Visual Analytics (VA) is essential for supporting insight generation and knowledge discovery in complex data analysis tasks. However, traditional approaches often overlook the value of user-generated artefacts—such as annotations, parameterizations, selections, spatializations, and other constructs—encapsulating subjective judgments about data and highly contextualized insights. To address this gap, we propose persistent interaction as a paradigm for formalizing how users’ decisions are embedded within artefacts, ensuring their transferability across analytical contexts. In this paper, we introduce a classification of persistent user-generated artefacts, demonstrating their potential through two case studies. We contribute a framework for understanding persistent interaction, insights into artefact generalizability, knowledge transferability, and guidance enhancement, as well as theoretical implications for VA.

Abstract Data size and complexity in Visual Analytics (VA)pose significant challenges for VA systems andVA users. Two recent developments address these challenges: progressive VA (PVA) and guidance for VA (GVA). Both share the goal of supporting the analysis flow. PVA primarily considers the system perspective and incrementally generates partial results during long computations to avoid an unresponsive VA system. GVA is primarily concerned with the user perspective and strives to mitigate knowledge gaps during VA activities to prevent the analysis from stalling. Although PVA and GVA share the same goal, it has not yet been studied how PVA and GVA can join forces to achieve it. Our paper investigates this in detail. We structure our research around two questions: How can guidance enhance PVA and how can progressiveness enhance GVA? This leads to two main themes: Guidance for Progressiveness (G4P) and Progressiveness for Guidance (P4G). By exploring both themes, we arrive at a conceptual model of how progressiveness and guidance can work together. We illustrate the practical value of our theoretical considerations in two case studies ofG4P and P4G.

Abstract Art history traditionally relies on qualitative methods. However, the increasing availability of digitized archives has opened new possibilities for research by integrating visual analytics. This survey presents a comprehensive review of the intersection between art history and visual analytics, focusing on network visualization and how it supports researchers in analyzing and understanding complex art historical relationships through nodes (e.g., artists, artworks, institutions) and edges (the relationships between them). We explore how these approaches enable dynamic analysis, offering novel perspectives on artistic influence, stylistic evolution, and social interactions within the art world. Through this, we also examine wedges, a metaphor for the friction often present in art history between individuals and institutions. These tensions, which have historically played a pivotal role in shaping artistic movements, are now better understood through the lens of network visualization, revealing how conflicts and power dynamics influenced the development of art. Through a hierarchical categorization of the literature, we outline saturated problems and research areas as well as ongoing challenges in art historical research. Furthermore, we highlight the potential of visual analytics to bridge the gap between traditional qualitative research and modern computational analysis, offering interactive exploration, temporal analysis, and complex network visualization. We provide a structured foundation for future research in art history, emphasizing the value of network visualization in enriching the understanding of art history.

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

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

Enhancing Visual Analytics (VA) systems with guidance, such as the automated provision of data-driven suggestions and answers to the user’s task, is becoming increasingly important and common. However, how to design such systems remains a challenging task. We present a methodology to aid and structure the design of guidance for enhancing VA solutions consisting of four steps: (S1) defining the target of analysis, (S2) identifying the user tasks, (S3) describing the guidance tasks, and (S4) placing guidance. In summary, our proposed methodology specifies a space of possible user tasks and maps them to the corresponding space of guidance tasks, using recent abstract task typologies for guidance and visualization. We exemplify this methodology through two case studies from the literature: Overview , a system for exploring and labeling document collections aimed at journalists, and DoRIAH , a system for historical imagery analysis. We show how our methodology enriches existing VA solutions with guidance and provides a structured way to design guidance in complex VA scenarios.

Guidance can support users during the exploration and analysis of complex data. Previous research focused on characterizing the theoretical aspects of guidance in visual analytics and implementing guidance in different scenarios. However, the evaluation of guidance-enhanced visual analytics solutions remains an open research question. We tackle this question by introducing and validating a practical evaluation methodology for guidance in visual analytics. We identify eight quality criteria to be fulfilled and collect expert feedback on their validity. To facilitate actual evaluation studies, we derive two sets of heuristics. The first set targets heuristic evaluations conducted by expert evaluators. The second set facilitates end-user studies where participants actually use a guidance-enhanced system. By following such a dual approach, the different quality criteria of guidance can be examined from two different perspectives, enhancing the overall value of evaluation studies. To test the practical utility of our methodology, we employ it in two studies to gain insight into the quality of two guidance-enhanced visual analytics solutions, one being a work-in-progress research prototype, and the other being a publicly available visualization recommender system. Based on these two evaluations, we derive good practices for conducting evaluations of guidance in visual analytics and identify pitfalls to be avoided during such studies.

The dynamic network visualization design space consists of two major dimensions: network structural and temporal representation. As more techniques are developed and published, a clear need for evaluation and experimental comparisons between them emerges. Most studies explore the temporal dimension and diverse interaction techniques supporting the participants, focusing on a single structural representation. Empirical evidence about performance and preference for different visualization approaches is scattered over different studies, experimental settings, and tasks. This paper aims to comprehensively investigate the dynamic network visualization design space in two evaluations. First, a controlled study assessing participants' response times, accuracy, and preferences for different combinations of network structural and temporal representations on typical dynamic network exploration tasks, with and without the support of standard interaction methods. Second, the best-performing combinations from the first study are enhanced based on participants' feedback and evaluated in a heuristic-based qualitative study with visualization experts on a real-world network. Our results highlight node-link with animation and playback controls as the best-performing combination and the most preferred based on ratings. Matrices achieve similar performance to node-link in the first study but have considerably lower scores in our second evaluation. Similarly, juxtaposition exhibits evident scalability issues in more realistic analysis contexts.

Unexploded Ordnance (UXO) detection, the identification of remnant active bombs buried underground from archival aerial images, implies a complex workflow involving decision-making at each stage. An essential phase in UXO detection is the task of image selection, where a small subset of images must be chosen from archives to reconstruct an area of interest (AOI) and identify craters. The selected image set must comply with good spatial and temporal coverage over the AOI, particularly in the temporal vicinity of recorded aerial attacks, and do so with minimal images for resource optimization. This paper presents a guidance-enhanced visual analytics prototype to select images for UXO detection. In close collaboration with domain experts, our design process involved analyzing user tasks, eliciting expert knowledge, modeling quality metrics, and choosing appropriate guidance. We report on a user study with two real-world scenarios of image selection performed with and without guidance. Our solution was well-received and deemed highly usable. Through the lens of our task-based design and developed quality measures, we observed guidance-driven changes in user behavior and improved quality of analysis results. An expert evaluation of the study allowed us to improve our guidance-enhanced prototype further and discuss new possibilities for user-adaptive guidance.

Modern science and industry rely on computational models for simulation, prediction, and data analysis. Spatial blind source separation (SBSS) is a model used to analyze spatial data. Designed explicitly for spatial data analysis, it is superior to popular non-spatial methods, like PCA. However, a challenge to its practical use is setting two complex tuning parameters, which requires parameter space analysis. In this paper, we focus on sensitivity analysis (SA). SBSS parameters and outputs are spatial data, which makes SA difficult as few SA approaches in the literature assume such complex data on both sides of the model. Based on the requirements in our design study with statistics experts, we developed a visual analytics prototype for data type agnostic visual sensitivity analysis that fits SBSS and other contexts. The main advantage of our approach is that it requires only dissimilarity measures for parameter settings and outputs (Fig. 1). We evaluated the prototype heuristically with visualization experts and through interviews with two SBSS experts. In addition, we show the transferability of our approach by applying it to microclimate simulations. Study participants could confirm suspected and known parameter-output relations, find surprising associations, and identify parameter subspaces to examine in the future. During our design study and evaluation, we identified challenging future research opportunities.

Draco has been developed as an automated visualization recommendation system formalizing design knowledge as logical constraints in ASP (Answer-Set Programming). With an increasing set of constraints and incorporated design knowledge, even visualization experts lose overview in Draco and struggle to retrace the automated recommendation decisions made by the system. Our paper proposes an Visual Analytics (VA) approach to visualize and analyze Draco's constraints. Our VA approach is supposed to enable visualization experts to accomplish identified tasks regarding the knowledge base and support them in better understanding Draco. We extend the existing data extraction strategy of Draco with a data processing architecture capable of extracting features of interest from the knowledge base. A revised version of the ASP grammar provides the basis for this data processing strategy. The resulting incorporated and shared features of the constraints are then visualized using a hypergraph structure inside the radial-arranged constraints of the elaborated visualization. The hierarchical categories of the constraints are indicated by arcs surrounding the constraints. Our approach is supposed to enable visualization experts to interactively explore the design rules' violations based on highlighting respective constraints or recommendations. A qualitative and quantitative evaluation of the prototype confirms the prototype's effectiveness and value in acquiring insights into Draco's recommendation process and design constraints.

Abstract Modelling the dynamics of a growing financial environment is a complex task that requires domain knowledge, expertise and access to heterogeneous information types. Such information can stem from several sources at different scales, complicating the task of forming a holistic impression of the financial landscape, especially in terms of the economical relationships between firms. Bringing this scattered information into a common context is, therefore, an essential step in the process of obtaining meaningful insights about the state of an economy. In this paper, we present Sabrina 2.0, a Visual Analytics (VA) approach for exploring financial data across different scales, from individual firms up to nation‐wide aggregate data. Our solution is coupled with a pipeline for the generation of firm‐to‐firm financial transaction networks, fusing information about individual firms with sector‐to‐sector transaction data and domain knowledge on macroscopic aspects of the economy. Each network can be created to have multiple instances to compare different scenarios. We collaborated with experts from finance and economy during the development of our VA solution, and evaluated our approach with seven domain experts across industry and academia through a qualitative insight‐based evaluation. The analysis shows how Sabrina 2.0 enables the generation of insights, and how the incorporation of transaction models assists users in their exploration of a national economy.

Networks are abstract and ubiquitous data structures, defined as a set of data points and relationships between them. Network visualization provides meaningful representations of these data, supporting researchers in understanding the connections, gathering insights, and detecting and identifying unexpected patterns. Research in this field is focusing on increasingly challenging problems, such as visualizing dynamic, complex, multivariate, and geospatial networked data. This ever-growing, and widely varied, body of research led to several surveys being published, each covering one or more disciplines of network visualization. Despite this effort, the variety and complexity of this research represents an obstacle when surveying the domain and building a comprehensive overview of the literature. Furthermore, there exists a lack of clarification and uniformity between the terminology used in each of the surveys, which requires further effort when mapping and categorizing the plethora of different visualization techniques and approaches. In this paper, we aim at providing researchers and practitioners alike with a "roadmap" detailing the current research trends in the field of network visualization. We design our contribution as a meta-survey where we discuss, summarize, and categorize recent surveys and task taxonomies published in the context of network visualization. We identify more and less saturated disciplines of research and consolidate the terminology used in the surveyed literature. We also survey the available task taxonomies, providing a comprehensive analysis of their varying support to each network visualization discipline and by establishing and discussing a classification for the individual tasks. With this combined analysis of surveys and task taxonomies, we provide an overarching structure of the field, from which we extrapolate the current state of research and promising directions for future work.

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

Abstract Temporal networks can naturally model real‐world complex phenomena such as contact networks, information dissemination and physical proximity. However, nodes and edges bear real‐time coordinates, making it difficult to organize them into discrete timeslices, without a loss of temporal information due to projection. Event‐based dynamic graph drawing rejects the notion of a timeslice and allows each node and edge to retain its own real‐valued time coordinate. While existing work has demonstrated clear advantages for this approach, they come at a running time cost. We investigate the problem of accelerating event‐based layout to make it more competitive with existing layout techniques. In this paper, we describe the design, implementation and experimental evaluation of MultiDynNoS, the first multi‐level event‐based graph layout algorithm. We consider three operators for coarsening and placement, inspired by Walshaw, GRIP and FM3, which we couple with an event‐based graph drawing algorithm. We also propose two extensions to the core algorithm: AutoTau and Bend Transfer. We perform two experiments: first, we compare MultiDynNoS variants to existing state‐of‐the‐art dynamic graph layout approaches; second, we investigate the impact of each of the proposed algorithm extensions. MultiDynNoS proves to be competitive with existing approaches, and the proposed extensions achieve their design goals and contribute in opening new research directions.

Abstract Guidance has been proposed as a conceptual framework to understand how mixed‐initiative visual analytics approaches can actively support users as they solve analytical tasks. While user tasks received a fair share of attention, it is still not completely clear how they could be supported with guidance and how such support could influence the progress of the task itself. Our observation is that there is a research gap in understanding the effect of guidance on the analytical discourse, in particular, for the knowledge generation in mixed‐initiative approaches. As a consequence, guidance in a visual analytics environment is usually indistinguishable from common visualization features, making user responses challenging to predict and measure. To address these issues, we take a system perspective to propose the notion of guidance tasks and we present it as a typology closely aligned to established user task typologies. We derived the proposed typology directly from a model of guidance in the knowledge generation process and illustrate its implications for guidance design. By discussing three case studies, we show how our typology can be applied to analyze existing guidance systems. We argue that without a clear consideration of the system perspective, the analysis of tasks in mixed‐initiative approaches is incomplete. Finally, by analyzing matchings of user and guidance tasks, we describe how guidance tasks could either help the user conclude the analysis or change its course.

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

In social networks, individuals' decisions are strongly influenced by recommendations from their friends, acquaintances, and favorite renowned personalities. The popularity of online social networking platforms makes them the prime venues to advertise products and promote opinions. The Influence Maximization (IM) problem entails selecting a seed set of users that maximizes the influence spread, i.e., the expected number of users positively influenced by a stochastic diffusion process triggered by the seeds. Engineering and analyzing IM algorithms remains a difficult and demanding task due to the NP-hardness of the problem and the stochastic nature of the diffusion processes. Despite several heuristics being introduced, they often fail in providing enough information on how the network topology affects the diffusion process, precious insights that could help researchers improve their seed set selection. In this paper, we present VAIM, a visual analytics system that supports users in analyzing, evaluating, and comparing information diffusion processes determined by different IM algorithms. Furthermore, VAIM provides useful insights that the analyst can use to modify the seed set of an IM algorithm, so to improve its influence spread. We assess our system by: (i) a qualitative evaluation based on a guided experiment with two domain experts on two different data sets; (ii) a quantitative estimation of the value of the proposed visualization through the ICE-T methodology by Wall et al. (IEEE TVCG - 2018). The twofold assessment indicates that VAIM effectively supports our target users in the visual analysis of the performance of IM algorithms.

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 eight 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.

This February 2022 issue of theIEEE Transactions on Visualization and Computer Graphics (TVCG)contains the proceedings of IEEE VIS 2021, held online on October 24-29, 2021, with General Chairs from Tulane University and Universidade de Sao Paulo. With IEEE VIS 2021, the conference series is in its 32nd year.

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

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.

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.

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. 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.

This February 2021 issue of the IEEE Transactions on Visualization and Computer Graphics (TVCG) contains the proceedings of IEEE VIS 2020, held online between 25-30 October 2020, hosted by General Chairs from the University of Utah. With IEEE VIS 2020, the conference series is in its 31st year. IEEE VIS consists of three conferences, held concurrently: the IEEE Visual Analytics Science and Technology Conference (VAST), the IEEE Information Visualization Conference (InfoVis), and the IEEE Scientific Visualization Conference (SciVis). These three conferences are the premier venues for the visualization community to exchange the latest ideas and developments, attracting researchers and practitioners alike.

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.

Visualizing big and complex multivariate data is challenging. To address this challenge, we propose flexible visual analytics (FVA) with the aim to mitigate visual complexity and interaction complexity challenges in visual analytics, while maintaining the strengths of multiple perspectives on the studied data. At the heart of our proposed approach are transitions that fluidly transform data between user-relevant views to offer various perspectives and insights into the data. While smooth display transitions have been already proposed, there has not yet been an interdisciplinary discussion to systematically conceptualize and formalize these ideas. As a call to further action, we argue that future research is necessary to develop a conceptual framework for flexible visual analytics. We discuss preliminary ideas for prioritizing multi-aspect visual representations and multi-aspect transitions between them, and consider the display user for whom such depictions are produced and made available for visual analytics. With this contribution we aim to further facilitate visual analytics on complex data sets for varying data exploration tasks and purposes based on different user characteristics and data use contexts.

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.

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.

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.

Abstract Visual data analysis can be envisioned as a collaboration of the user and the computational system with the aim of completing a given task. Pursuing an effective system‐user integration, in which the system actively helps the user to reach his/her analysis goal has been focus of visualization research for quite some time. However, this problem is still largely unsolved. As a result, users might be overwhelmed by powerful but complex visual analysis systems which also limits their ability to produce insightful results. In this context, guidance is a promising step towards enabling an effective mixed‐initiative collaboration to promote the visual analysis. However, the way how guidance should be put into practice is still to be unravelled. Thus, we conducted a comprehensive literature research and provide an overview of how guidance is tackled by different approaches in visual analysis systems. We distinguish between guidance that is provided by the system to support the user, and guidance that is provided by the user to support the system. By identifying open problems, we highlight promising research directions and point to missing factors that are needed to enable the envisioned human‐computer collaboration, and thus, promote a more effective visual data analysis.

Abstract The Curriculum Vitae (CV, also referred to as “résumé”) is an established representation of a person's academic and professional history. A typical CV is comprised of multiple sections associated with spatio‐temporal, nominal, hierarchical, and ordinal data. The main task of a recruiter is, given a job application with specific requirements, to compare and assess CVs in order to build a short list of promising candidates to interview. Commonly, this is done by viewing CVs in a side‐by‐side fashion. This becomes challenging when comparing more than two CVs, because the reader is required to switch attention between them. Furthermore, there is no guarantee that the CVs are structured similarly, thus making the overview cluttered and significantly slowing down the comparison process. In order to address these challenges, in this paper we propose “CV3”, an interactive exploration environment offering users a new way to explore, assess, and compare multiple CVs, to suggest suitable candidates for specific job requirements. We validate our system by means of domain expert feedback whose results highlight both the efficacy of our approach and its limitations. We learned that CV3 eases the overall burden of recruiters thereby assisting them in the selection process.

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.

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.

Abstract 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 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.

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.

Abstract 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.

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.

The increasingly large number of available writings describing technical and scientific progress, calls for advanced analytic tools for their efficient analysis. This is true for many application scenarios in science and industry and for different types of writings, comprising patents and scientific articles. Despite important differences between patents and scientific articles, both have a variety of common characteristics that lead to similar search and analysis tasks. However, the analysis and visualization of these documents is not a trivial task due to the complexity of the documents as well as the large number of possible relations between their multivariate attributes. In this survey, we review interactive analysis and visualization approaches of patents and scientific articles, ranging from exploration tools to sophisticated mining methods. In a bottom-up approach, we categorize them according to two aspects: (a) data type (text, citations, authors, metadata, and combinations thereof), and (b) task (finding and comparing single entities, seeking elementary relations, finding complex patterns, and in particular temporal patterns, and investigating connections between multiple behaviours). Finally, we identify challenges and research directions in this area that ask for future investigations.

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 .

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.

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.

Multi-class classifiers often compute scores for the classification samples describing probabilities to belong to different classes. In order to improve the performance of such classifiers, machine learning experts need to analyze classification results for a large number of labeled samples to find possible reasons for incorrect classification. Confusion matrices are widely used for this purpose. However, they provide no information about classification scores and features computed for the samples. We propose a set of integrated visual methods for analyzing the performance of probabilistic classifiers. Our methods provide insight into different aspects of the classification results for a large number of samples. One visualization emphasizes at which probabilities these samples were classified and how these probabilities correlate with classification error in terms of false positives and false negatives. Another view emphasizes the features of these samples and ranks them by their separation power between selected true and false classifications. We demonstrate the insight gained using our technique in a benchmarking classification dataset, and show how it enables improving classification performance by interactively defining and evaluating post-classification rules.

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.

Visual Analytics (VA) is an evolving field that, at its core, is directed to the science of analytical reasoning supported by highly interactive visual interfaces. The IEEE Conference on Visual Analytics Science and Technology (IEEE VAST), founded in 2006 as the IEEE Symposium on Visual Analytics Science and Technology, is the first international conference dedicated to advances in Visual Analytics Science and Technology. The IEEE Transactions on Visualization and Computer Graphics (TVCG) has recognized and honored the importance of Visual Analytics from the beginning, and invites the authors of the best conference papers to submit substantively extended versions of VAST papers to the journal. For these papers, TVCG applies the usual standard in asking for more than 30% new material and insights compared to the conference paper. This special section presents the extended versions of the best papers of IEEE VAST 2011, which took place in October 2011 in Providence, Rhode Island, USA. These papers were selected together with the best paper award selection committee, which was composed of three members who reviewed the top papers and their peer reviews. The three selected papers went through the regular and standard reviewing process of TVCG. The papers presented here reflect the diversity of the growing field of visual analytics. Collectively, the set of papers exemplify three components that are central to visual analytics as a field. the Guest Editors then provide an overview of the technical articles and features presented in this issue.

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.

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.

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.

The study of isolated populations is a promising approach for the identification of genes conferring susceptibility to disease. Due to the complex genealogies of such populations epidemiological studies are challenging.We present ongoing research to using a visual analytics based approach for the identification of diseases that cluster in families, risk factors and heritability patterns.

Transforming a text-based clinical guideline in a computer-interpretable form is a time-consuming and demanding task due to the various users involved, who have different technical and medical background. In the past, different guideline representation languages and supporting tools have been developed, however, these approaches seldom address the various users' demands and needs in the different steps of the guidelines' life cycle. Our approach is oriented on the guideline life-cycle and takes the requirements and the interactions of the various actors into account to formalize guidelines in a computer-interpretable guideline representation by using a semi-automatic way based on NLP techniques. We analyzed the guideline life-cycle and the roles of the actors to build such a model. This model is prototypical implemented and showed the usefulness and utility for the various users.

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.

Clinical practice guidelines are widely used to support medical staff in treatment planning and decision-making, whereas, the classification of different recommendations in the CPGs are one of the most important information sources to use. However, there is a lack of consensus amongst guideline developers, regarding those classification schemes. To address this problem, we mapped the different graded and ungraded evidence information used by different guideline developing organizations into our meta schema. In this paper we describe how guideline representation languages, such as Asbru and PROforma can be extended to model our meta schema.

The evaluation of Information Visualization (InfoVis) techniques can help to identify specific strengths and weaknesses of these methods. The following article describes the results of an empirical study assessing the contribution of an interactive InfoVis method based on a spring metaphor (GRAVI), exploratory data analysis (EDA) and machine learning (ML) to ease understanding. The application domain is the psychotherapeutic treatment of anorectic young women. The three methods are supposed to support the therapists in finding the variables which influence success or failure of the therapy. To conduct the evaluation we developed a report system which helped subjects to formulate and document in a self-directed manner the insights they gained when using the three methods. The results indicate that the three methods are complementary and should be used in conjunction.

Most applications in physical calculations provide powerful multivariate outputs but use rather simple visual representations (diagrams) without the possibilities to explore the resulting data sets. Interdependences in the data are overlooked or stay unseen and the analysis is very time-consuming, because of the difficulty and complexity for users to grasp the results. In this paper, we present a tool which combines a visual structure recognised by the physical community with interactive information visualization techniques to support the analysis of decay chain calculations. In a case study we showed that our tool helped experts in the field to analyse radioactive decay chains where the representation of results is more concise and conclusions are easier drawn

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.

Comparing different information visualization (InfoVis) techniques is a challenging task and a necessary step to reach the users and their tasks. We evaluated the effectiveness in visualizing psychotherapeutic data of two InfoVis techniques, namely the stardinates and the parallel coordinates by a comparative study with 22 participants. Based on three research questions we interpret the results of our study in order to derive statements on both visualization techniques. We evaluated (1) the time participants were engaged in testing our material; (2) the number of correct answers; (3) subjects' statements, which were categorized by the type of statement; and (4) the subjects' key statements in comparison to those defined by an expert. Our empirical results indicate that the stardinates are a more appropriate method for interpreting such highly structured data in detail whereas parallel coordinates show advantages for gaining information at the first glance.

In modern intensive care units (ICUs), the medical staff has to monitor a huge amount of high-dimensional and time-oriented data, which needs to be visualized user- and task-specifically to ease diagnosis and treatment planning. Available visual representations, like diagrams or charts neglect the implicit information as well as a-priory or associated knowledge about the data and its meaning (for example, 38.5°C (101.3°F) is moderate fever and 41°C (105.8°F) is critical fever). Another challenge is to provide appropriate interaction techniques to explore and navigate the data and its temporal dimensions. In this context one major challenge is to connect time-oriented data and information to a coherent interactive visualization. In this paper we present different interactive visualization techniques which enable the users to reveal the data at several levels of detail and abstraction, ranging from a broad overview to the fine structure. We will also introduce a time visualization and navigation technique that connects overview+detail, pan+zoom, and focus+context features to one powerful time-browser.

The Stardinates are a novel interactive information visualization (InfoVis) technique, which aims at visualizing highly structured data. They represent some Gestalt principles very well, especially the principles of Closure and 'Pragnanz'. As a consequence, Stardinates form very distinct and memorable patterns, which make abstraction and aggregation much easier. We give a formal description of the Stardinates as a basis for implementation. Furthermore, we show an application by visualizing psychotherapeutic data derived from a clinical study on anorectic girls.

OBJECTIVES: Many groups are developing computer-interpretable clinical guidelines (CIGs) for use during clinical encounters. CIGs use "Task-Network Models" for representation but differ in their approaches to addressing particular modeling challenges. We have studied similarities and differences between CIGs in order to identify issues that must be resolved before a consensus on a set of common components can be developed. DESIGN: We compared six models: Asbru, EON, GLIF, GUIDE, PRODIGY, and PROforma. Collaborators from groups that created these models represented, in their own formalisms, portions of two guidelines: American College of Chest Physicians cough guidelines [correction] and the Sixth Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. MEASUREMENTS: We compared the models according to eight components that capture the structure of CIGs. The components enable modelers to encode guidelines as plans that organize decision and action tasks in networks. They also enable the encoded guidelines to be linked with patient data-a key requirement for enabling patient-specific decision support. RESULTS: We found consensus on many components, including plan organization, expression language, conceptual medical record model, medical concept model, and data abstractions. Differences were most apparent in underlying decision models, goal representation, use of scenarios, and structured medical actions. CONCLUSION: We identified guideline components that the CIG community could adopt as standards. Some of the participants are pursuing standardization of these components under the auspices of HL7.

Clinical guidelines can be viewed as generic skeletal-plan schemata that represent clinical procedural knowledge and that are instantiated and refined dynamically by care providers over significant time periods. In the Asgaard project, we are investigating a set of tasks that support the application of clinical guidelines by a care provider other than the guideline's designer. We are focusing on the application of the guideline, recognition of care providers' intentions from their actions, and critique of care providers' actions given the guideline and the patient's medical record. We are developing methods that perform these tasks in multiple clinical domains, given an instance of a properly represented clinical guideline and an electronic medical patient record. In this paper, we point out the precise domain-specific knowledge required by each method, such as the explicit intentions of the guideline designer (represented as temporal patterns to be achieved or avoided). We present a machine-readable language, called Asbru, to represent and to annotate guidelines based on the task-specific ontology. We also introduce an automated tool for the acquisition of clinical guidelines based on the same ontology, developed using the PROTEGE-II framework.