This article reports on an in-depth study that investigates barriers to network exploration with visualizations. Network visualization tools are becoming increasingly popular, but little is known about how analysts plan and engage in the visual exploration of network data-which exploration strategies they employ, and how they prepare their data, define questions, and decide on visual mappings. Our study involved a series of workshops, interaction logging, and observations from a 6-week network exploration course. Our findings shed light on the stages that define analysts' approaches to network visualization and barriers experienced by some analysts during their network visualization processes. These barriers mainly appear before using a specific tool and include defining exploration goals, identifying relevant network structures and abstractions, or creating appropriate visual mappings for their network data. Our findings inform future work in visualization education and analyst-centered network visualization tool design.

Abstract Hypergraphs are a generalization of graphs in which edges (hyperedges) can connect more than two vertices—as opposed to ordinary graphs where edges involve only two vertices. Hypergraphs are a fairly common data structure but there is little consensus on how to visualize them. To optimize a hypergraph drawing for readability, we need a layout algorithm. Common graph layout algorithms only consider ordinary graphs and do not take hyperedges into account. We focus on layered hypergraphs, a particular class of hypergraphs that, like layered graphs, assigns every vertex to a layer, and the vertices in a layer are drawn aligned on a linear axis with the axes arranged in parallel. In this paper, we propose a general method to apply layered graph layout algorithms to layered hypergraphs. We introduce six different transformations for layered hypergraphs. The choice of transformation affects the subsequent graph layout algorithm in terms of computational performance and readability of the results. Thus, we perform a comparative evaluation of these transformations in terms of number of crossings, edge length, and impact on performance. We also provide two case studies showing how our transformations can be applied to real‐life use cases. A copy of this paper with all appendices and supplemental material is available at osf.io/grvwu.

We propose a new approach-called PK-clustering-to help social scientists create meaningful clusters in social networks. Many clustering algorithms exist but most social scientists find them difficult to understand, and tools do not provide any guidance to choose algorithms, or to evaluate results taking into account the prior knowledge of the scientists. Our work introduces a new clustering approach and a visual analytics user interface that address this issue. It is based on a process that 1) captures the prior knowledge of the scientists as a set of incomplete clusters, 2) runs multiple clustering algorithms (similarly to clustering ensemble methods), 3) visualizes the results of all the algorithms ranked and summarized by how well each algorithm matches the prior knowledge, 4) evaluates the consensus between user-selected algorithms and 5) allows users to review details and iteratively update the acquired knowledge. We describe our approach using an initial functional prototype, then provide two examples of use and early feedback from social scientists. We believe our clustering approach offers a novel constructive method to iteratively build knowledge while avoiding being overly influenced by the results of often randomly selected black-box clustering algorithms.

Presents the recipient of the 2020 Visualization Career Award.

Parallel Aggregated Ordered Hypergraph(PAOH) is a novel technique to visualize dynamic hypergraphs. Hypergraphs are a generalization of graphs where edges can connect several vertices. Hypergraphs can be used to model networks of business partners or co-authorship networks with multiple authors per article. A dynamic hypergraph evolves over discrete time slots. PAOH represents vertices as parallel horizontal bars and hyperedges as vertical lines, using dots to depict the connections to one or more vertices. We describe a prototype implementation of Parallel Aggregated Ordered Hypergraph, report on a usability study with 9 participants analyzing publication data, and summarize the improvements made. Two case studies and several examples are provided. We believe that PAOH is the first technique to provide a highly readable representation of dynamic hypergraphs. It is easy to learn and well suited for medium size dynamic hypergraphs (50-500 vertices) such as those commonly generated by digital humanities projects-our driving application domain.

Information visualization designers strive to design data displays that allow for efficient exploration, analysis, and communication of patterns in data, leading to informed decisions. Unfortunately, human judgment and decision making are imperfect and often plagued by cognitive biases. There is limited empirical research documenting how these biases affect visual data analysis activities. Existing taxonomies are organized by cognitive theories that are hard to associate with visualization tasks. Based on a survey of the literature we propose a task-based taxonomy of 154 cognitive biases organized in 7 main categories. We hope the taxonomy will help visualization researchers relate their design to the corresponding possible biases, and lead to new research that detects and addresses biased judgment and decision making in data visualization.

Objective: Most electronic health records display historical medication information only in a data table or clinician notes. We designed a medication timeline visualization intended to improve ease of use, speed, and accuracy in the ambulatory care of chronic disease. Materials and Methods: We identified information needs for understanding a patient medication history, then applied human factors and interaction design principles to support that process. After research and analysis of existing medication lists and timelines to guide initial requirements, we hosted design workshops with multidisciplinary stakeholders to expand on our initial concepts. Subsequent core team meetings used an iterative user-centered design approach to refine our prototype. Finally, a small pilot evaluation of the design was conducted with practicing physicians. Results: We propose an open-source online prototype that incorporates user feedback from initial design workshops, and broad multidisciplinary audience feedback. We describe the applicable design principles associated with each of the prototype's key features. A pilot evaluation of the design showed improved physician performance in 5 common medication-related tasks, compared to tabular presentation of the same information. Discussion: There is industry interest in developing medication timelines based on the example prototype concepts. An open, standards-based technology platform could enable developers to create a medication timeline that could be deployable across any compatible health IT application. Conclusion: The design goal was to improve physician understanding of a patient's complex medication history, using a medication timeline visualization. Such a design could reduce temporal and cognitive load on physicians for improved and safer care.

People use recommender systems to improve their decisions; for example, item recommender systems help them find films to watch or books to buy. Despite the ubiquity of item recommender systems, they can be improved by giving users greater transparency and control. This article develops and assesses interactive strategies for transparency and control, as applied to event sequence recommender systems, which provide guidance in critical life choices such as medical treatments, careers decisions, and educational course selections. This article’s main contribution is the use of both record attributes and temporal event information as features to identify similar records and provide appropriate recommendations. While traditional item recommendations are based on choices by people with similar attributes, such as those who looked at this product or watched this movie, our event sequence recommendation approach allows users to select records that share similar attribute values and start with a similar event sequence. Then users see how different choices of actions and the orders and times between them might lead to users’ desired outcomes. This paper applies a visual analytics approach to present and explain recommendations of event sequences. It presents a workflow for event sequence recommendation that is implemented in EventAction and reports on three case studies in two domains to illustrate the use of generating event sequence recommendations based on personal histories. It also offers design guidelines for the construction of user interfaces for event sequence recommendation and discusses ethical issues in dealing with personal histories. A demo video of EventAction is available at https://hcil.umd.edu/eventaction.

Recommendation applications can guide users in making important life choices by referring to the activities of similar peers. For example, students making academic plans may learn from the data of similar students, while patients and their physicians may explore data from similar patients to select the best treatment. Selecting an appropriate peer group has a strong impact on the value of the guidance that can result from analyzing the peer group data. In this article, we describe a visual interface that helps users review the similarity and differences between a seed record and a group of similar records and refine the selection. We introduce the LikeMeDonuts, Ranking Glyph, and History Heatmap visualizations. The interface was refined through three rounds of formative usability evaluation with 12 target users, and its usefulness was evaluated by a case study with a student review manager using real student data. We describe three analytic workflows observed during use and summarize how users’ input shaped the final design.

Wrong-patient errors from inadvertent menu selections while using computerized provider order entry (CPOE) systems could have fatal consequences. This study investigated whether the manipulation of CPOE interface design could improve healthcare providers’ ability to recognize patient selection errors and also decrease the time to error recognition. Using a 2 × 2 design, 120 participants were randomly assigned to one of four groups, interacting with different versions of a simulated CPOE: (1) control – standard version; (2) highlighted selection – the selected patient row was highlighted for 2 s, by blanking the rest of the screen; (3) photo – photographs of patients’ faces were displayed in all screens; (4) combined – with photo and highlighted selection. Each participant navigated through five order scenarios. On the last scenario, an error was simulated by directing the participant to a wrong patient. Recognition rates of the wrong-patient error and times to error recognition were significantly improved for the highlighted selection, photo, and combined groups, relative to the control group. These results suggest that the addition of patient photos and highlighted selection could substantially reduce errors in CPOE systems and other applications.

People often seek examples of similar individuals to guide their own life choices. For example, students making academic plans refer to friends; patients refer to acquaintances with similar conditions, physicians mention past cases seen in their practice. How would they want to search for similar people in databases? We discuss the challenge of finding similar people to guide life choices and report on a need analysis based on 13 interviews. Our PeerFinder prototype enables users to find records that are similar to a seed record, using both record attributes and temporal events found in the records. A user study with 18 participants and four experts shows that users are more engaged and more confident about the value of the results to provide useful evidence to guide life choices when provided with more control over the search process and more context for the results, even at the cost of added complexity.

The growing volume and variety of data presents both opportunities and challenges for visual analytics. Addressing these challenges is needed for big data to provide valuable insights and novel solutions for business, security, social media, and healthcare. In the case of temporal event sequence analytics it is the number of events in the data and variety of temporal sequence patterns that challenges users of visual analytic tools. This paper describes 15 strategies for sharpening analytic focus that analysts can use to reduce the data volume and pattern variety. Four groups of strategies are proposed: (1) extraction strategies, (2) temporal folding, (3) pattern simplification strategies, and (4) iterative strategies. For each strategy, we provide examples of the use and impact of this strategy on volume and/or variety. Examples are selected from 20 case studies gathered from either our own work, the literature, or based on email interviews with individuals who conducted the analyses and developers who observed analysts using the tools. Finally, we discuss how these strategies might be combined and report on the feedback from 10 senior event sequence analysts.

Animations are commonplace in today's user interfaces. From bouncing icons that catch attention, to transitions helping with orientation, to tutorials, animations can serve numerous purposes. We revisit Baecker and Small's pioneering work Animation at the Interface, 25 years later. We reviewed academic publications and commercial systems, and interviewed 20 professionals of various backgrounds. Our insights led to an expanded set of roles played by animation in interfaces today for keeping in context, teaching, improving user experience, data encoding and visual discourse. We illustrate each role with examples from practice and research, discuss evaluation methods and point to opportunities for future research. This expanded description of roles aims at inspiring the HCI research community to find novel uses of animation, guide them towards evaluation and spark further research.

Medical treatments carry unique benefits and risks which patients must understand in order to decide which option is best for them. Prior research has demonstrated that patients are ill-equipped to understand the statistical information presented to them through standard decision aids. We describe a prototype decision aid, TreatmentExplorer, which supports patients’ needs by presenting treatment outcome, onset of symptoms and treatment side effects using a novel graphic representation with staged animation and text-only narration. Our prototype also illustrates the use of a data-driven personalization approach by using electronic health record data. We report on expert reviews, a pilot study (⁠|$n=24$|⁠) and a main study (⁠|$n=42$|⁠), which characterize the benefits of TreatmentExplorer over a text-only decision aid as well as a version without staged animation, and conclude with guidelines for designers.

Cohort comparison studies have traditionally been hypothesis driven and conducted in carefully controlled environments (such as clinical trials). Given two groups of event sequence data, researchers test a single hypothesis (e.g., does the group taking Medication A exhibit more deaths than the group taking Medication B?). Recently, however, researchers have been moving toward more exploratory methods of retrospective analysis with existing data. In this article, we begin by showing that the task of cohort comparison is specific enough to support automatic computation against a bounded set of potential questions and objectives, a method that we refer to as High-Volume Hypothesis Testing (HVHT). From this starting point, we demonstrate that the diversity of these objectives, both across and within different domains, as well as the inherent complexities of real-world datasets, still requires human involvement to determine meaningful insights. We explore how visualization and interaction better support the task of exploratory data analysis and the understanding of HVHT results (how significant they are, why they are meaningful, and whether the entire dataset has been exhaustively explored). Through interviews and case studies with domain experts, we iteratively design and implement visualization and interaction techniques in a visual analytics tool, CoCo. As a result of our evaluation, we propose six design guidelines for enabling users to explore large result sets of HVHT systematically and flexibly in order to glean meaningful insights more quickly. Finally, we illustrate the utility of this method with three case studies in the medical domain.

Finding the differences and similarities between two datasets is a common analytics task. With temporal event sequence data, this task is complex because of the many ways single events and event sequences can differ between the two datasets (or cohorts) of records: the structure of the event sequences (e.g., event order, co-occurring events, or event frequencies), the attributes of events and records (e.g., patient gender), or metrics about the timestamps themselves (e.g., event duration). In exploratory analyses, running statistical tests to cover all cases is time-consuming and determining which results are significant becomes cumbersome. Current analytics tools for comparing groups of event sequences emphasize a purely statistical or purely visual approach for comparison. This paper presents a taxonomy of metrics for comparing cohorts of temporal event sequences, showing that the problem-space is bounded. We also present a visual analytics tool, CoCo (for "Cohort Comparison"), which implements balanced integration of automated statistics with an intelligent user interface to guide users to significant, distinguishing features between the cohorts. Lastly, we describe two early case studies: the first with a research team studying medical team performance in the emergency department and the second with pharmacy researchers.

OBJECTIVE: The primary objective was to evaluate time, number of interface actions, and accuracy on medication reconciliation tasks using a novel user interface (Twinlist, which lays out the medications in five columns based on similarity and uses animation to introduce the grouping - www.cs.umd.edu/hcil/sharp/twinlist) compared to a Control interface (where medications are presented side by side in two columns). A secondary objective was to assess participant agreement with statements regarding clarity and utility and to elicit comparisons. MATERIAL AND METHODS: A 1 × 2 within-subjects experimental design was used with interface (Twinlist or Control) as an independent variable; time, number of clicks, scrolls, and errors were used as dependent variables. Participants were practicing medical providers with experience performing medication reconciliation but no experience with Twinlist. They reconciled two cases in each interface (in a counterbalanced order), then provided feedback on the design of the interface. RESULTS: Twenty medical providers participated in the study for a total of 80 trials. The trials using Twinlist were statistically significantly faster (18%), with fewer clicks (40%) and scrolls (60%). Serious errors were noted 12 and 31 times in Twinlist and Control trials, respectively. DISCUSSION: Trials using Twinlist were faster and more accurate. Subjectively, participants rated Twinlist more favorably than Control. They valued the novel layout of the drugs, but indicated that the included animation would be valuable for novices, but not necessarily for advanced users. Additional feedback from participants provides guidance for further development and clinical implementations. CONCLUSIONS: Cognitive support of medication reconciliation through interface design can significantly improve performance and safety.

Visualization has proven to be a useful tool for understanding network structures. Yet the dynamic nature of social media networks requires powerful visualization techniques that go beyond static network diagrams. To provide strong temporal network visualization tools, designers need to understand what tasks the users have to accomplish. This paper describes a taxonomy of temporal network visualization tasks. We identify the 1) entities, 2) properties, and 3) temporal features, which were extracted by surveying 53 existing temporal network visualization systems. By building and examining the task taxonomy, we report which tasks are well covered by existing systems and make suggestions for designing future visualization tools. The feedback from 12 network analysts helped refine the taxonomy.

In our burgeoning world of ubiquitous sensors and affordable data storage, records of timestamped events are being produced across nearly every domain of personal and professional computing. The resulting data surge has created an overarching need to search these records for meaningful patterns of events. This paper reports on a two-part user study, as well as a series of early tests and interviews with clinical researchers, that informed the development of two temporal query interfaces: a basic, menu-based interface and an advanced, graphic-based interface. While the scope of temporal query is very broad, this work focuses on two particularly complex and critical facets of temporal event sequences: intervals (events with both a start time and an end time), and the absence of an event. We describe how users encounter a common set of difficulties when specifying such queries, and propose solutions to help overcome them. Finally, we report on two case studies with epidemiologists at the US Army Pharmacovigilance Center, illustrating how both query interfaces were used to study patterns of drug use.

Visual overviews of tables of numerical and categorical data have been proposed for tables with a single value per cell. This article addresses the problem of exploring tables with columns that consist of cells that are distributions, for example, the distributions of movie ratings or trust ratings in recommender systems, age distributions in demographic data, usage distributions in logs of telephone calls, and so on. This article expands on heatmap approaches and proposes a novel way of displaying and interacting with distribution data. The usability study demonstrates the benefits of the heatmap interface in providing an overview of the data and facilitating the discovery of interesting clusters, patterns, outliers and relationships between columns.

To analyze data such as the US Federal Budget or characteristics of the student population of a University it is common to look for changes over time. This task can be made easier and more fruitful if the analysis is performed by grouping by attributes, such as by Agencies, Bureaus and Accounts for the Budget, or Ethnicity, Gender and Major in a University. We present TreeVersity2, a web based interactive data visualization tool that allows users to analyze change in datasets by creating dynamic hierarchies based on the data attributes. TreeVersity2 introduces a novel space filling visualization (StemView) to represent change in trees at multiple levels--not just at the leaf level. With this visualization users can explore absolute and relative changes, created and removed nodes, and each node's actual values, while maintaining the context of the tree. In addition, TreeVersity2 provides overviews of change over the entire time period, and a reporting tool that lists outliers in textual form, which helps users identify the major changes in the data without having to manually setup filters. We validated TreeVersity2 with 12 case studies with organizations as diverse as the National Cancer Institute, Federal Drug Administration, Department of Transportation, Office of the Bursar of the University of Maryland, or eBay. Our case studies demonstrated that TreeVersity2 is flexible enough to be used in different domains and provide useful insights for the data owners. A TreeVersity2 demo can be found at https://treeversity.cattlab.umd.edu.

Electronic Health Records (EHRs) have emerged as a cost-effective data source for conducting medical research. The difficulty in using EHRs for research purposes, however, is that both patient selection and record analysis must be conducted across very large, and typically very noisy datasets. Our previous work introduced EventFlow, a visualization tool that transforms an entire dataset of temporal event records into an aggregated display, allowing researchers to analyze population-level patterns and trends. As datasets become larger and more varied, however, it becomes increasingly difficult to provide a succinct, summarizing display. This paper presents a series of user-driven data simplifications that allow researchers to pare event records down to their core elements. Furthermore, we present a novel metric for measuring visual complexity, and a language for codifying disjoint strategies into an overarching simplification framework. These simplifications were used by real-world researchers to gain new and valuable insights from initially overwhelming datasets.

Specifying event sequence queries is challenging even for skilled computer professionals familiar with SQL. Most graphical user interfaces for database search use an exact match approach, which is often effective, but near misses may also be of interest. We describe a new similarity search interface, in which users specify a query by simply placing events on a blank timeline and retrieve a similarity-ranked list of results. Behind this user interface is a new similarity measure for event sequences which the users can customize by four decision criteria, enabling them to adjust the impact of missing, extra, or swapped events or the impact of time shifts. We describe a use case with Electronic Health Records based on our ongoing collaboration with hospital physicians. A controlled experiment with 18 participants compared exact match and similarity search interfaces. We report on the advantages and disadvantages of each interface and suggest a hybrid interface combining the best of both.

We take a new, scenario-based look at evaluation in information visualization. Our seven scenarios, evaluating visual data analysis and reasoning, evaluating user performance, evaluating user experience, evaluating environments and work practices, evaluating communication through visualization, evaluating visualization algorithms, and evaluating collaborative data analysis were derived through an extensive literature review of over 800 visualization publications. These scenarios distinguish different study goals and types of research questions and are illustrated through example studies. Through this broad survey and the distillation of these scenarios, we make two contributions. One, we encapsulate the current practices in the information visualization research community and, two, we provide a different approach to reaching decisions about what might be the most effective evaluation of a given information visualization. Scenarios can be used to choose appropriate research questions and goals and the provided examples can be consulted for guidance on how to design one's own study.

Event sequence analysis is an important task in many domains: medical researchers may study the patterns of transfers within the hospital for quality control; transportation experts may study accident response logs to identify best practices. In many cases they deal with thousands of records. While previous research has focused on searching and browsing, overview tasks are often overlooked. We introduce a novel interactive visual overview of event sequences called \emph{LifeFlow}. LifeFlow is scalable, can summarize all possible sequences, and represents the temporal spacing of the events within sequences. Two case studies with healthcare and transportation domain experts are presented to illustrate the usefulness of LifeFlow. A user study with ten participants confirmed that after 15 minutes of training novice users were able to rapidly answer questions about the prevalence and temporal characteristics of sequences, find anomalies, and gain significant insight from the data.

Traditional network analysis tools support analysts in studying a single network. ManyNets offers these analysts a powerful new approach that enables them to work on multiple networks simultaneously. Several thousand networks can be presented as rows in a tabular visualization, and then inspected, sorted and filtered according to their attributes. The networks to be displayed can be obtained by subdivision of larger networks. Examples of meaningful subdivisions used by analysts include ego networks, community extraction, and time-based slices. Cell visualizations and interactive column overviews allow analysts to assess the distribution of attributes within particular sets of networks. Details, such as traditional node-link diagrams, are available on demand. We describe a case study analyzing a social network geared towards film recommendations by means of decomposition. A small usability study provides feedback on the use of the interface on a set of tasks issued from the case study.

Abstract Interactive information visualization systems rely on widgets to allow users to interact with the data and modify the representation. We define interactive legends as a class of controls combining the visual representation of static legends and interaction mechanisms of widgets. As interactive legends start to appear in popular websites, we categorize their designs for common data types and evaluate their effectiveness compare to standard widgets. Results suggest that 1) interactive legends can lead to faster perception of the mapping between data values and visual encodings and 2) interaction time is affected differently depending on the data type. Additionally, our study indicates superiority both in terms of perception and interaction of ordinal controls over numerical ones. Numerical techniques are mostly used in today's systems. By providing solutions to allowing users to modify ranges interactively, we believe that interactive legends make it possible to increase the use of ordinal techniques for visual exploration.

Abstract Informal communications media pose new challenges for information‐systems design, but the nature of informal interaction offers new opportunities as well. This paper describes NetLens‐E‐mail, a system designed to support exploration of the content‐actor network in large e‐mail collections. Unique features of NetLens‐E‐mail include close coupling of orientation, specification, restriction, and expansion, and introduction and incorporation of a novel capability for iterative projection between content and actor networks within the same collection. Scenarios are presented to illustrate the intended employment of NetLens‐E‐mail, and design walkthroughs with two domain experts provide an initial basis for assessment of the suitability of the design by scholars and analysts.

When analyzing thousands of event histories, analysts often want to see the events as an aggregate to detect insights and generate new hypotheses about the data. An analysis tool must emphasize both the prevalence and the temporal ordering of these events. Additionally, the analysis tool must also support flexible comparisons to allow analysts to gather visual evidence. In a previous work, we introduced align, rank, and filter (ARF) to accentuate temporal ordering. In this paper, we present temporal summaries, an interactive visualization technique that highlights the prevalence of event occurrences. Temporal summaries dynamically aggregate events in multiple granularities (year, month, week, day, hour, etc.) for the purpose of spotting trends over time and comparing several groups of records. They provide affordances for analysts to perform temporal range filters. We demonstrate the applicability of this approach in two extensive case studies with analysts who applied temporal summaries to search, filter, and look for patterns in electronic health records and academic records.

Electronic Health Records (EHRs) and other temporal databases contain hidden patterns that reveal important cause-and-effect phenomena. Finding these patterns is a challenge when using traditional query languages and tabular displays. We present an interactive visual tool that complements query formulation by providing operations to align, rank and filter the results, and to visualize estimates of the intervals of validity of the data. Display of patient histories aligned on sentinel events (such as a first heart attack) enables users to spot precursor, co-occurring, and aftereffect events. A controlled study demonstrates the benefits of providing alignment (with a 61% speed improvement for complex tasks). A qualitative study and interviews with medical professionals demonstrates that the interface can be learned quickly and seems to address their needs.

We describe the development and evaluation of a tool, iSonic, to assist users with visual impairment in exploring georeferenced data using coordinated maps and tables, augmented with nontextual sounds and speech output. Our in-depth case studies with 7 blind users during 42 hours of data collection, showed that iSonic enabled them to find facts and discover trends in georeferenced data, even in unfamiliar geographical contexts, without special devices. Our design was guided by an Action-by-Design-Component (ADC) framework, which was also applied to scatterplots to demonstrate its generalizability. Video and download is available at www.cs.umd.edu/hcil/iSonic/.

Information Visualization (InfoVis) is now an accepted and growing field but questions remain about the best uses for and the maturity of novel visualizations. Usability studies and controlled experiments are helpful but generalization is difficult. We believe that the systematic development of benchmarks will facilitate the comparison of techniques and help identify their strengths under different conditions. We were involved in the organization and management of three information visualization contests for the 2003, 2004 and 2005 IEEE InfoVis Symposia, which requested teams to report on insights gained while exploring data. We give a summary of the state of the art of evaluation in information visualization, describe the three contests, summarize their results, discuss outcomes and lessons learned, and conjecture the future of visualization contests. All materials produced by the contests are archived in the InfoVis Benchmark Repository.

This paper addresses the problem of making text mining results more comprehensible to humanities scholars, journalists, intelligence analysts, and other researchers, in order to support the analysis of text collections. Our system, FeatureLens1, visualizes a text collection at several levels of granularity and enables users to explore interesting text patterns. The current implementation focuses on frequent itemsets of n-grams, as they capture the repetition of exact or similar expressions in the collection. Users can find meaningful co-occurrences of text patterns by visualizing them within and across documents in the collection. This also permits users to identify the temporal evolution of usage such as increasing, decreasing or sudden appearance of text patterns. The interface could be used to explore other text features as well. Initial studies suggest that FeatureLens helped a literary scholar and 8 users generate new hypotheses and interesting insights using 2 text collections.

Time-series forecasting has a large number of applications. Users with a partial time series for auctions, new stock offerings, or industrial processes desire estimates of the future behavior. We present a data driven forecasting method and interface called similarity-based forecasting (SBF). A pattern matching search in an historical time series dataset produces a subset of curves similar to the partial time series. The forecast is displayed graphically as a river plot showing statistical information about the SBF subset. A forecasting preview interface allows users to interactively explore alternative pattern matching parameters and see multiple forecasts simultaneously. User testing with 8 users demonstrated advantages and led to improvements.

Round numbers call for reflection and celebration.In 2007, SIGCHI celebrated its 25th anniversary, and Ben Shneiderman celebrated his 60th birthday (August 21).To follow the agreeable tradition of celebrating anniversaries in our field, the editors of International Journal of Human-Computer Interaction (IJHCI) have asked us to assemble a special issue-a Festschrift-to honor Dr. Ben Shneiderman's accomplishments, while looking forward to the next phases for human-computer interaction (HCI) and for him.By bringing scientific methods to the study of human use of computers, Ben Shneiderman has played a key role in developing a new academic discipline that promotes more usable information and computing technologies-generally called HCI.His early influential work helped foster graphical user interfaces, the World Wide Web, information visualization, and touch screen designs for portable devices.His projects for the U.S. Library of Congress, National Library of Medicine, NASA, Census Bureau, and so on, and consultations for corporations have Each paper was read by four to six reviewers, some reading multiple times.We thank all the anonymous reviewers but also the many others who helped us during the preparation

We describe the design and use of a system facilitating the sharing of calendar information between remotely located, multi-generational family members. Most previous work in this area involves software enabling younger family members to monitor their parents. We have found, however, that older adults are equally if not more interested in the activities of younger family members. The major obstacle preventing them from participating in information sharing is the technology itself. Therefore, we developed a multi-layered interface approach that offers simple interaction to older users. In our system, users can choose to enter information into a computerized calendar or write it by hand on digital paper calendars. All of the information is automatically shared among everyone in the distributed family. By making the interface more accessible to older users, we promote symmetrical sharing of information among both older and younger family members. We present our participatory design process, describe the user interface, and report on an exploratory field study in three households of an extended family.

Despite extensive research, it is still difficult to produce effective interactive layouts for large graphs. Dense layout and occlusion make food webs, ontologies, and social networks difficult to understand and interact with. We propose a new interactive Visual Analytics component called TreePlus that is based on a tree-style layout. TreePlus reveals the missing graph structure with visualization and interaction while maintaining good readability. To support exploration of the local structure of the graph and gathering of information from the extensive reading of labels, we use a guiding metaphor of "Plant a seed and watch it grow." It allows users to start with a node and expand the graph as needed, which complements the classic overview techniques that can be effective at (but often limited to) revealing clusters. We describe our design goals, describe the interface, and report on a controlled user study with 28 participants comparing TreePlus with a traditional graph interface for six tasks. In general, the advantage of TreePlus over the traditional interface increased as the density of the displayed data increased. Participants also reported higher levels of confidence in their answers with TreePlus and most of them preferred TreePlus.

iSonic is an interactive sonification tool for vision impaired users to explore geo-referenced statistical data, such as population or crime rates by geographical regions. Users use a keyboard or a smooth surface touchpad to interact with coordinated map and table views of the data. The integrated use of musical sounds and speech allows users to grasp the overall data trends and to explore the data to get more details. Scenarios of use are described.

Although recorded demonstrations (screen capture animations with narration) have become a popular form of instruction for user interfaces, little work has been done to describe guidelines for their design. Based on our experience in several projects, we offer a starting set of guidelines for the design of visually appealing and cognitively effective recorded demonstrations. Technical guidelines encourage users to keep file sizes small, strive for universal usability, and ensure user control etc. and provide tips to achieve those goals. Content guidelines include: create short demonstrations that focus on tasks, highlight each step with auditory and visual cues, synchronize narration and animation carefully, and create demonstrations with a clear beginning, middle, and end.

As the field of information visualization matures, the tools and ideas described in our research publications are reaching users. The reports of usability studies and controlled experiments are helpful to understand the potential and limitations of our tools, but we need to consider other evaluation approaches that take into account the long exploratory nature of users tasks, the value of potential discoveries or the benefits of overall awareness. We need better metrics and benchmark repositories to compare tools, and we should also seek reports of successful adoption and demonstrated utility.

Treemaps are a visualization technique for presenting hierarchical information on two-dimensional displays. Prior implementations limit the visualization to pre-defined static hierarchies. Flexible hierarchy, a new capability of Treemap 4.0, enables users to define various hierarchies through dynamically selecting a series of data attributes so that they can discover patterns, clusters and outliers. This work describes the design and implementation issues of flexible hierarchy. It then reports on a usability study, which led to enhancements to the interface.

This paper describes a novel instantiation of a digital photo library in a public access system. It demonstrates how designers can utilize characteristics of a target user community (social constraints, trust, and a lack of anonymity) to provide capabilities that would be impractical in other types of public access systems. It also presents a compact set of design principles and guidelines for ensuring the immediate usability of public access information systems. These principles and guidelines were derived from our experience developing PhotoFinder Kiosk, a community photo library. Attendees of a major HCI conference (CHI 2001 Conference on Human Factors in Computing Systems) successfully used the tool to browse and annotate collections of photographs spanning 20 years of HCI-related conferences, producing a richly annotated photo history of the field of human-computer interaction. Observations and log data were used to evaluate the tool and develop the guidelines. They provide specific guidance for practitioners, as well as a useful framework for additional research in public access interfaces.

We describe a new method for use in the process of co-designing technologies with users called technology probes. Technology probes are simple, flexible, adaptable technologies with three interdisciplinary goals: the social science goal of understanding the needs and desires of users in a real-world setting, the engineering goal of field-testing the technology, and the design goal of inspiring users and researchers to think about new technologies. We present the results of designing and deploying two technology probes, the messageProbe and the videoProbe, with diverse families in France, Sweden, and the U.S. We conclude with our plans for creating new technologies for and with families based on our experiences.

Georges Grinstein Questions often asked when presenting some new model, new theory, new research or new visualization include: How useful or how usable is it? and Have you performed any tests? Visualization is an interface technology and as such includes not just software algorithms and techniques, but computer human interaction issues as well. This makes it draw from both areas, one appearing more focused on utility and the other on usability. One key step in the development of a new theory is the attempt to first solve a problem. That problem or question does not include a section; in some domain it may not even contain a utility one from most people's perspectives. We discuss both sides of the issue to clarify the role of each in the development of new visualization technologies. Position Statement Alfred Kobsa In the HCI literature one can find studies which conclude that ease of use is more important than usefulness (Hubona & Blanton 1996), that the opposite holds true (Liao and Landry 2000), and that ease of use is more important for females while the usefulness is more important for males (Yuen and Ma 2002). In this panel contribution, we will present several user studies with information visualization systems, ranging from lab experiments with closed questions to longitudinal adoption studies with administrative data analysts (Gonzales & Kobsa, 2003; Kobsa 2001, 2003; Mark et al. 2003). -------------------------------------------a e-mail: [email protected] e-mail: [email protected] e-mail: [email protected] e-mail [email protected] e-mail [email protected] Consistent with general HCI research, the results show that both factors are important in certain situations, but do not indicate a clear superiority of one factor over the other. Position Statement Catherine Plaisant Is an airplane a better vehicle than a Jaguar, a mountain bicycle or a kid scooter? It all depends of where you need to go, what your goal for the travel is, how old you are, what terrain you will encounter on the way, how long you can spend learning, and many other parameters. All those vehicles are fairly usable but they all require training except for adults using the scooter, and their utility varies enormously as a function of the task and the user. The average car drivers benefit from years of human factor engineering and a large amount of standardization, allowing them to switch from a pickup truck to a convertible in a snap. Similarly, the success of a visualization tool depends on how well it fits the needs of the users it attempts to serve, and the tasks they want to accomplish. If utility may come first for an expert tool (e.g. for discovery tasks requires days of data examination and manipulation), has to come first in public access information systems that requires immediate usability (e.g. interactive displays of census statistics) otherwise users will walk away frustrated. Usability design principles imply that designers and evaluators understand the needs of users to decide which one of the two utility or comes first, and to set levels of required utility and usability. Utility and are both attainable goals that make each other stronger. Like others, we at the University of Maryland have been developing visualization techniques and have struggled over the years to find the best way to evaluate their benefits. Many evaluations have been controlled experiments and we have found that the most useful evaluations were multi-faceted, including qualitative and quantitative measurements or performance, preference and learnability, and I will be show examples from our research. Often we also find that the observations gathered during the experiment can be as informative as the collected measurements. More recently we have been promoting the development of benchmark datasets and tasks that will allow better comparisons between tools and techniques. We have been involved in the 1st InfoVis contest, which calls for the submission of case studies of pairwise comparison of trees. Three pairs of datasets were provided: philogenies, classifications and file system usage data, 605 Proceedings of the 14th IEEE Visualization Conference (VIS’03) 0-7695-2030-8/03 $ 17.00 © 2003 IEEE and open ended tasks described. Accepted submissions will seed an online repository that can be enhanced over time with additional datasets, tasks, case studies and controlled experiment results. I will report on the results of the contest and reflect on how evaluation repository and benchmark datasets might help us understand how to judge the and utility of our tools. The contest is at: http://www.cs.umd.edu/hcil/iv03contest/ Position Statement Ben Shneiderman The answer to the question of or utility first depends on your definition of usability. For me, is more than the color of widgets and placement of text. Usability is about understanding, stating, and serving user needs. Since these needs are the requirements that shape the tool, they determine the utility. The design of excellent tools depends upon understanding how they will be used; therefore is a pre-requisite for successful utility. Position Statement John T. Stasko If the question is, Which comes first, utility or usability? my answer is yes. Both notions are vitally important in the development of information visualization techniques and systems, and they are just two sides of the same coin. When the field of information visualization formed and first grew, the computer graphics and visualization aspects dominated. More recently, an increasing emphasis on the HCI aspects of the field has emerged as we strive to better understand how people can truly benefit from our ideas. When an information visualization technique is implemented in a system, the component is crucial. Poor interface design can hamper adoption and cloud the utility benefits that may be possible. In information visualization, typically does not equate with the common notion of a system being easy to learn. Information visualization systems are complex and they often will be used extensively for long periods of time. Consequently, making a system efficient and natural to use, making functions and operations visible, and simply paying attention to the user interface are key components. This is especially true in systems where interaction and multiple views are essential. For any information visualization technique to be adopted beyond the initial idea generation, there must be some utility or value in that technique. Our field is not about making pretty pictures. It is about helping people with the complex tasks involved in data analysis and understanding. We need to do a better job of articulating the cognitive tasks that occur in data analysis (location, correlation, emphasis, association, etc.) and articulating how information visualization techniques and systems can help with these tasks. Ultimately, a kind of natural selection will occur: systems with utility and value will be adopted and used, while others will quietly fade away.

The literature on information visualization establishes the usability of interfaces with an overview of the information space, but for zoomable user interfaces, results are mixed. We compare zoomable user interfaces with and without an overview to understand the navigation patterns and usability of these interfaces. Thirty-two subjects solved navigation and browsing tasks on two maps. We found no difference between interfaces in subjects' ability to solve tasks correctly. Eighty percent of the subjects preferred the interface with an overview, stating that it supported navigation and helped keep track of their position on the map. However, subjects were faster with the interface without an overview when using one of the two maps. We conjecture that this difference was due to the organization of that map in multiple levels, which rendered the overview unnecessary by providing richer navigation cues through semantic zooming. The combination of that map and the interface without an overview also improved subjects' recall of objects on the map. Subjects who switched between the overview and the detail windows used more time, suggesting that integration of overview and detail windows adds complexity and requires additional mental and motor effort.

We are developing a prototype storytelling robot for use with children in rehabilitation. Children can remotely control a large furry robot by using a variety of body sensors adapted to their disability or rehabilitation goal. In doing so, they can teach the robot to act out series of movements or "emotions" and then write stories - using a storytelling software - including those movements in the story. The story can then be "played" by the remote controlled robot, which acts out the story. We believe that this robot can motivate the children and help them reach their therapy goals through therapeutic play, either by exercising muscles or joints (e.g. for physically challenges children) or by reflecting on the stories (e.g. for children with developmental disabilities). We use an innovative design methodology involving children as design partners.

Networked and local data exploration systems that use command languages, menus, or form fill-in interfaces rarely give users an indication of the distribution of data. This often leads users to waste time, posing queries that have zero-hit or mega-hit results. Query previews are a novel visual approach for browsing databases. Query previews supply users with data distribution information for selected attributes of the database, and give continuous feedback about the size of the result set as the query is being formed. Subsequent refinements might be necessary to narrow the search. As there is a risk that query previews are an additional step, leading to a more complex and slow search process, a within-subjects empirical study was ran with 12 subjects who used interfaces with and without query previews and with minimized network delays. Even with 12 subjects and minimized network delays statistically significant differences were found, showing that query previews could speed up performance 1.6 to 2.1 times and lead to higher user satisfaction.

To aid designers of digital library interfaces, we present a framework for the design of information representations in terms of previews and overviews. Previews and overviews are graphic or textual representations of information abstracted from primary information objects. Previews act as surrogates for one or a few objects and overviews represent collections of objects. A design framework is elaborated in terms of the following three dimensions: (1) what information objects are available to users, (2) how information objects are related and displayed, and (3) how users can manipulate information objects. When utilized properly, previews and overviews allow users to rapidly discriminate objects of interest from those not of interest, and to more fully understand the scope and nature of digital libraries. This article presents a definition of previews and overviews in context, provides design guidelines, and describes four example applications.

The widespread use of information visualization is hampered by the lack of effective labeling techniques. An informal taxonomy of labeling methods is proposed. We then describe excentric labeling, a new dynamic technique to label a neighborhood of objects located around the cursor. This technique does not intrude into the existing interaction, it is not computationally intensive, and was easily applied to several visualization applications. A pilot study with eight subjects indicates a strong speed benefit over a zoom interface for tasks that involve the exploration of large numbers of objects. Observations and comments from users are presented.

There are numerous problems associated with formulating queries on networked information systems. These include increased data volume and complexity, accompanied by slow network access. This article proposes a new approach to a network query user interfaces that consists of two phases: query preview and query refinement. This new approach is based on the concepts of dynamic queries and query previews, which guides users in rapidly and dynamically eliminating undesired records, reducing the data volume to a manageable size, and refining queries locally before submission over a network. Examples of two applications are given: a Restaurant Finder and a prototype for NASA's Earth Observing Systems Data Information Systems (EOSDIS). Data architecture is discussed, and user feedback is presented.

Thirty-six participants used a static version of either LifeLines, a graphical interface, or a tabular representation to answer questions about a database of temporal personal history information. Results suggest that overall the LifeLines representation led to much faster response times, primarily for questions which involved interval comparisons and making intercategorical connections. A 'first impression' test showed that LifeLines can reduce some of the biases of the tabular record summary. A post-experimental memory test led to significantly (p< 0.004) higher recall for LifeLines. Finally, simple interaction techniques are proposed to compensate for the problems of the static LifeLines display's ability to deal with precise dates, attribute coding and overlaps.

Dynamic telepathology uses a teleoperated microscope to allow pathologists to view samples at a remote location. However, time delays introduced by remote operation have made use of a commercial dynamic telepathology system difficult and frustrating. This paper describes the iterative redesign of the user interface. We redesigned the interface, conducted experiments to evaluate the improvements, and then redesigned the user interface based on the results. Our work shows that predictive displays and local maps improve user control of the microscope and increase user comfort with the system. It also indicates that some degree of automation is necessary to support the navigation process and increase the overall usefulness of the system. Therefore, we also make recommendations for further automation to support the telepathology process and increase the usefulness of the system. While performed on a specific device using a dedicated communications system, the same problems would be encountered in other environments. For example, Internet-based systems that enable remote control or require browsing of large images will need to compensate for time delays and can benefit from our experience with the telepathology application.

Article Putting visualization to work: ProgramFinder for youth placement Share on Authors: Jason B. Ellis GVU Center at Georgia Tech. and Human-Computer Interaction Laboratory, University of Maryland, Institute for Advanced Computer Studies, College Park, MD GVU Center at Georgia Tech. and Human-Computer Interaction Laboratory, University of Maryland, Institute for Advanced Computer Studies, College Park, MDView Profile , Anne Rose Human-Computer Interaction Laboratory, University of Maryland, Institute for Advanced Computer Studies, College Park, MD Human-Computer Interaction Laboratory, University of Maryland, Institute for Advanced Computer Studies, College Park, MDView Profile , Catherine Plaisant Human-Computer Interaction Laboratory, University of Maryland, Institute for Advanced Computer Studies, College Park, MD Human-Computer Interaction Laboratory, University of Maryland, Institute for Advanced Computer Studies, College Park, MDView Profile Authors Info & Claims CHI '97: Proceedings of the ACM SIGCHI Conference on Human factors in computing systemsMarch 1997 Pages 502–509https://doi.org/10.1145/258549.259003Online:27 March 1997Publication History 2citation207DownloadsMetricsTotal Citations2Total Downloads207Last 12 Months3Last 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

Article Bringing treasures to the surface: iterative design for the Library of Congress National Digital Library Program Share on Authors: Catherine Plaisant Human-Computer Interaction Laboratory and Digital Library Research Group, University of Maryland, College Park, MD Human-Computer Interaction Laboratory and Digital Library Research Group, University of Maryland, College Park, MDView Profile , Gary Marchionini Human-Computer Interaction Laboratory and Digital Library Research Group, University of Maryland, College Park, MD Human-Computer Interaction Laboratory and Digital Library Research Group, University of Maryland, College Park, MDView Profile , Tom Bruns Human-Computer Interaction Laboratory and Digital Library Research Group, University of Maryland, College Park, MD Human-Computer Interaction Laboratory and Digital Library Research Group, University of Maryland, College Park, MDView Profile , Anita Komlodi Human-Computer Interaction Laboratory and Digital Library Research Group, University of Maryland, College Park, MD Human-Computer Interaction Laboratory and Digital Library Research Group, University of Maryland, College Park, MDView Profile , Laura Campbell Library of Congress, National Digital Library Program, Washington, DC Library of Congress, National Digital Library Program, Washington, DCView Profile Authors Info & Claims CHI '97: Proceedings of the ACM SIGCHI Conference on Human factors in computing systemsMarch 1997 Pages 518–525https://doi.org/10.1145/258549.259009Published:27 March 1997 11citation597DownloadsMetricsTotal Citations11Total Downloads597Last 12 Months5Last 6 weeks1 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

LifeLines provide a general visualization environment for personal histories that can be applied to medical and court records, professional histories and other types of biographical data.A one screen overview shows multiple facets of the records.Aspects, for example medical conditions or legal cases, are displayed as individual time lines, while icons indicate discrete events, such as physician consultations or legal reviews.Line color and thickness illustrate relationships or significance, rescaling tools and filters allow users to focus on part of the information.LifeLines reduce the chances of missing information, facilitate spotting anomalies and trends, streamline access to details, while remaining tailorable and easily transferable between applications.The paper describes the use of LifeLines for youth records of the Maryland Department of Juvenile Justice and also for medical records.User's feedback was collected using a Visual Basic prototype for the youth record.

Article Where is information visualization technology going? Share on Authors: Mountaz Hascoët-Zizi LRI - Bât 490- Université Paris-Sud, 91 405 ORSAY Cedex - France LRI - Bât 490- Université Paris-Sud, 91 405 ORSAY Cedex - FranceView Profile , Chris Ahlberg IVEE - Chalmers University IVEE - Chalmers UniversityView Profile , Robert Korfhage Pittsburgh University Pittsburgh UniversityView Profile , Catherine Plaisant A.V. Williams Building, University of Maryland, College Park, MD A.V. Williams Building, University of Maryland, College Park, MDView Profile , Matthew Chalmers UBILAB UBILABView Profile , Ramana Rao Xerox Parc Xerox ParcView Profile Authors Info & Claims UIST '96: Proceedings of the 9th annual ACM symposium on User interface software and technologyNovember 1996 Pages 75–77https://doi.org/10.1145/237091.237101Online:01 November 1996Publication History 3citation641DownloadsMetricsTotal Citations3Total Downloads641Last 12 Months9Last 6 weeks1 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

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