Twin-Win Model: A human-centered approach to research success.

A 70-year-old simmering debate has erupted into vigorous battles over the most effective ways to conduct research. Well-established beliefs are being forcefully challenged by advocates of new research models. While there can be no final resolution to this battle, this paper offers the Twin-Win Model to guide teams of researchers, academic leaders, business managers, and government funding policymakers. The Twin-Win Model favors a problem-oriented approach to research, which encourages formation of teams to pursue the dual goals of breakthrough theories in published papers and validated solutions that are ready for widespread dissemination. The raised expectations of simultaneously pursuing foundational discoveries and powerful innovations are a step beyond traditional approaches that advocate basic research first. Evidence from citation analysis and researcher interviews suggests that simultaneous pursuit of both goals raises the chance of twin-win success.

IVESA - Visual Analysis of Time-Stamped Event Sequences.

Time-stamped event sequences (TSEQs) are time-oriented data without value information, shifting the focus of users to the exploration of temporal event occurrences. TSEQs exist in application domains, such as sleeping behavior, earthquake aftershocks, and stock market crashes. Domain experts face four challenges, for which they could use interactive and visual data analysis methods. First, TSEQs can be large with respect to both the number of sequences and events, often leading to millions of events. Second, domain experts need validated metrics and features to identify interesting patterns. Third, after identifying interesting patterns, domain experts contextualize the patterns to foster sensemaking. Finally, domain experts seek to reduce data complexity by data simplification and machine learning support. We present IVESA, a visual analytics approach for TSEQs. It supports the analysis of TSEQs at the granularities of sequences and events, supported with metrics and feature analysis tools. IVESA has multiple linked views that support overview, sort+filter, comparison, details-on-demand, and metadata relation-seeking tasks, as well as data simplification through feature analysis, interactive clustering, filtering, and motif detection and simplification. We evaluated IVESA with three case studies and a user study with six domain experts working with six different datasets and applications. Results demonstrate the usability and generalizability of IVESA across applications and cases that had up to 1,000,000 events.

Querying Event Sequences by Exact Match or Similarity Search: Design and Empirical Evaluation.

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.

Lightning and Thunder: The Early Days of Interactive Information Visualization at the University of Maryland.

The thrill of scientific discovery, the excitement of engineering development, and the fresh thinking of design explorations were invigorating as we participated in the birth of a new discipline: Information Visualization. This discipline, based on graphical user interfaces with pointing devices, became possible as software matured, hardware sped up, and screen resolution improved. Driven by the concepts of direct manipulation and dynamic queries, we made interactive interfaces that empowered users and opened up new possibilities for the next generation of designers. We worked with professionals who had real problems and tested real users to get their feedback. Some projects failed and some papers never got published, but many of the new ideas found their way into widely used commercial products. Our great satisfaction is that our students have spread the community spirit of the Human-Computer Interaction Laboratory as they continue to make further contributions.

Temporal summaries: supporting temporal categorical searching, aggregation and comparison.

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.

Interactive entity resolution in relational data: a visual analytic tool and its evaluation.

Databases often contain uncertain and imprecise references to real-world entities. Entity resolution, the process of reconciling multiple references to underlying real-world entities, is an important data cleaning process required before accurate visualization or analysis of the data is possible. In many cases, in addition to noisy data describing entities, there is data describing the relationships among the entities. This relational data is important during the entity resolution process; it is useful both for the algorithms which determine likely database references to be resolved and for visual analytic tools which support the entity resolution process. In this paper, we introduce a novel user interface, D-Dupe, for interactive entity resolution in relational data. D-Dupe effectively combines relational entity resolution algorithms with a novel network visualization that enables users to make use of an entity's relational context for making resolution decisions. Since resolution decisions often are interdependent, D-Dupe facilitates understanding this complex process through animations which highlight combined inferences and a history mechanism which allows users to inspect chains of resolution decisions. An empirical study with 12 users confirmed the benefits of the relational context visualization on the performance of entity resolution tasks in relational data in terms of time as well as users' confidence and satisfaction.

eHealth research from the user's perspective.

The application of information technology (IT) to issues of healthcare delivery has had a long and tortuous history in the United States. Within the field of eHealth, vanguard applications of advanced computing techniques, such as applications in artificial intelligence or expert systems, have languished in spite of a track record of scholarly publication and decisional accuracy. The problem is one of purpose, of asking the right questions for the science to solve. Historically, many computer science pioneers have been tempted to ask "what can the computer do?" New advances in eHealth are prompting developers to ask "what can people do?" How can eHealth take part in national goals for healthcare reform to empower relationships between healthcare professionals and patients, healthcare teams and families, and hospitals and communities to improve health equitably throughout the population? To do this, eHealth researchers must combine best evidence from the user sciences (human factors engineering, human-computer interaction, psychology, and usability) with best evidence in medicine to create transformational improvements in the quality of care that medicine offers. These improvements should follow recommendations from the Institute of Medicine to create a healthcare system that is (1) safe, (2) effective (evidence based), (3) patient centered, and (4) timely. Relying on the eHealth researcher's intuitive grasp of systems issues, improvements should be made with considerations of users and beneficiaries at the individual (patient-physician), group (family-staff), community, and broad environmental levels.

Nuclear envelope dystrophies show a transcriptional fingerprint suggesting disruption of Rb-MyoD pathways in muscle regeneration.

Mutations of lamin A/C (LMNA) cause a wide range of human disorders, including progeria, lipodystrophy, neuropathies and autosomal dominant Emery-Dreifuss muscular dystrophy (EDMD). EDMD is also caused by X-linked recessive loss-of-function mutations of emerin, another component of the inner nuclear lamina that directly interacts with LMNA. One model for disease pathogenesis of LMNA and emerin mutations is cell-specific perturbations of the mRNA transcriptome in terminally differentiated cells. To test this model, we studied 125 human muscle biopsies from 13 diagnostic groups (125 U133A, 125 U133B microarrays), including EDMD patients with LMNA and emerin mutations. A Visual and Statistical Data Analyzer (VISDA) algorithm was used to statistically model cluster hierarchy, resulting in a tree of phenotypic classifications. Validations of the diagnostic tree included permutations of U133A and U133B arrays, and use of two probe set algorithms (MAS5.0 and MBEI). This showed that the two nuclear envelope defects (EDMD LMNA, EDMD emerin) were highly related disorders and were also related to fascioscapulohumeral muscular dystrophy (FSHD). FSHD has recently been hypothesized to involve abnormal interactions of chromatin with the nuclear envelope. To identify disease-specific transcripts for EDMD, we applied a leave-one-out (LOO) cross-validation approach using LMNA patient muscle as a test data set, with reverse transcription-polymerase chain reaction (RT-PCR) validations in both LMNA and emerin patient muscle. A high proportion of top-ranked and validated transcripts were components of the same transcriptional regulatory pathway involving Rb1 and MyoD during muscle regeneration (CRI-1, CREBBP, Nap1L1, ECREBBP/p300), where each was specifically upregulated in EDMD. Using a muscle regeneration time series (27 time points) we develop a transcriptional model for downstream consequences of LMNA and emerin mutations. We propose that key interactions between the nuclear envelope and Rb and MyoD fail in EDMD at the point of myoblast exit from the cell cycle, leading to poorly coordinated phosphorylation and acetylation steps. Our data is consistent with mutations of nuclear lamina components leading to destabilization of the transcriptome in differentiated cells.

Community response grids: using information technology to help communities respond to bioterror emergencies.

Access to accurate and trusted information is vital in preparing for, responding to, and recovering from an emergency. To facilitate response in large-scale emergency situations, Community Response Grids (CRGs) integrate Internet and mobile technologies to enable residents to report information, professional emergency responders to disseminate instructions, and residents to assist one another. CRGs use technology to help residents and professional emergency responders to work together in community response to emergencies, including bioterrorism events. In a time of increased danger from bioterrorist threats, the application of advanced information and communication technologies to community response is vital in confronting such threats. This article describes CRGs, their underlying concepts, development efforts, their relevance to biosecurity and bioterrorism, and future research issues in the use of technology to facilitate community response.

Coping with Volume and Variety in Temporal Event Sequences: Strategies for Sharpening Analytic Focus.

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.

Apply or Die: On the Role and Assessment of Application Papers in Visualization.

Application-oriented papers provide an important way to invigorate and cross-pollinate the visualization field, but the exact criteria for judging an application paper's merit remain an open question. This article builds on a panel at the 2016 IEEE Visualization Conference entitled "Application Papers: What Are They, and How Should They Be Evaluated?" that sought to gain a better understanding of prevalent views in the visualization community. This article surveys current trends that favor application papers, reviews the benefits and contributions of this paper type, and discusses their assessment in the review process. It concludes with recommendations to ensure that the visualization community is more inclusive to application papers.

Knowledge discovery in high-dimensional data: case studies and a user survey for the rank-by-feature framework.

Knowledge discovery in high-dimensional data is a challenging enterprise, but new visual analytic tools appear to offer users remarkable powers if they are ready to learn new concepts and interfaces. Our three-year effort to develop versions of the Hierarchical Clustering Explorer (HCE) began with building an interactive tool for exploring clustering results. It expanded, based on user needs, to include other potent analytic and visualization tools for multivariate data, especially the rank-by-feature framework. Our own successes using HCE provided some testimonial evidence of its utility, but we felt it necessary to get beyond our subjective impressions. This paper presents an evaluation of the Hierarchical Clustering Explorer (HCE) using three case studies and an e-mail user survey (n = 57) to focus on skill acquisition with the novel concepts and interface for the rank-by-feature framework. Knowledgeable and motivated users in diverse fields provided multiple perspectives that refined our understanding of strengths and weaknesses. A user survey confirmed the benefits of HCE, but gave less guidance about improvements. Both evaluations suggested improved training methods.

Balancing systematic and flexible exploration of social networks.

Social network analysis (SNA) has emerged as a powerful method for understanding the importance of relationships in networks. However, interactive exploration of networks is currently challenging because: (1) it is difficult to find patterns and comprehend the structure of networks with many nodes and links, and (2) current systems are often a medley of statistical methods and overwhelming visual output which leaves many analysts uncertain about how to explore in an orderly manner. This results in exploration that is largely opportunistic. Our contributions are techniques to help structural analysts understand social networks more effectively. We present SocialAction, a system that uses attribute ranking and coordinated views to help users systematically examine numerous SNA measures. Users can (1) flexibly iterate through visualizations of measures to gain an overview, filter nodes, and find outliers, (2) aggregate networks using link structure, find cohesive subgroups, and focus on communities of interest, and (3) untangle networks by viewing different link types separately, or find patterns across different link types using a matrix overview. For each operation, a stable node layout is maintained in the network visualization so users can make comparisons. SocialAction offers analysts a strategy beyond opportunism, as it provides systematic, yet flexible, techniques for exploring social networks.

Network visualization by semantic substrates.

Networks have remained a challenge for information visualization designers because of the complex issues of node and link layout coupled with the rich set of tasks that users present. This paper offers a strategy based on two principles: (1) layouts are based on user-defined semantic substrates, which are non-overlapping regions in which node placement is based on node attributes, (2) users interactively adjust sliders to control link visibility to limit clutter and thus ensure comprehensibility of source and destination. Scalability is further facilitated by user control of which nodes are visible. We illustrate our semantic substrates approach as implemented in NVSS 1.0 with legal precedent data for up to 1122 court cases in three regions with 7645 legal citations.

Novel user interface design for medication reconciliation: an evaluation of Twinlist.

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 and analysis of microarray and gene ontology data with treemaps.

BACKGROUND: The increasing complexity of genomic data presents several challenges for biologists. Limited computer monitor views of data complexity and the dynamic nature of data in the midst of discovery increase the challenge of integrating experimental results with information resources. The use of Gene Ontology enables researchers to summarize results of quantitative analyses in this framework, but the limitations of typical browser presentation restrict data access. RESULTS: Here we describe extensions to the treemap design to visualize and query genome data. Treemaps are a space-filling visualization technique for hierarchical structures that show attributes of leaf nodes by size and color-coding. Treemaps enable users to rapidly compare sizes of nodes and sub-trees, and we use Gene Ontology categories, levels of RNA, and other quantitative attributes of DNA microarray experiments as examples. Our implementation of treemaps, Treemap 4.0, allows user-defined filtering to focus on the data of greatest interest, and these queried files can be exported for secondary analyses. Links to model system web pages from Treemap 4.0 enable users access to details about specific genes without leaving the query platform. CONCLUSIONS: Treemaps allow users to view and query the data from an experiment on a single computer monitor screen. Treemap 4.0 can be used to visualize various genome data, and is particularly useful for revealing patterns and details within complex data sets.

In vivo filtering of in vitro expression data reveals MyoD targets.

A published set of downstream targets of MyoD defined in a well-controlled in vitro experiment was filtered for relevance to muscle regeneration using a 27-time-point in vivo murine regeneration series. Using interactive hierarchical and Bayes soft clustering, only a minority of the targets defined in vitro can be confirmed in vivo (approximately 50% of induced transcripts, and none of repressed transcripts). This approach provided strong support that 18 targets including of MyoD are biologically relevant during myoblast differentiation.

A task taxonomy for network evolution analysis.

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.