Understanding how individuals interpret charts is a crucial concern for visual data communication. This imperative has motivated a number of studies, including past work demonstrating that causal priors-a priori belief about causal relationships between concepts-can have significant influences on the perceived strength of variable relationships inferred from visualizations. This paper builds on these previous results, demonstrating that causal priors can also influence the types of patterns that people perceive as the most salient within ambiguous scatterplots that have roughly equal evidence for trend and cluster patterns. Using a mixed-design approach that combines a large-scale online experiment for breadth of findings with an in-person think-aloud study for analytical depth, we investigated how users' interpretations are influenced by the interplay between causal priors and the visualized data patterns. Our analysis suggests two archetypal reasoning behaviors through which people often make their observations: contextualization, in which users accept a visual pattern that aligns with causal priors and use their existing knowledge to enrich interpretation, and rationalization, in which users encounter a pattern that conflicts with causal priors and attempt to explain away the discrepancy by invoking external factors, such as positing confounding variables or data selection bias. These findings provide initial evidence highlighting the critical role of causal priors in shaping high-level visualization comprehension, and introduce a vocabulary for describing how users reason about data that either confirms or challenges prior beliefs of causality.

Providing effective guidance for users has long been an important and challenging task for efficient exploratory visual analytics, especially when selecting variables for visualization in high-dimensional datasets. Correlation is the most widely applied metric for guidance in statistical and analytical tools, however a reliance on correlation may lead users towards false positives when interpreting causal relations in the data. In this work, inspired by prior insights on the benefits of counterfactual visualization in supporting visual causal inference, we propose a novel, simple, and efficient counterfactual guidance method to enhance causal inference performance in guided exploratory analytics based on insights and concerns gathered from expert interviews. Our technique aims to capitalize on the benefits of counterfactual approaches while reducing their complexity for users. We integrated counterfactual guidance into an exploratory visual analytics system, and using a synthetically generated ground-truth causal dataset, conducted a comparative user study and evaluated to what extent counterfactual guidance can help lead users to more precise visual causal inferences. The results suggest that counterfactual guidance improved visual causal inference performance, and also led to different exploratory behaviors compared to correlation-based guidance. Based on these findings, we offer future directions and challenges for incorporating counterfactual guidance to better support exploratory visual analytics.

"Correlation does not imply causation" is a famous mantra in statistical and visual analysis. However, consumers of visualizations often draw causal conclusions when only correlations between variables are shown. In this paper, we investigate factors that contribute to causal relationships users perceive in visualizations. We collected a corpus of concept pairs from variables in widely used datasets and created visualizations that depict varying correlative associations using three typical statistical chart types. We conducted two MTurk studies on (1) preconceived notions on causal relations without charts, and (2) perceived causal relations with charts, for each concept pair. Our results indicate that people make assumptions about causal relationships between pairs of concepts even without seeing any visualized data. Moreover, our results suggest that these assumptions constitute causal priors that, in combination with visualized association, impact how data visualizations are interpreted. The results also suggest that causal priors may lead to over- or under-estimation in perceived causal relations in different circumstances, and that those priors can also impact users' confidence in their causal assessments. In addition, our results align with prior work, indicating that chart type may also affect causal inference. Using data from the studies, we develop a model to capture the interaction between causal priors and visualized associations as they combine to impact a user's perceived causal relations. In addition to reporting the study results and analyses, we provide an open dataset of causal priors for 56 specific concept pairs that can serve as a potential benchmark for future studies. We also suggest remaining challenges and heuristic-based guidelines to help designers improve visualization design choices to better support visual causal inference.

In the early 2000s, the transition to paperless documentation of patients’ health data begun at large scale, with the introduction of Electronic Health and Medical Records (EHR and EMR, respectively). This constituted a paradigm shift in how patient data was stored and exchanged among institutions. The impact of the so-called “Electronic Health Revolution”1 was significant. Standardization of personal health data allowed for a more uniform definition of diagnoses and their ensuing clinical process, with fewer mistakes in diagnosis and treatment, and a more reliable application of medical guidelines.2 For instance, in the United States (US), patients now have control over their information, with more mandated electronic access.3 Recent studies showed that online medical records by US adults doubled over the last 8 years.4 Simultaneously, a new generation of smart, affordable, and wearable devices, such as smartwatches, has emerged. These devices generate fine-grained and continuous data about the health status of their users, with minimal discomfort, eliminating the need for specialized equipment. The rapid evolution of Artificial Intelligence (AI) technologies is about to significantly impact healthcare as well. AI technologies present opportunities and challenges for both physicians and patients.5 AI models recognize patterns in complex datasets, potentially identifying a broader range of disease progression patterns that might not be immediately apparent to clinicians or patients. However, the inherent “black-box” nature of AI has slowed its adoption, as healthcare professionals often struggle to evaluate the underlying process that led to the AI recommendations. In essence, while it can be impressive what AI models predict, concerns remain about why the AI produces a particular output, and how. The considerable lack of transparency impedes trust-building, such that “the doctor just won’t accept that,”6 calling for explainable AI output.

Abstract The visual analytics community has long aimed to understand users better and assist them in their analytic endeavors. As a result, numerous conceptual models of visual analytics aim to formalize common workflows, techniques, and goals leveraged by analysts. While many of the existing approaches are rich in detail, they each are specific to a particular aspect of the visual analytic process. Furthermore, with an ever‐expanding array of novel artificial intelligence techniques and advances in visual analytic settings, existing conceptual models may not provide enough expressivity to bridge the two fields. In this work, we propose an agent‐based conceptual model for the visual analytic process by drawing parallels from the artificial intelligence literature. We present three examples from the visual analytics literature as case studies and examine them in detail using our framework. Our simple yet robust framework unifies the visual analytic pipeline to enable researchers and practitioners to reason about scenarios that are becoming increasingly prominent in the field, namely mixed‐initiative, guided, and collaborative analysis. Furthermore, it will allow us to characterize analysts, visual analytic settings, and guidance from the lenses of human agents, environments, and artificial agents, respectively.

When people search for information about a new topic within large document collections, they implicitly construct a mental model of the unfamiliar information space to represent what they currently know and guide their exploration into the unknown. Building this mental model can be challenging as it requires not only finding relevant documents but also synthesizing important concepts and the relationships that connect those concepts both within and across documents. This article describes a novel interactive approach designed to help users construct a mental model of an unfamiliar information space during exploratory search. We propose a new semantic search system to organize and visualize important concepts and their relations for a set of search results. A user study (n=20) was conducted to compare the proposed approach against a baseline faceted search system on exploratory literature search tasks. Experimental results show that the proposed approach is more effective in helping users recognize relationships between key concepts, leading to a more sophisticated understanding of the search topic while maintaining similar functionality and usability as a faceted search system.

Recommendation algorithms have been leveraged in various ways within visualization systems to assist users as they perform of a range of information tasks. One common focus for these techniques has been the recommendation of content, rather than visual form, as a means to assist users in the identification of information that is relevant to their task context. A wide variety of techniques have been proposed to address this general problem, with a range of design choices in how these solutions surface relevant information to users. This paper reviews the state-of-the-art in how visualization systems surface recommended content to users during users' visual analysis; introduces a four-dimensional design space for visual content recommendation based on a characterization of prior work; and discusses key observations regarding common patterns and future research opportunities.

Event sequence data record series of discrete events in the time order of occurrence. They are commonly observed in a variety of applications ranging from electronic health records to network logs, with the characteristics of large-scale, high-dimensional and heterogeneous. This high complexity of event sequence data makes it difficult for analysts to manually explore and find patterns, resulting in ever-increasing needs for computational and perceptual aids from visual analytics techniques to extract and communicate insights from event sequence datasets. In this paper, we review the state-of-the-art visual analytics approaches, characterize them with our proposed design space, and categorize them based on analytical tasks and applications. From our review of relevant literature, we have also identified several remaining research challenges and future research opportunities.

Anomaly detection is a common analytical task that aims to identify rare cases that differ from the typical cases that make up the majority of a dataset. When analyzing event sequence data, the task of anomaly detection can be complex because the sequential and temporal nature of such data results in diverse definitions and flexible forms of anomalies. This, in turn, increases the difficulty in interpreting detected anomalies. In this article, we propose a visual analytic approach for detecting anomalous sequences in an event sequence dataset via an unsupervised anomaly detection algorithm based on Variational AutoEncoders. We further compare the anomalous sequences with their reconstructions and with the normal sequences through a sequence matching algorithm to identify event anomalies. A visual analytics system is developed to support interactive exploration and interpretations of anomalies through novel visualization designs that facilitate the comparison between anomalous sequences and normal sequences. Finally, we quantitatively evaluate the performance of our anomaly detection algorithm, demonstrate the effectiveness of our system through case studies, and report feedback collected from study participants.

Complex, high-dimensional data is used in a wide range of domains to explore problems and make decisions. Analysis of high-dimensional data, however, is vulnerable to the hidden influence of confounding variables, especially as users apply ad hoc filtering operations to visualize only specific subsets of an entire dataset. Thus, visual data-driven analysis can mislead users and encourage mistaken assumptions about causality or the strength of relationships between features. This work introduces a novel visual approach designed to reveal the presence of confounding variables via counterfactual possibilities during visual data analysis. It is implemented in CoFact, an interactive visualization prototype that determines and visualizes counterfactual subsets to better support user exploration of feature relationships. Using publicly available datasets, we conducted a controlled user study to demonstrate the effectiveness of our approach; the results indicate that users exposed to counterfactual visualizations formed more careful judgments about feature-to-outcome relationships.

Visual analytics systems enable highly interactive exploratory data analysis. Across a range of fields, these technologies have been successfully employed to help users learn from complex data. However, these same exploratory visualization techniques make it easy for users to discover spurious findings. This paper proposes new methods to monitor a user’s analytic focus during visual analysis of structured datasets and use it to surface relevant articles that contextualize the visualized findings. Motivated by interactive analyses of electronic health data, this paper introduces a formal model of analytic focus, a computational approach to dynamically update the focus model at the time of user interaction, and a prototype application that leverages this model to surface relevant medical publications to users during visual analysis of a large corpus of medical records. Evaluation results with 24 users show that the modeling approach has high levels of accuracy and is able to surface highly relevant medical abstracts.

Abstract Data videos, a storytelling genre that visualizes data facts with motion graphics, are gaining increasing popularity among data journalists, non‐profits, and marketers to communicate data to broad audiences. However, crafting a data video is often time‐consuming and asks for various domain knowledge such as data visualization, animation design, and screenwriting. Existing authoring tools usually enable users to edit and compose a set of templates manually, which still cost a lot of human effort. To further lower the barrier of creating data videos, this work introduces a new approach, AutoClips, which can automatically generate data videos given the input of a sequence of data facts. We built AutoClips through two stages. First, we constructed a fact‐driven clip library where we mapped ten data facts to potential animated visualizations respectively by analyzing 230 online data videos and conducting interviews. Next, we constructed an algorithm that generates data videos from data facts through three steps: selecting and identifying the optimal clip for each of the data facts, arranging the clips into a coherent video, and optimizing the duration of the video. The results from two user studies indicated that the data videos generated by AutoClips are comprehensible, engaging, and have comparable quality with human‐made videos.

The collection and visual analysis of large-scale data from complex systems, such as electronic health records or clickstream data, has become increasingly common across a wide range of industries. This type of retrospective visual analysis, however, is prone to a variety of selection bias effects, especially for high-dimensional data where only a subset of dimensions is visualized at any given time. The risk of selection bias is even higher when analysts dynamically apply filters or perform grouping operations during ad hoc analyses. These bias effects threaten the validity and generalizability of insights discovered during visual analysis as the basis for decision making. Past work has focused on bias transparency, helping users understand when selection bias may have occurred. However, countering the effects of selection bias via bias mitigation is typically left for the user to accomplish as a separate process. Dynamic reweighting (DR) is a novel computational approach to selection bias mitigation that helps users craft bias-corrected visualizations. This paper describes the DR workflow, introduces key DR visualization designs, and presents statistical methods that support the DR process. Use cases from the medical domain, as well as findings from domain expert user interviews, are also reported.

Causality is crucial to understanding the mechanisms behind complex systems and making decisions that lead to intended outcomes. Event sequence data is widely collected from many real-world processes, such as electronic health records, web clickstreams, and financial transactions, which transmit a great deal of information reflecting the causal relations among event types. Unfortunately, recovering causalities from observational event sequences is challenging, as the heterogeneous and high-dimensional event variables are often connected to rather complex underlying event excitation mechanisms that are hard to infer from limited observations. Many existing automated causal analysis techniques suffer from poor explainability and fail to include an adequate amount of human knowledge. In this paper, we introduce a visual analytics method for recovering causalities in event sequence data. We extend the Granger causality analysis algorithm on Hawkes processes to incorporate user feedback into causal model refinement. The visualization system includes an interactive causal analysis framework that supports bottom-up causal exploration, iterative causal verification and refinement, and causal comparison through a set of novel visualizations and interactions. We report two forms of evaluation: a quantitative evaluation of the model improvements resulting from the user-feedback mechanism, and a qualitative evaluation through case studies in different application domains to demonstrate the usefulness of the system.

Clinical decision support systems are widely used to assist with medical decision making. However, clinical decision support systems typically require manually curated rules and other data that are difficult to maintain and keep up to date. Recent systems leverage advanced deep learning techniques and electronic health records to provide a more timely and precise result. Many of these techniques have been developed with a common focus on predicting upcoming medical events. However, although the prediction results from these approaches are promising, their value is limited by their lack of interpretability. To address this challenge, we introduce CarePre, an intelligent clinical decision assistance system. The system extends a state-of-the-art deep learning model to predict upcoming diagnosis events for a focal patient based on his or her historical medical records. The system includes an interactive framework together with intuitive visualizations designed to support diagnosis, treatment outcome analysis, and the interpretation of the analysis results. We demonstrate the effectiveness and usefulness of the CarePre system by reporting results from a quantities evaluation of the prediction algorithm, two case studies, and interviews with senior physicians and pulmonologists.

OBJECTIVE: To create an online visualization to support fatality management in North Carolina. MATERIALS AND METHODS: A web application aggregates online datasets for coronavirus disease 2019 (COVID-19) infection rates and morgue utilization. The data are visualized through an interactive, online dashboard. RESULTS: The web application was shared with state and local public health officials across North Carolina. Users could adjust interactive maps and other statistical charts to view live reports of metrics at multiple aggregation levels (eg, county or region). The application also provides access to detailed tabular data for individual facilities. DISCUSSION: Stakeholders found this tool helpful for providing situational awareness of capacity, hotspots, and utilization fluctuations. Timely reporting of facility and county data were key, and future work can help streamline the data collection process. There is potential to generalize the technology to other use cases. CONCLUSIONS: This dashboard facilitates fatality management by visualizing county and regional aggregate statistics in North Carolina.

The collection of large, complex datasets has become common across a wide variety of domains. Visual analytics tools increasingly play a key role in exploring and answering complex questions about these large datasets. However, many visualizations are not designed to concurrently visualize the large number of dimensions present in complex datasets (e.g. tens of thousands of distinct codes in an electronic health record system). This fact, combined with the ability of many visual analytics systems to enable rapid, ad-hoc specification of groups, or cohorts, of individuals based on a small subset of visualized dimensions, leads to the possibility of introducing selection bias-when the user creates a cohort based on a specified set of dimensions, differences across many other unseen dimensions may also be introduced. These unintended side effects may result in the cohort no longer being representative of the larger population intended to be studied, which can negatively affect the validity of subsequent analyses. We present techniques for selection bias tracking and visualization that can be incorporated into high-dimensional exploratory visual analytics systems, with a focus on medical data with existing data hierarchies. These techniques include: (1) tree-based cohort provenance and visualization, including a user-specified baseline cohort that all other cohorts are compared against, and visual encoding of cohort "drift", which indicates where selection bias may have occurred, and (2) a set of visualizations, including a novel icicle-plot based visualization, to compare in detail the per-dimension differences between the baseline and a user-specified focus cohort. These techniques are integrated into a medical temporal event sequence visual analytics tool. We present example use cases and report findings from domain expert user interviews.

Temporal event data are collected across a broad range of domains, and a variety of visual analytics techniques have been developed to empower analysts working with this form of data. These techniques generally display aggregate statistics computed over sets of event sequences that share common patterns. Such techniques are often hindered, however, by the high-dimensionality of many real-world event sequence datasets which can prevent effective aggregation. A common coping strategy for this challenge is to group event types together prior to visualization, as a pre-process, so that each group can be represented within an analysis as a single event type. However, computing these event groupings as a pre-process also places significant constraints on the analysis. This paper presents a new visual analytics approach for dynamic hierarchical dimension aggregation. The approach leverages a predefined hierarchy of dimensions to computationally quantify the informativeness, with respect to a measure of interest, of alternative levels of grouping within the hierarchy at runtime. This information is then interactively visualized, enabling users to dynamically explore the hierarchy to select the most appropriate level of grouping to use at any individual step within an analysis. Key contributions include an algorithm for interactively determining the most informative set of event groupings for a specific analysis context, and a scented scatter-plus-focus visualization design with an optimization-based layout algorithm that supports interactive hierarchical exploration of alternative event type groupings. We apply these techniques to high-dimensional event sequence data from the medical domain and report findings from domain expert interviews.

Anomaly detection is a common analytical task that aims to identify rare cases that differ from the typical cases that make up the majority of a dataset. When applied to the analysis of event sequence data, the task of anomaly detection can be complex because the sequential and temporal nature of such data results in diverse definitions and flexible forms of anomalies. This, in turn, increases the difficulty in interpreting detected anomalies. In this paper, we propose an unsupervised anomaly detection algorithm based on Variational AutoEncoders (VAE) to estimate underlying normal progressions for each given sequence represented as occurrence probabilities of events along the sequence progression. Events in violation of their occurrence probability are identified as abnormal. We also introduce a visualization system, EventThread3 (ET3, to support interactive exploration and interpretations of anomalies within the context of normal sequence progressions in the dataset through comprehensive one-to-many sequence comparison. Finally, we quantitatively evaluate the performance of our anomaly detection algorithm and demonstrate the effectiveness of our system through a case study.

OBJECTIVE: This article reports results from a systematic literature review related to the evaluation of data visualizations and visual analytics technologies within the health informatics domain. The review aims to (1) characterize the variety of evaluation methods used within the health informatics community and (2) identify best practices. METHODS: A systematic literature review was conducted following PRISMA guidelines. PubMed searches were conducted in February 2017 using search terms representing key concepts of interest: health care settings, visualization, and evaluation. References were also screened for eligibility. Data were extracted from included studies and analyzed using a PICOS framework: Participants, Interventions, Comparators, Outcomes, and Study Design. RESULTS: After screening, 76 publications met the review criteria. Publications varied across all PICOS dimensions. The most common audience was healthcare providers (n = 43), and the most common data gathering methods were direct observation (n = 30) and surveys (n = 27). About half of the publications focused on static, concentrated views of data with visuals (n = 36). Evaluations were heterogeneous regarding setting and measurements used. DISCUSSION: When evaluating data visualizations and visual analytics technologies, a variety of approaches have been used. Usability measures were used most often in early (prototype) implementations, whereas clinical outcomes were most common in evaluations of operationally-deployed systems. These findings suggest opportunities for both (1) expanding evaluation practices, and (2) innovation with respect to evaluation methods for data visualizations and visual analytics technologies across health settings. CONCLUSION: Evaluation approaches are varied. New studies should adopt commonly reported metrics, context-appropriate study designs, and phased evaluation strategies.

editorial Free Access Share on ACM TIST Special Issue on Visual Analytics Authors: Nan Cao Tongji University Tongji UniversityView Profile , Steffen Koch University of Stuttgart University of StuttgartView Profile , David Gotz University of North Carolina at Chapel Hill University of North Carolina at Chapel HillView Profile , Editor: Yingcai Wu State Key Lab of CAD8CG Zhejiang University State Key Lab of CAD8CG Zhejiang UniversityView Profile Authors Info & Claims ACM Transactions on Intelligent Systems and TechnologyVolume 10Issue 1January 2019 Article No.: 1pp 1–4https://doi.org/10.1145/3277019Published:13 December 2018Publication History 0citation404DownloadsMetricsTotal Citations0Total Downloads404Last 12 Months43Last 6 weeks10 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF

Event sequence data is common to a broad range of application domains, from security to health care to scholarly communication. This form of data captures information about the progression of events for an individual entity (e.g., a computer network device; a patient; an author) in the form of a series of time-stamped observations. Moreover, each event is associated with an event type (e.g., a computer login attempt, or a hospital discharge). Analyses of event sequence data have been shown to help reveal important temporal patterns, such as clinical paths resulting in improved outcomes, or an understanding of common career trajectories for scholars. Moreover, recent research has demonstrated a variety of techniques designed to overcome methodological challenges such as large volumes of data and high dimensionality. However, the effective identification and analysis of latent stages of progression, which can allow for variation within different but similarly evolving event sequences, remain a significant challenge with important real-world motivations. In this paper, we propose an unsupervised stage analysis algorithm to identify semantically meaningful progression stages as well as the critical events which help define those stages. The algorithm follows three key steps: (1) event representation estimation, (2) event sequence warping and alignment, and (3) sequence segmentation. We also present a novel visualization system, ET2, which interactively illustrates the results of the stage analysis algorithm to help reveal evolution patterns across stages. Finally, we report three forms of evaluation for ET2: (1) case studies with two real-world datasets, (2) interviews with domain expert users, and (3) a performance evaluation on the progression analysis algorithm and the visualization design.

The Electrocardiogram (ECG) is commonly used to detect arrhythmias. Traditionally, a single ECG observation is used for diagnosis, making it difficult to detect irregular arrhythmias. Recent technology developments, however, have made it cost-effective to collect large amounts of raw ECG data over time. This promises to improve diagnosis accuracy, but the large data volume presents new challenges for cardiologists. This paper introduces ECGLens, an interactive system for arrhythmia detection and analysis using large-scale ECG data. Our system integrates an automatic heartbeat classification algorithm based on convolutional neural network, an outlier detection algorithm, and a set of rich interaction techniques. We also introduce A-glyph, a novel glyph designed to improve the readability and comparison of ECG signals. We report results from a comprehensive user study showing that A-glyph improves the efficiency in arrhythmia detection, and demonstrate the effectiveness of ECGLens in arrhythmia detection through two expert interviews.

Event sequence data such as electronic health records, a person's academic records, or car service records, are ordered series of events which have occurred over a period of time. Analyzing collections of event sequences can reveal common or semantically important sequential patterns. For example, event sequence analysis might reveal frequently used care plans for treating a disease, typical publishing patterns of professors, and the patterns of service that result in a well-maintained car. It is challenging, however, to visually explore large numbers of event sequences, or sequences with large numbers of event types. Existing methods focus on extracting explicitly matching patterns of events using statistical analysis to create stages of event progression over time. However, these methods fail to capture latent clusters of similar but not identical evolutions of event sequences. In this paper, we introduce a novel visualization system named EventThread which clusters event sequences into threads based on tensor analysis and visualizes the latent stage categories and evolution patterns by interactively grouping the threads by similarity into time-specific clusters. We demonstrate the effectiveness of EventThread through usage scenarios in three different application domains and via interviews with an expert user.

Rare category identification is an important task in many application domains, ranging from network security, to financial fraud detection, to personalized medicine. These are all applications which require the discovery and characterization of sets of rare but structurally-similar data entities which are obscured within a larger but structurally different dataset. This paper introduces RCLens, a visual analytics system designed to support user-guided rare category exploration and identification. RCLens adopts a novel active learning-based algorithm to iteratively identify more accurate rare categories in response to user-provided feedback. The algorithm is tightly integrated with an interactive visualization-based interface which supports a novel and effective workflow for rare category identification. This paper (1) defines RCLens' underlying active-learning algorithm; (2) describes the visualization and interaction designs, including a discussion of how the designs support user-guided rare category identification; and (3) presents results from an evaluation demonstrating RCLens' ability to support the rare category identification process.

Large and high-dimensional real-world datasets are being gathered across a wide range of application disciplines to enable data-driven decision making. Interactive data visualization can play a critical role in allowing domain experts to select and analyze data from these large collections. However, there is a critical mismatch between the very large number of dimensions in complex real-world datasets and the much smaller number of dimensions that can be concurrently visualized using modern techniques. This gap in dimensionality can result in high levels of selection bias that go unnoticed by users. The bias can in turn threaten the very validity of any subsequent insights. This article describes Adaptive Contextualization (AC), a novel approach to interactive visual data selection that is specifically designed to combat the invisible introduction of selection bias. The AC approach (1) monitors and models a user’s visual data selection activity, (2) computes metrics over that model to quantify the amount of selection bias after each step, (3) visualizes the metric results, and (4) provides interactive tools that help users assess and avoid bias-related problems. This article expands on an earlier article presented at ACM IUI 2016 [16] by providing a more detailed review of the AC methodology and additional evaluation results.

Large and high-dimensional real-world datasets are being gathered across a wide range of application disciplines to enable data-driven decision making. Interactive data visualization can play a critical role in allowing domain experts to select and analyze data from these large collections. However, there is a critical mismatch between the very large number of dimensions in complex real-world datasets and the much smaller number of dimensions that can be concurrently visualized using modern techniques. This gap in dimensionality can result in high levels of selection bias that go unnoticed by users. The bias can in turn threaten the very validity of any subsequent insights. In this paper, we present Adaptive Contextualization (AC), a novel approach to interactive visual data selection that is specifically designed to combat the invisible introduction of selection bias. Our approach (1) monitors and models a user's visual data selection activity, (2) computes metrics over that model to quantify the amount of selection bias after each step, (3) visualizes the metric results, and (4) provides interactive tools that help users assess and avoid bias-related problems. We also share results from a user study which demonstrate the effectiveness of our technique.

The ten papers in this special section are devoted to the topic of visual analytics, an emerging research direction that focuses on data exploration and analysis with a seamless integration of interaction, visualization, and analysis.

The unprecedented availability of social media data offers substantial opportunities for data owners, system operators, solution providers, and end users to explore and understand social dynamics. However, the exponential growth in the volume, velocity, and variability of social media data prevents people from fully utilizing such data. Visual analytics, which is an emerging research direction, has received considerable attention in recent years. Many visual analytics methods have been proposed across disciplines to understand large-scale structured and unstructured social media data. This objective, however, also poses significant challenges for researchers to obtain a comprehensive picture of the area, understand research challenges, and develop new techniques. In this paper, we present a comprehensive survey to characterize this fast-growing area and summarize the state-of-the-art techniques for analyzing social media data. In particular, we classify existing techniques into two categories: gathering information and understanding user behaviors. We aim to provide a clear overview of the research area through the established taxonomy. We then explore the design space and identify the research trends. Finally, we discuss challenges and open questions for future studies.

Data with multiple probabilistic labels are common in many situations. For example, a movie may be associated with multiple genres with different levels of confidence. Despite their ubiquity, the problem of visualizing probabilistic labels has not been adequately addressed. Existing approaches often either discard the probabilistic information, or map the data to a low-dimensional subspace where their associations with original labels are obscured. In this paper, we propose a novel visual technique, UnTangle Map, for visualizing probabilistic multi-labels. In our proposed visualization, data items are placed inside a web of connected triangles, with labels assigned to the triangle vertices such that nearby labels are more relevant to each other. The positions of the data items are determined based on the probabilistic associations between items and labels. UnTangle Map provides both (a) an automatic label placement algorithm, and (b) adaptive interactions that allow users to control the label positioning for different information needs. Our work makes a unique contribution by providing an effective way to investigate the relationship between data items and their probabilistic labels, as well as the relationships among labels. Our user study suggests that the visualization effectively helps users discover emergent patterns and compare the nuances of probabilistic information in the data labels.

As medical organizations modernize their operations, they are increasingly adopting electronic health records (EHRs) and deploying new health information technology systems that create, gather, and manage their information. As a result, the amount of data available to clinicians, administrators, and researchers in the healthcare system continues to grow at an unprecedented rate.1 However, despite the substantial evidence showing the benefits of EHR adoption, e-prescriptions, and other components of health information exchanges, healthcare providers often report only modest improvements in their ability to make better decisions by using more comprehensive clinical information.2,3 The large volume of clinical data now being captured for each patient poses many challenges to (a) clinicians trying to combine data from different disparate systems and make sense of the patient’s condition within the context of the patient’s medical history, (b) administrators trying to make decisions grounded in data, (c) researchers trying to understand differences in population outcomes, and (d) patients trying to make use of their own medical data. In fact, despite the many hopes that access to more information would lead to more informed decisions, access to comprehensive and large-scale clinical data resources has instead made some analytical processes even more difficult.4 Visual analytics is an emerging discipline that has shown significant promise in addressing many of these information overload challenges. Visual analytics is the science of analytical reasoning facilitated by advanced interactive visual interfaces.5 In order to facilitate reasoning over, and interpretation of, complex data, visual analytics techniques combine concepts from data mining, machine learning, human computing interaction, and human cognition. As the volume of health-related data continues to grow at unprecedented rates and new information systems are deployed to those already overrun with too much data, there is a need for exploring how visual analytics methods can be used to avoid information overload. Information overload is the problem that arises when individuals try to analyze a number of variables that surpass the limits of human cognition.6 Information overload often leads to users ignoring, overlooking, or misinterpreting crucial information. The information overload problem is widespread in the healthcare domain and can result in incorrect interpretations of data, wrong diagnoses, and missed warning signs of impending changes to patient conditions. The multi-modal and heterogeneous properties of EHR data together with the frequency of redundant, irrelevant, and subjective measures pose significant challenges to users trying to synthesize the information and obtain actionable insights. Yet despite these challenges, the promise of big data in healthcare remains.1 There is a critical need to support research and pilot projects to study effective ways of using visual analytics to support the analysis of large amounts of medical data. Currently new interactive interfaces are being developed to unlock the value of large-scale clinical databases for a wide variety of different tasks. For instance, visual analytics could help provide clinicians with more effective ways to combine the longitudinal clinical data with the patient-generated health data to better understand patient progression. Patients could be supported in understanding personalized wellness plans and comparing their health measurements against similar patients. Researchers could use visual analytics tools to help perform population-based analysis and obtain insights from large amounts of clinical data. Hospital administrators could use visual analytics to better understand the productivity of an organization, gaps in care, outcomes measurements, and patient satisfaction. Visual analytics systems—by combining advanced interactive visualization methods with statistical inference and correlation models—have the potential to support intuitive analysis for all of these user populations while masking the underlying complexity of the data. This special focus issue of JAMIA is dedicated to new research, applications, case studies, and approaches that use visual analytics to support the analysis of complex clinical data. The issue provides a broad picture of active work in this area, including four research and applications papers, one review paper, two brief communications, and one case report. First, West et al.7 present a systematic review of innovative visual analytics approaches that have been proposed to illustrate EHR data. The review paper groups over 800 articles in different categories and is a great resource for readers who are interested in understanding what has been done in applying visual analytics in healthcare settings. In the first of four research and application papers, Klimov et al.8 present a data-driven temporal data mining system with an interactive visual analytics interface to support the analysis and exploration of renal-damage risk factors in type II diabetes patients. Second, Huang et al. present a system to visually analyze the polymorbidity associated with chronic kidney disease. The study uses a large-scale database with over 14 000 patients and a Sankey-style visualization tool to help clinicians predict the outcome of complex disease based on comorbidities the patients have developed.9 Third, Hirsch et al.10 present a system called HARVEST, an interactive temporal visualization for longitudinal patient record. The system implements a patient record summarizer by extracting content from patient notes, aggregating and presenting information from multiple care settings, and supporting clinicians by providing a flexible tool for reviewing patients’ information. Finally, Soulakis et al.11 discuss practical methods for employing network analysis to visualize and describe multidisciplinary, collaborative care for hospitalized heart failure patients. The authors used data for over 500 patients and describe the provider’s collaboration network as a subset of collaborations within a complex graph with 1504 nodes and 83 998 edges. In a comprehensive case study, Warner et al.12 present a network visualization-based system to explore, analyze, and display the phenotypic patterns that may have remained occult or hidden in basic statistical and pairwise correlation analysis. The system allows clinicians and researchers to quickly generate hypotheses and gain deeper understanding of subpopulations. Finally, two brief communications describe early results from a pair of visual analysis systems. First, Basole et al.13 present an interactive system that uses visual analytics techniques to explore clinical data from 5784 pediatric asthma emergency department patients. The system makes cohort identification more efficient and possibly even more accurate that regular approaches. Second, Ratwani et al.14 present a dashboard-based system that employs visualization techniques to more effectively explore patient safety event reports, find patterns, and analyze the correlation between different hospital organizations. This special issue provides readers with a sample of the variety of work being performed around the world on the topic of visual analytics in healthcare. Researchers in this discipline are exploring a range of topics and a growing, vibrant research community has emerged.15 Many challenges remain, but we believe that visual analytics techniques will become essential elements in the future of health informatics. As the articles in this issue demonstrate, these methods promise to allow users of all sorts—clinicians, researchers, administrators, patients, and more—to derive actionable, meaningful insights form the vast and complex data resources that the modern health system is now creating. Yet this issue also indirectly highlights some of the areas of informatics that need more attention. These include the visual exploration of unstructured data (e.g., text), the challenge of clinical or operational adoption, and the lack of standardized methods for evaluation, validation, and measurement of efficacy of visual analytics tools. These are all critical areas for future work.

Data with multiple uncertain labels are common in many situations. For examples, a movie may be associated with multiple genres with different levels of confidence, and a protein sequence may be probabilistically assigned to several structural subcategories. Despite their ubiquity, the problem of visualizing uncertain labels has not been adequately addressed. Existing approaches often either discard the uncertainty information, or map the data to a low-dimensional subspace where their associations with multiple labels are obscured. In this paper, we propose a novel visual mining technique, UnTangle, for visualizing uncertain multi-labels. In our proposed visualization, data items are placed inside a web of connected triangles, with labels assigned to the triangle vertices such that nearby labels are more relevant to each other. The positions of the data items are determined based on the probabilistic associations between items and labels. UnTangle provides both (a) an automatic label placement algorithm, and (b) adaptive interaction mechanisms that allow users to control the label positioning for different visual queries. Our work makes a unique contribution by providing an effective way to investigate the relationship between data items and their uncertain labels, as well as the relationships among labels. Our user study suggests that the visualization effectively helps users discover emergent patterns and compare the nuances of uncertainty information in the data labels.

Temporal event sequence data is increasingly commonplace, with applications ranging from electronic medical records to financial transactions to social media activity. Previously developed techniques have focused on low-dimensional datasets (e.g., with less than 20 distinct event types). Real-world datasets are often far more complex. This paper describes DecisionFlow, a visual analysis technique designed to support the analysis of high-dimensional temporal event sequence data (e.g., thousands of event types). DecisionFlow combines a scalable and dynamic temporal event data structure with interactive multi-view visualizations and ad hoc statistical analytics. We provide a detailed review of our methods, and present the results from a 12-person user study. The study results demonstrate that DecisionFlow enables the quick and accurate completion of a range of sequence analysis tasks for datasets containing thousands of event types and millions of individual events.

As datasets grow and analytic algorithms become more complex, the typical workflow of analysts launching an analytic, waiting for it to complete, inspecting the results, and then re-Iaunching the computation with adjusted parameters is not realistic for many real-world tasks. This paper presents an alternative workflow, progressive visual analytics, which enables an analyst to inspect partial results of an algorithm as they become available and interact with the algorithm to prioritize subspaces of interest. Progressive visual analytics depends on adapting analytical algorithms to produce meaningful partial results and enable analyst intervention without sacrificing computational speed. The paradigm also depends on adapting information visualization techniques to incorporate the constantly refining results without overwhelming analysts and provide interactions to support an analyst directing the analytic. The contributions of this paper include: a description of the progressive visual analytics paradigm; design goals for both the algorithms and visualizations in progressive visual analytics systems; an example progressive visual analytics system (Progressive Insights) for analyzing common patterns in a collection of event sequences; and an evaluation of Progressive Insights and the progressive visual analytics paradigm by clinical researchers analyzing electronic medical records.

Event sequence data is common in many domains, ranging from electronic medical records (EMRs) to sports events. Moreover, such sequences often result in measurable outcomes (e.g., life or death, win or loss). Collections of event sequences can be aggregated together to form event progression pathways. These pathways can then be connected with outcomes to model how alternative chains of events may lead to different results. This paper describes the Outflow visualization technique, designed to (1) aggregate multiple event sequences, (2) display the aggregate pathways through different event states with timing and cardinality, (3) summarize the pathways' corresponding outcomes, and (4) allow users to explore external factors that correlate with specific pathway state transitions. Results from a user study with twelve participants show that users were able to learn how to use Outflow easily with limited training and perform a range of tasks both accurately and rapidly.

Documents in rich text corpora often contain multiple facets of information. For example, an article from a medical document collection might consist of multifaceted information about symptoms, treatments, causes, diagnoses, prognoses, and preventions. Thus, documents in the collection may have different relations across each of these various facets. Topic analysis and exploration for such multi-relational corpora is a challenging visual analytic task. This paper presents Solar Map, a multifaceted visual analytic technique for visually exploring topics in multi-relational data. Solar Map simultaneously visualizes the topic distribution of the underlying entities from one facet together with keyword distributions that convey the semantic definition of each cluster along a secondary facet. Solar Map combines several visual techniques including 1) topic contour clusters and interactive multifaceted keyword topic rings, 2) a global layout optimization algorithm that aligns each topic cluster with its corresponding keywords, and 3) 2) an optimal temporal network segmentation and layout method that renders temporal evolution of clusters. Finally, the paper concludes with two case studies and quantitative user evaluation which show the power of the Solar Map technique.

Clustering as a fundamental data analysis technique has been widely used in many analytic applications. However, it is often difficult for users to understand and evaluate multidimensional clustering results, especially the quality of clusters and their semantics. For large and complex data, high-level statistical information about the clusters is often needed for users to evaluate cluster quality while a detailed display of multidimensional attributes of the data is necessary to understand the meaning of clusters. In this paper, we introduce DICON, an icon-based cluster visualization that embeds statistical information into a multi-attribute display to facilitate cluster interpretation, evaluation, and comparison. We design a treemap-like icon to represent a multidimensional cluster, and the quality of the cluster can be conveniently evaluated with the embedded statistical information. We further develop a novel layout algorithm which can generate similar icons for similar clusters, making comparisons of clusters easier. User interaction and clutter reduction are integrated into the system to help users more effectively analyze and refine clustering results for large datasets. We demonstrate the power of DICON through a user study and a case study in the healthcare domain. Our evaluation shows the benefits of the technique, especially in support of complex multidimensional cluster analysis.

Documents in rich text corpora usually contain multiple facets of information. For example, an article about a specific disease often consists of different facets such as symptom, treatment, cause, diagnosis, prognosis, and prevention. Thus, documents may have different relations based on different facets. Powerful search tools have been developed to help users locate lists of individual documents that are most related to specific keywords. However, there is a lack of effective analysis tools that reveal the multifaceted relations of documents within or cross the document clusters. In this paper, we present FacetAtlas, a multifaceted visualization technique for visually analyzing rich text corpora. FacetAtlas combines search technology with advanced visual analytical tools to convey both global and local patterns simultaneously. We describe several unique aspects of FacetAtlas, including (1) node cliques and multifaceted edges, (2) an optimized density map, and (3) automated opacity pattern enhancement for highlighting visual patterns, (4) interactive context switch between facets. In addition, we demonstrate the power of FacetAtlas through a case study that targets patient education in the health care domain. Our evaluation shows the benefits of this work, especially in support of complex multifaceted data analysis.

Sensemaking tasks require that users gather and comprehend information from many sources to answer complex questions. Such tasks are common and include, for example, researching vacation destinations or performing market analysis. In this paper, we present an algorithm and interface which provides context-based page unit recommendation to assist in connection discovery during sensemaking tasks. We exploit the natural note-taking activity common to sensemaking behavior as the basis for a task-specific context model. Our algorithm then dynamically analyzes each web page visited by a user to determine which page units are most relevant to the user's task. We present the details of our recommendation algorithm, describe the user interface, and present the results of a user study which show the effectiveness of our approach.

We present a novel approach to visualization recommendation that monitors user behavior for implicit signals of user intent to provide more effective recommendation. This is in contrast to previous approaches which are either insensitive to user intent or require explicit, user specified task information. Our approach, called Behavior-Driven Visualization Recommendation (BDVR), consists of two distinct phases: (1) pattern detection, and (2) visualization recommendation. In the first phase, user behavior is analyzed dynamically to find semantically meaningful interaction patterns using a library of pattern definitions developed through observations of real-world visual analytic activity. In the second phase, our BDVR algorithm uses the detected patterns to infer a user's intended visual task. It then automatically suggests alternative visualizations that support the inferred visual task more directly than the user's current visualization. We present the details of BDVR and describe its implementation within our lab's prototype visual analysis system. We also present study results that demonstrate that our approach shortens task completion time and reduces error rates when compared to behavior-agnostic recommendation.

Sensemaking tasks require users to perform complex research behaviors to gather and comprehend information from many sources. Such tasks are common and include, for example, researching vacation destinations or deciding how to invest. In this paper, we present an algorithm and interface that provides context-based page unit recommendation to assist in connection discovery during sensemaking tasks. We exploit the natural note-taking activity common to sensemaking behavior as the basis for a task-specific context model. Each web page visited by a user is dynamically analyzed to determine the most relevant content fragments which are then recommended to the user. Our initial evaluations indicate that our approach improves user performance.

The World Wide Web is a powerful platform for a wide range of information tasks. Dramatic advances in technology, such as improved search capabilities and the AJAX application model, have enabled entirely new web-based applications and usage patterns, making many tasks easier to perform than ever before. However, few tools have been developed to assist with sensemaking tasks: complex research behaviors in which users gather and comprehend information from many sources to answer potentially vague, non-procedural questions. Sensemaking tasks are common and include, for example, researching vacation destinations or deciding how to invest. This paper presents the ScratchPad, an extension to the standard browser interface that is designed to capture, organize, and exploit the information discovered while performing a sensemaking task.

Streaming of linear media objects, such as audio and video, has become ubiquitous on today's Internet. Large groups of users regularly tune in to a wide variety of online programming, including radio shows, sports events, and news coverage. However, non-linear media objects, such as large 3D computer graphics models and visualization databases, have proven more difficult to stream due to their interactive nature. This paper presents Channel Set Adaptation (CSA), a framework that allows for the efficient streaming of non-linear datasets to large user groups. CSA allows individual clients to request custom data flows for interactive applications using standard broadcast or multicast join and leave operations. CSA scales to support very large user groups while continuing to provide interactive data access to independently operating clients. We discuss a motivating sample application for digital museums and present results from an experimental evaluation of CSA's performance.

No abstract available.

Article Share on A general framework for multidimensional adaptation Authors: David Gotz University of North Carolina at Chapel Hill, Chapel Hill, NC University of North Carolina at Chapel Hill, Chapel Hill, NCView Profile , Ketan Mayer-Patel University of North Carolina at Chapel Hill, Chapel Hill, NC University of North Carolina at Chapel Hill, Chapel Hill, NCView Profile Authors Info & Claims MULTIMEDIA '04: Proceedings of the 12th annual ACM international conference on MultimediaOctober 2004 Pages 612–619https://doi.org/10.1145/1027527.1027671Online:10 October 2004Publication History 14citation423DownloadsMetricsTotal Citations14Total Downloads423Last 12 Months4Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

We present a new representation for image-based interactive walk-throughs. The target applications reconstruct a scene from novel viewpoints using samples from a spatial image dataset collected from a plane at eye-level. These datasets consist of pose augmented 2D images and often have a very large number of samples. Our representation exploits spatial coherence and rearranges the input samples as epipolar images. The base unit corresponds to a column of the original image that can be individually addressed and accessed. The overall representation, IRW, supports incremental updates, efficient encoding, scalable performance, and selective inclusion used by different reconstruction algorithms. We demonstrate the performance of our representation on a synthetic as well as a real-world environment.

This paper presents PixelFlex - a spatially reconfigurable multi-projector display system. The PixelFlex system is composed of ceiling-mounted projectors, each with computer-controlled pan, tilt, zoom and focus; and a camera for closed-loop calibration. Working collectively, these controllable projectors function as a single logical display capable of being easily modified into a variety of spatial formats of differing pixel density, size and shape. New layouts are automatically calibrated within minutes to generate the accurate warping and blending functions needed to produce seamless imagery across planar display surfaces, thus giving the user the flexibility to quickly create, save and restore multiple screen configurations. Overall, PixelFlex provides a new level of automatic reconfigurability and usage, departing from the static, one-size-fits-all design of traditional large-format displays. As a front-projection system, PixelFlex can be installed in most environments with space constraints and requires little or no post-installation mechanical maintenance because of the closed-loop calibration.