endeavor, humans’ tacit knowledge from prior experience is an important asset that can be leveraged by both human and computer to improve the analytic process. While VA environments are starting to include features to formalize, store, and utilize such knowledge, the mechanisms and degree in which these environments integrate explicit knowledge varies widely. Additionally, this important class of VA environments has never been elaborated on by existing work on VA theory. This paper proposes a conceptual model of Knowledge-assisted VA conceptually grounded on the visualization model by van Wijk. We apply the model to describe various examples of knowledge-assisted VA from the literature and elaborate on three of them in finer detail. Moreover, we illustrate the utilization of the model to compare different design alternatives and to evaluate existing approaches with respect to their use of knowledge. Finally, the model can inspire designers to generate novel VA environments using explicit knowledge effectively.

Preprocessing is a mandatory first step to make data usable for analysis. While in time series analysis many established methods require data that are sampled in regular time intervals, in practice sensors may sample data at varying interval lengths. Time series rastering is the process of aggregating unequally spaced time series into equal interval lengths. In this paper we discuss critical aspects in the context of time series rastering, and we present a visual design which supports the parametrization of the rastering transformation, communicates the introduced uncertainties and quality issues, and facilitates the comparison of alternative rastering outcomes to achieve optimal results.

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 consider methods that guide or assist users in the visual analysis process. Several such methods already exist in the literature, yet we are lacking a general model that facilitates in-depth reasoning about guidance. We establish such a model by extending van Wijk's model of visualization with the fundamental components of guidance. Guidance is defined as a process that gradually narrows the gap that hinders effective continuation of the data analysis. We describe diverse inputs based on which guidance can be generated and discuss different degrees of guidance and means to incorporate guidance into VA tools. We use existing guidance approaches from the literature to illustrate the various aspects of our model. As a conclusion, we identify research challenges and suggest directions for future studies. With our work we take a necessary step to pave the way to a systematic development of guidance techniques that effectively support users in the context of VA.

Increasing amounts of data offer great opportunities to promote technological progress and business success. Visual analytics (VA) aims at enabling the exploration and the understanding of large and complex data sets by intertwining interactive visualization, data analysis, human-computer interaction, as well as cognitive and perceptual science. We propose a design triangle, which considers three main aspects to ease the design: (1) the characteristics of the data, (2) the users, and (3) the users' tasks. Addressing the particular characteristics of time and time-oriented data focuses the VA methods, but turns the design space into a more complex and challenging one. We demonstrate the applicability of the design triangle by three use cases tackling the time-oriented aspects explicitly. Our design triangle provides a high-level framework, which is simple and very effective for the design process as well as easily applicable for both, researchers and practitioners.

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.

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.

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.

Health Technology Assessment (HTA) provides information for decision-makers in the health care system. The consortium IFEDH (Innovative Framework for Evidence-based Decision Support in Healthcare) was formed in order to design a new strategy for the integration of modelling and simulation into the HTA process within limited resources whilst staying open to emerging new technologies. This aim resulted in a framework that consists of a standardized workflow, tasks, methods and good practices. Beginning with formalization of the underlying problem and identification of research questions, the process is followed by data acquisition and modelling and finally ends by generating results for decision support. Visualization and documentation are crucial tasks that support the whole process. Additionally, standards for modelling were defined and new general modelling concepts were developed. All these developments serve as a basis for fast, credible, evidence-based decision support for comparing various technologies in the health system.

Characterizing the problem domain and understanding users' practices and processes are recognized as important steps in order to design and validate visualization, but are often disregarded in practice, also because of their complexity. We introduce the nested workflow model for design and validation of visual analytics, aimed at providing designers with a powerful and expressive modelling tool. This model enables the description of visual analytics processes, at different design levels, in terms of tasks, data, and users, including complex workflow patterns, data and knowledge flows, and collaboration between users. We discuss its application to two visual analytics projects, demonstrating its usefulness for their design and validation.