We investigate tasks that can be accomplished with unlabeled graphs, which are graphs with nodes that do not have persistent or semantically meaningful labels attached. New visualization techniques to represent unlabeled graphs have been proposed, but more understanding of unlabeled graph tasks is required before these techniques can be adequately evaluated. Some network visualization tasks apply to both labeled and unlabeled graphs, but many do not translate between these contexts. We propose a data abstraction model that distinguishes the Unlabeled context from the increasingly semantically rich Labeled, Attributed, and Augmented contexts. We filter tasks collected and gleaned from the literature according to our data abstraction and analyze the surfaced tasks, leading to a taxonomy of abstract tasks for unlabeled graphs. Our task taxonomy is organized according to the Target data under consideration, the Action intended by the user, and the Scope of the data at play. We show the descriptive power of this task abstraction by connecting to concrete examples from previous frameworks, and connecting these abstractions to real-world problems. To showcase the evaluative power of the taxonomy, we perform a preliminary assessment across 6 different network visualization idioms for each task. For each combination of task and visual encoding, we consider the effort required from viewers, the likelihood of task success, and how both factors vary between small-scale and large-scale graphs.

Designing multiscale visualizations, particularly when the ratio between the largest scale and the smallest item is large, can be challenging, and designers have developed many approaches to overcome this challenge. We present a design space for visualization with multiple scales. The design space includes three dimensions, with eight total subdimensions. We demonstrate its descriptive power by using it to code approaches from a corpus we compiled of 52 examples, created by a mix of academics and practitioners. We demonstrate descriptive power by analyzing and partitioning these examples into four high-level strategies for designing multiscale visualizations, which are shared approaches with respect to design space dimension choices. We demonstrate generative power by analyzing missed opportunities within the corpus of examples, identified through analysis of the design space, where we note how certain examples could have benefited from different choices. We discuss patterns in the use of different dimension and strategy choices in the different visualization contexts of analysis and presentation. Supplemental materials: https://osf.io/wbrdm/ Design space website: https://marasolen.github.io/multiscale-vis-ds/.

At the beginning of the COVID-19 pandemic, HealthLink BC (HLBC) rapidly integrated physicians into the triage process of their virtual healthcare service to improve patient outcomes and satisfaction with this service and preserve health care system capacity. We present the design and implementation of a visual analytics tool, VIVA (Virtual healthcare Interactions using Visual Analytics), to support HLBC in analysing various forms of usage data from the service. We abstract HLBC's data and data analysis tasks, which we use to inform our design of VIVA. We also present the interactive workflow abstraction of Scan, Act, Adapt. We validate VIVA's design through three case studies with stakeholder domain experts. We also propose the Controllability Through Configuration model to conduct and analyze design studies, and discuss architectural evolution of VIVA through that lens. It articulates configuration, both that specified by a developer or technical power user and that constructed automatically through log data from previous interactive sessions, as a bridge between the rigidity of hardwired programming and the time-consuming implementation of full end-user interactivity.

We offer a new model of the sensemaking process for data analysis and visualization. Whereas past sensemaking models have been grounded in positivist assumptions about the nature of knowledge, we reframe data sensemaking in critical, humanistic terms by approaching it through an interpretivist lens. Our three-phase process model uses the analogy of an iceberg, where data is the visible tip of underlying schemas. In the Add phase, the analyst acquires data, incorporates explicit schemas from the data, and absorbs the tacit schemas of both data and people. In the Check phase, the analyst interprets the data with respect to the current schemas and evaluates whether the schemas match the data. In the Refine phase, the analyst considers the role of power, articulates what was tacit into explicitly stated schemas, updates data, and formulates findings. Our model has four important distinguishing features: Tacit and Explicit Schemas, Schemas First and Always, Data as a Schematic Artifact, and Schematic Multiplicity. We compare the roles of schemas in past sensemaking models and draw conceptual distinctions based on a historical review of schemas in different academic traditions. We validate the descriptive and prescriptive power of our model through four analysis scenarios: noticing uncollected data, learning to wrangle data, downplaying inconvenient data, and measuring with sensors. We conclude by discussing the value of interpretivism, the virtue of epistemic humility, and the pluralism this sensemaking model can foster.

This January 2025 issue of the IEEE Transactions on Visualization and Computer Graphics (TVCG) contains the proceedings of IEEE VIS 2024, held on October 1318 October, 2024 in St. Pete Beach, Florida, USA, with the three General Chairs Paul Rosen (University of Utah), Kristi Potter (U.S. National Renewable Energy Laboratory), and Remco Chang (Tufts University). With IEEE VIS 2024, the conference series is in its 35th year.

An 'invariant descriptor' captures meaningful structural features of networks, useful where traditional visualizations, like node-link views, face challenges like the 'hairball phenomenon' (inscrutable overlap of points and lines). Designing invariant descriptors involves balancing abstraction and information retention, as richer data summaries demand more storage and computational resources. Building on prior work, chiefly the BMatrix-a matrix descriptor visualized as the invariant 'network portrait' heatmap-we introduce BFS-Census, a new algorithm computing our Census data structures: Census-Node, Census-Edge, and Census-Stub. Our experiments show Census-Stub, which focuses on 'stubs' (half-edges), has orders of magnitude greater discerning power (ability to tell non-isomorphic graphs apart) than any other descriptor in this study, without a difficult trade-off: the substantial increase in resolution doesn't come at a commensurate cost in storage space or computation power. We also present new visualizations-our Hop-Census polylines and Census-Census trajectories-and evaluate them using real-world graphs, including a sensitivity analysis that shows graph topology change maps to visual Census change. Availability: Our Supplemental materials are available at osf.io/nmzra.

While previous work has found success in deploying visualizations as museum exhibits, it has not investigated whether museum context impacts visitor behaviour with these exhibits. We present an interactive Deep-time Literacy Visualization Exhibit (DeLVE) to help museum visitors understand deep time (lengths of extremely long geological processes) by improving proportional reasoning skills through comparison of different time periods. DeLVE uses a new visualization idiom, Connected Multi-Tier Ranges, to visualize curated datasets of past events across multiple scales of time, relating extreme scales with concrete scales that have more familiar magnitudes and units. Museum staff at three separate museums approved the deployment of DeLVE as a digital kiosk, and devoted time to curating a unique dataset in each of them. We collect data from two sources, an observational study and system trace logs. We discuss the importance of context: similar museum exhibits in different contexts were received very differently by visitors. We additionally discuss differences in our process from Sedlmair et al.'s design study methodology which is focused on design studies triggered by connection with collaborators rather than the discovery of a concept to communicate. Supplemental materials are available at: https://osf.io/z53dq/.

The work involved in gathering, wrangling, cleaning, and otherwise preparing data for analysis is often the most time consuming and tedious aspect of data work. Although many studies describe data preparation within the context of data science workflows, there has been little research on data preparation in data journalism. We address this gap with a hybrid form of thematic analysis that combines deductive codes derived from existing accounts of data science workflows and inductive codes arising from an interview study with 36 professional data journalists. We extend a previous model of data science work to incorporate detailed activities of data preparation. We synthesize 60 dirty data issues from 16 taxonomies on dirty data and our interview data, and we provide a novel taxonomy to characterize these dirty data issues as discrepancies between mental models. We also identify four challenges faced by journalists: diachronic, regional, fragmented, and disparate data sources.

Group decision making occurs when individuals collectively choose from a set of alternatives based on individual preferences. In these ubiquitous situations, it can be helpful for decision makers to visually model and compare stakeholder preferences in order to better understand others' points of view and reach consensus. Although a number of collaboration support tools allow preference inspection in some form, they are rarely based on a comprehensive understanding of the needs of group decision makers. The goal of our work is to study these demands, develop abstractions to model them, and create a framework to inform the design and assessment of existing and future tools. First, guided by decision analysis theory, we examine a diverse set of group decision making scenarios, characterizing variations in problem formulation, analysis goals, and situational features. Second, we amalgamate these findings into data and task abstractions that can be used to relate specific scenarios to the language of visualization. Finally, we use this framework to assess existing preference visualization tools in order to shed light on areas for future work in supporting group decision making.

Genomic Epidemiology (genEpi) is a branch of public health that uses many different data types including tabular, network, genomic, and geographic, to identify and contain outbreaks of deadly diseases. Due to the volume and variety of data, it is challenging for genEpi domain experts to conduct data reconnaissance; that is, have an overview of the data they have and make assessments toward its quality, completeness, and suitability. We present an algorithm for data reconnaissance through automatic visualization recommendation, GEViTRec. Our approach handles a broad variety of dataset types and automatically generates visually coherent combinations of charts, in contrast to existing systems that primarily focus on singleton visual encodings of tabular datasets. We automatically detect linkages across multiple input datasets by analyzing non-numeric attribute fields, creating a data source graph within which we analyze and rank paths. For each high-ranking path, we specify chart combinations with positional and color alignments between shared fields, using a gradual binding approach to transform initial partial specifications of singleton charts to complete specifications that are aligned and oriented consistently. A novel aspect of our approach is its combination of domain-agnostic elements with domain-specific information that is captured through a domain-specific visualization prevalence design space. Our implementation is applied to both synthetic data and real Ebola outbreak data. We compare GEViTRec's output to what previous visualization recommendation systems would generate, and to manually crafted visualizations used by practitioners. We conducted formative evaluations with ten genEpi experts to assess the relevance and interpretability of our results. Code, Data, and Study Materials Availability: https://github.com/amcrisan/GEVitRec.

Graph neural networks (GNNs) are a class of powerful machine learning tools that model node relations for making predictions of nodes or links. GNN developers rely on quantitative metrics of the predictions to evaluate a GNN, but similar to many other neural networks, it is difficult for them to understand if the GNN truly learns characteristics of a graph as expected. We propose an approach to corresponding an input graph to its node embedding (aka latent space), a common component of GNNs that is later used for prediction. We abstract the data and tasks, and develop an interactive multi-view interface called CorGIE to instantiate the abstraction. As the key function in CorGIE, we propose the K-hop graph layout to show topological neighbors in hops and their clustering structure. To evaluate the functionality and usability of CorGIE, we present how to use CorGIE in two usage scenarios, and conduct a case study with five GNN experts. Availability: Open-source code at https://github.com/zipengliu/corgie-ui/, supplemental materials & video at https://osf.io/tr3sb/.

Visualization collections, accessed by platforms such as Tableau Online or Power Bl, are used by millions of people to share and access diverse analytical knowledge in the form of interactive visualization bundles. Result snippets, compact previews of these bundles, are presented to users to help them identify relevant content when browsing collections. Our engagement with Tableau product teams and review of existing snippet designs on five platforms showed us that current practices fail to help people judge the relevance of bundles because they include only the title and one image. Users frequently need to undertake the time-consuming endeavour of opening a bundle within its visualization system to examine its many views and dashboards. In response, we contribute the first systematic approach to visualization snippet design. We propose a framework for snippet design that addresses eight key challenges that we identify. We present a computational pipeline to compress the visual and textual content of bundles into representative previews that is adaptive to a provided pixel budget and provides high information density with multiple images and carefully chosen keywords. We also reflect on the method of visual inspection through random sampling to gain confidence in model and parameter choices.

Class separation is an important concept in machine learning and visual analytics. We address the visual analysis of class separation measures for both high-dimensional data and its corresponding projections into 2D through dimensionality reduction (DR) methods. Although a plethora of separation measures have been proposed, it is difficult to compare class separation between multiple datasets with different characteristics, multiple separation measures, and multiple DR methods. We present ProSeCo, an interactive visualization approach to support comparison between up to 20 class separation measures and up to 4 DR methods, with respect to any of 7 dataset characteristics: dataset size, dataset dimensions, class counts, class size variability, class size skewness, outlieriness, and real-world vs. synthetically generated data. ProSeCo supports (1) comparing across measures, (2) comparing high-dimensional to dimensionally-reduced 2D data across measures, (3) comparing between different DR methods across measures, (4) partitioning with respect to a dataset characteristic, (5) comparing partitions for a selected characteristic across measures, and (6) inspecting individual datasets in detail. We demonstrate the utility of ProSeCo in two usage scenarios, using datasets [1] posted at https://osf.io/epcf9/.

Strategies for selecting the next data instance to label, in service of generating labeled data for machine learning, have been considered separately in the machine learning literature on active learning and in the visual analytics literature on human-centered approaches. We propose a unified design space for instance selection strategies to support detailed and fine-grained analysis covering both of these perspectives. We identify a concise set of 15 properties, namely measureable characteristics of datasets or of machine learning models applied to them, that cover most of the strategies in these literatures. To quantify these properties, we introduce Property Measures (PM) as fine-grained building blocks that can be used to formalize instance selection strategies. In addition, we present a taxonomy of PMs to support the description, evaluation, and generation of PMs across four dimensions: machine learning (ML) Model Output , Instance Relations , Measure Functionality , and Measure Valence . We also create computational infrastructure to support qualitative visual data analysis: a visual analytics explainer for PMs built around an implementation of PMs using cascades of eight atomic functions. It supports eight analysis tasks, covering the analysis of datasets and ML models using visual comparison within and between PMs and groups of PMs, and over time during the interactive labeling process. We iteratively refined the PM taxonomy, the explainer, and the task abstraction in parallel with each other during a two-year formative process, and show evidence of their utility through a summative evaluation with the same infrastructure. This research builds a formal baseline for the better understanding of the commonalities and differences of instance selection strategies, which can serve as the stepping stone for the synthesis of novel strategies in future work.

For the many journalists who use data and computation to report the news, data wrangling is an integral part of their work. Despite an abundance of literature on data wrangling in the context of enterprise data analysis, little is known about the specific operations, processes, and pain points journalists encounter while performing this tedious, time-consuming task. To better understand the needs of this user group, we conduct a technical observation study of 50 public repositories of data and analysis code authored by 33 professional journalists at 26 news organizations. We develop two detailed and cross-cutting taxonomies of data wrangling in computational journalism, for actions and for processes. We observe the extensive use of multiple tables, a notable gap in previous wrangling analyses. We develop a concise, actionable framework for general multi-table data wrangling that includes wrangling operations documented in our taxonomy that are without clear parallels in other work. This framework, the first to incorporate tables as first-class objects, will support future interactive wrangling tools for both computational journalism and general-purpose use. We assess the generative and descriptive power of our framework through discussion of its relationship to our set of taxonomies.

Cloud-based visualization services have made visual analytics accessible to a much wider audience than ever before. Systems such as Tableau have started to amass increasingly large repositories of analytical knowledge in the form of interactive visualization workbooks. When shared, these collections can form a visual analytic knowledge base. However, as the size of a collection increases, so does the difficulty in finding relevant information. Content-based recommendation (CBR) systems could help analysts in finding and managing workbooks relevant to their interests. Toward this goal, we focus on text-based content that is representative of the subject matter of visualizations rather than the visual encodings and style. We discuss the challenges associated with creating a CBR based on visualization specifications and explore more concretely how to implement the relevance measures required using Tableau workbook specifications as the source of content data. We also demonstrate what information can be extracted from these visualization specifications and how various natural language processing techniques can be used to compute similarity between workbooks as one way to measure relevance. We report on a crowd-sourced user study to determine if our similarity measure mimics human judgement. Finally, we choose latent Dirichl et al.ocation (LDA) as a specific model and instantiate it in a proof-of-concept recommender tool to demonstrate the basic function of our similarity measure.

Group decision making is when two or more individuals must collectively choose among a competing set of alternatives based on their individual preferences. In these situations, it can be helpful for decision makers to model and visually compare their preferences in order to better understand each others’ points of view. Although a number of tools for preference modelling and inspection exist, none are based on detailed data and task models that capture the demands of group decision making in particular. This paper is a first step in addressing this gap. By going through the four stages of the nested model of visualization design, we have developed and tested a prototype to support group decision making when decision makers express their preferences directly on the alternatives.

Automatic segmentation of logs for creativity tools such as image editing systems could improve their usability and learnability by supporting such interaction use cases as smart history navigation or recommending alternative design choices. We propose a multi-level segmentation model that works for many image editing tasks including poster creation, portrait retouching, and special effect creation. The lowest-level chunks of logged events are computed using a support vector machine model and higher-level chunks are built on top of these, at a level of granularity that can be customized for specific use cases. Our model takes into account features derived from four event attributes collected in realistically complex Photoshop sessions with expert users: command, timestamp, image content, and artwork layer. We present a detailed analysis of the relevance of each feature and evaluate the model using both quantitative performance metrics and qualitative analysis of sample sessions.

Abstract Understanding how spaces in buildings are being used is vital for optimizing space utilization, for improving resource allocation, and for the design of new facilities. We present a multi‐year design study that resulted in Ocupado, a set of visual decision‐support tools centered around occupancy data for stakeholders in facilities management and planning. Ocupado uses WiFi devices as a proxy for human presence, capturing location‐based counts that preserve privacy without trajectories. We contribute data and task abstractions for studying space utilization for combinations of data granularities in both space and time. In addition, we contribute generalizable design choices for visualizing location‐based counts relating to indoor environments. We provide evidence of Ocupado's utility through multiple analysis scenarios with real‐world data refined through extensive stakeholder feedback, and discussion of its take‐up by our industry partner.

Graph drawing readability metrics are routinely used to assess and create node-link layouts of network data. Existing readability metrics fall short in three ways. The many count-based metrics such as edge-edge or node-edge crossings simply provide integer counts, missing the opportunity to quantify the amount of overlap between items, which may vary in size, at a more fine-grained level. Current metrics focus solely on single-level topological structure, ignoring the possibility of multi-level structure such as large and thus highly salient metanodes. Most current metrics focus on the measurement of clutter in the form of crossings and overlaps, and do not take into account the trade-off between the clutter and the information sparsity of the drawing, which we refer to as sprawl. We propose an area-aware approach to clutter metrics that tracks the extent of geometric overlaps between node-node, node-edge, and edge-edge pairs in detail. It handles variable-size nodes and explicitly treats metanodes and leaf nodes uniformly. We call the combination of a sprawl metric and an area-aware clutter metric a sprawlter metric. We present an instantiation of the sprawlter metrics featuring a formal and thorough discussion of the crucial component, the penalty mapping function. We implement and validate our proposed metrics with extensive computational analysis of graph layouts, considering four layout algorithms and 56 layouts encompassing both real-world data and synthetic examples illustrating specific configurations of interest.

We address the visual comparison of multiple phylogenetic trees that arises in evolutionary biology, specifically between one reference tree and a collection of dozens to hundreds of other trees. We abstract the domain questions of phylogenetic tree comparison as tasks to look for supporting or conflicting evidence for hypotheses that requires inspection of both topological structure and attribute values at different levels of detail in the tree collection. We introduce the new visual encoding idiom of aggregated dendrograms to concisely summarize the topological relationships between interactively chosen focal subtrees according to biologically meaningful criteria, and provide a layout algorithm that automatically adapts to the available screen space. We design and implement the ADView system, which represents trees at multiple levels of detail across multiple views: the entire collection, a subset of trees, an individual tree, specific subtrees of interest, and the individual branch level. We benchmark the algorithms developed for ADView, compare its information density to previous work, and demonstrate its utility for quickly gathering evidence about biological hypotheses through usage scenarios with data from recently published phylogenetic analysis and case studies of expert use with real-world data, drawn from a summative interview study.

MOTIVATION: Data visualization is an important tool for exploring and communicating findings from genomic and healthcare datasets. Yet, without a systematic way of organizing and describing the design space of data visualizations, researchers may not be aware of the breadth of possible visualization design choices or how to distinguish between good and bad options. RESULTS: We have developed a method that systematically surveys data visualizations using the analysis of both text and images. Our method supports the construction of a visualization design space that is explorable along two axes: why the visualization was created and how it was constructed. We applied our method to a corpus of scientific research articles from infectious disease genomic epidemiology and derived a Genomic Epidemiology Visualization Typology (GEViT) that describes how visualizations were created from a series of chart types, combinations and enhancements. We have also implemented an online gallery that allows others to explore our resulting design space of visualizations. Our results have important implications for visualization design and for researchers intending to develop or use data visualization tools. Finally, the method that we introduce is extensible to constructing visualizations design spaces across other research areas. AVAILABILITY AND IMPLEMENTATION: Our browsable gallery is available at http://gevit.net and all project code can be found at https://github.com/amcrisan/gevitAnalysisRelease. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Adjutant is an open-source, interactive and R-based application to support mining PubMed for literature reviews. Given a PubMed-compatible search query, Adjutant downloads the relevant articles and allows the user to perform an unsupervised clustering analysis to identify data-driven topic clusters. Following clustering, users can also sample documents using different strategies to obtain a more manageable dataset for further analysis. Adjutant makes explicit trade-offs between speed and accuracy, which are modifiable by the user, such that a complete analysis of several thousand documents can take a few minutes. All analytic datasets generated by Adjutant are saved, allowing users to easily conduct other downstream analyses that Adjutant does not explicitly support. AVAILABILITY AND IMPLEMENTATION: Adjutant is implemented in R, using Shiny, and is available at https://github.com/amcrisan/Adjutant. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract Clickstream data has the potential to provide insights into e‐commerce consumer behavior, but previous techniques fall short of handling the scale and complexity of real‐world datasets because they require relatively clean and small input. We present Segmentifier, a novel visual analytics interface that supports an iterative process of refining collections of action sequences into meaningful segments. We present task and data abstractions for clickstream data analysis, leading to a high‐level model built around an iterative view‐refine‐record loop with outcomes of conclude with an answer, export segment for further analysis in downstream tools, or abandon the question for a more fruitful analysis path. Segmentifier supports fast and fluid refinement of segments through tightly coupled visual encoding and interaction with a rich set of views that show evocative derived attributes for segments, sequences, and actions in addition to underlying raw sequences. These views support fast and fluid refinement of segments through filtering and partitioning attribute ranges. Interactive visual queries on custom action sequences are aggregated according to a three‐level hierarchy. Segmentifier features a detailed glyph‐based visual history of the automatically recorded analysis process showing the provenance of each segment as an analysis path of attribute constraints. We demonstrate the effectiveness of our approach through a usage scenario with real‐world data and a case study documenting the insights gained by a corporate e‐commerce analyst.

Visualization researchers and practitioners engaged in generating or evaluating designs are faced with the difficult problem of transforming the questions asked and actions taken by target users from domain-specific language and context into more abstract forms. Existing abstract task classifications aim to provide support for this endeavour by providing a carefully delineated suite of actions. Our experience is that this bottom-up approach is part of the challenge: low-level actions are difficult to interpret without a higher-level context of analysis goals and the analysis process. To bridge this gap, we propose a framework based on analysis reports derived from open-coding 20 design study papers published at IEEE InfoVis 2009-2015, to build on the previous work of abstractions that collectively encompass a broad variety of domains. The framework is organized in two axes illustrated by nine analysis goals. It helps situate the analysis goals by placing each goal under axes of specificity (Explore, Describe, Explain, Confirm) and number of data populations (Single, Multiple). The single-population types are Discover Observation, Describe Observation, Identify Main Cause, and Collect Evidence. The multiple-population types are Compare Entities, Explain Differences, and Evaluate Hypothesis. Each analysis goal is scoped by an input and an output and is characterized by analysis steps reported in the design study papers. We provide examples of how we and others have used the framework in a top-down approach to abstracting domain problems: visualization designers or researchers first identify the analysis goals of each unit of analysis in an analysis stream, and then encode the individual steps using existing task classifications with the context of the goal, the level of specificity, and the number of populations involved in the analysis.

There are many ways to visualize event sequences as timelines. In a storytelling context where the intent is to convey multiple narrative points, a richer set of timeline designs may be more appropriate than the narrow range that has been used for exploratory data analysis by the research community. Informed by a survey of 263 timelines, we present a design space for storytelling with timelines that balances expressiveness and effectiveness, identifying 14 design choices characterized by three dimensions: representation, scale, and layout. Twenty combinations of these choices are viable timeline designs that can be matched to different narrative points, while smooth animated transitions between narrative points allow for the presentation of a cohesive story, an important aspect of both interactive storytelling and data videos. We further validate this design space by realizing the full set of viable timeline designs and transitions in a proof-of-concept sandbox implementation that we used to produce seven example timeline stories. Ultimately, this work is intended to inform and inspire the design of future tools for storytelling with timelines.

Summary form only given. Computer-based visualization (vis) systems provide visual representations of datasets designed to help people carry out tasks more effectively. Visualization is suitable when there is a need to augment human capabilities rather than replace people with computational decision-making methods. The design space of possible vis idioms is huge, and includes the considerations of both how to create and how to interact with visual representations. Vis design is full of trade-offs, and most possibilities in the design space are ineffective for a particular task, so validating the effectiveness of a design is both necessary and difficult. Vis designers must take into account three very different kinds of resource limitations: those of computers, of humans, and of displays. Vis usage can be analyzed in terms of why the user needs it, what data is shown, and how the idiom is designed. I will discuss this framework for analyzing the design of visualization systems.

The energy performance of large building portfolios is challenging to analyze and monitor, as current analysis tools are not scalable or they present derived and aggregated data at too coarse of a level. We conducted a visualization design study, beginning with a thorough work domain analysis and a characterization of data and task abstractions. We describe generalizable visual encoding design choices for time-oriented data framed in terms of matches and mismatches, as well as considerations for workflow design. Our designs address several research questions pertaining to scalability, view coordination, and the inappropriateness of line charts for derived and aggregated data due to a combination of data semantics and domain convention. We also present guidelines relating to familiarity and trust, as well as methodological considerations for visualization design studies. Our designs were adopted by our collaborators and incorporated into the design of an energy analysis software application that will be deployed to tens of thousands of energy workers in their client base.

We present TimeLineCurator, a browser-based authoring tool that automatically extracts event data from temporal references in unstructured text documents using natural language processing and encodes them along a visual timeline. Our goal is to facilitate the timeline creation process for journalists and others who tell temporal stories online. Current solutions involve manually extracting and formatting event data from source documents, a process that tends to be tedious and error prone. With TimeLineCurator, a prospective timeline author can quickly identify the extent of time encompassed by a document, as well as the distribution of events occurring along this timeline. Authors can speculatively browse possible documents to quickly determine whether they are appropriate sources of timeline material. TimeLineCurator provides controls for curating and editing events on a timeline, the ability to combine timelines from multiple source documents, and export curated timelines for online deployment. We evaluate TimeLineCurator through a benchmark comparison of entity extraction error against a manual timeline curation process, a preliminary evaluation of the user experience of timeline authoring, a brief qualitative analysis of its visual output, and a discussion of prospective use cases suggested by members of the target author communities following its deployment.

Abstract A multiplex network has links of different types, allowing it to express many overlapping types of relationships. A core task in network analysis is to evaluate and understand group cohesion; that is, to explain why groups of elements belong together based on the underlying structure of the network. We present Detangler, a system that supports visual analysis of group cohesion in multiplex networks through dual linked views. These views feature new data abstractions derived from the original multiplex network: the substrate network and the catalyst network. We contribute two novel techniques that allow the user to analyze the complex structure of the multiplex network without the extreme visual clutter that would result from simply showing it directly. The harmonized layout visual encoding technique provides spatial stability between the substrate and catalyst views. The pivot brushing interaction technique supports linked highlighting between the views based on computations in the underlying multiplex network to leapfrog between subsets of catalysts and substrates. We present results from the motivating application domain of annotated news documents with a usage scenario and preliminary expert feedback. A second usage scenario presents group cohesion analysis of the social network of the early American independence movement.

For an investigative journalist, a large collection of documents obtained from a Freedom of Information Act request or a leak is both a blessing and a curse: such material may contain multiple newsworthy stories, but it can be difficult and time consuming to find relevant documents. Standard text search is useful, but even if the search target is known it may not be possible to formulate an effective query. In addition, summarization is an important non-search task. We present Overview, an application for the systematic analysis of large document collections based on document clustering, visualization, and tagging. This work contributes to the small set of design studies which evaluate a visualization system "in the wild", and we report on six case studies where Overview was voluntarily used by self-initiated journalists to produce published stories. We find that the frequently-used language of "exploring" a document collection is both too vague and too narrow to capture how journalists actually used our application. Our iterative process, including multiple rounds of deployment and observations of real world usage, led to a much more specific characterization of tasks. We analyze and justify the visual encoding and interaction techniques used in Overview's design with respect to our final task abstractions, and propose generalizable lessons for visualization design methodology.

The considerable previous work characterizing visualization usage has focused on low-level tasks or interactions and high-level tasks, leaving a gap between them that is not addressed. This gap leads to a lack of distinction between the ends and means of a task, limiting the potential for rigorous analysis. We contribute a multi-level typology of visualization tasks to address this gap, distinguishing why and how a visualization task is performed, as well as what the task inputs and outputs are. Our typology allows complex tasks to be expressed as sequences of interdependent simpler tasks, resulting in concise and flexible descriptions for tasks of varying complexity and scope. It provides abstract rather than domain-specific descriptions of tasks, so that useful comparisons can be made between visualization systems targeted at different application domains. This descriptive power supports a level of analysis required for the generation of new designs, by guiding the translation of domain-specific problems into abstract tasks, and for the qualitative evaluation of visualization usage. We demonstrate the benefits of our approach in a detailed case study, comparing task descriptions from our typology to those derived from related work. We also discuss the similarities and differences between our typology and over two dozen extant classification systems and theoretical frameworks from the literatures of visualization, human-computer interaction, information retrieval, communications, and cartography.

Scientists use DNA sequence differences between an individual's genome and a standard reference genome to study the genetic basis of disease. Such differences are called sequence variants, and determining their impact in the cell is difficult because it requires reasoning about both the type and location of the variant across several levels of biological context. In this design study, we worked with four analysts to design a visualization tool supporting variant impact assessment for three different tasks. We contribute data and task abstractions for the problem of variant impact assessment, and the carefully justified design and implementation of the Variant View tool. Variant View features an information-dense visual encoding that provides maximal information at the overview level, in contrast to the extensive navigation required by currently-prevalent genome browsers. We provide initial evidence that the tool simplified and accelerated workflows for these three tasks through three case studies. Finally, we reflect on the lessons learned in creating and refining data and task abstractions that allow for concise overviews of sprawling information spaces that can reduce or remove the need for the memory-intensive use of navigation.

To verify cluster separation in high-dimensional data, analysts often reduce the data with a dimension reduction (DR) technique, and then visualize it with 2D Scatterplots, interactive 3D Scatterplots, or Scatterplot Matrices (SPLOMs). With the goal of providing guidance between these visual encoding choices, we conducted an empirical data study in which two human coders manually inspected a broad set of 816 scatterplots derived from 75 datasets, 4 DR techniques, and the 3 previously mentioned scatterplot techniques. Each coder scored all color-coded classes in each scatterplot in terms of their separability from other classes. We analyze the resulting quantitative data with a heatmap approach, and qualitatively discuss interesting scatterplot examples. Our findings reveal that 2D scatterplots are often 'good enough', that is, neither SPLOM nor interactive 3D adds notably more cluster separability with the chosen DR technique. If 2D is not good enough, the most promising approach is to use an alternative DR technique in 2D. Beyond that, SPLOM occasionally adds additional value, and interactive 3D rarely helps but often hurts in terms of poorer class separation and usability. We summarize these results as a workflow model and implications for design. Our results offer guidance to analysts during the DR exploration process.

Rating interfaces are widely used on the Internet to elicit people's opinions. Little is known, however, about the effectiveness of these interfaces and their design space is relatively unexplored. We provide a taxonomy for the design space by identifying two axes: Measurement Scale for absolute rating vs. relative ranking, and Recall Support for the amount of information provided about previously recorded opinions. We present an exploration of the design space through iterative prototyping of three alternative interfaces and their evaluation. Among many findings, the study showed that users do take advantage of recall support in interfaces, preferring those that provide it. Moreover, we found that designing ranking systems is challenging; there may be a mismatch between a ranking interface that forces people to specify a total ordering for a set of items, and their mental model that some items are not directly comparable to each other.

Abstract In this design study, we present an analysis and abstraction of the data and task in the domain of fisheries management, and the design and implementation of the Vismon tool to address the identified requirements. Vismon was designed to support sophisticated data analysis of simulation results by managers who are highly knowledgeable about the fisheries domain but not experts in simulation software and statistical data analysis. The previous workflow required the scientists who built the models to spearhead the analysis process. The features of Vismon include sensitivity analysis, comprehensive and global trade‐offs analysis, and a staged approach to the visualization of the uncertainty of the underlying simulation model. The tool was iteratively refined through a multi‐year engagement with fisheries scientists with a two‐phase approach, where an initial diverging experimentation phase to test many alternatives was followed by a converging phase where the set of multiple linked views that proved effective were integrated together in a useable way. Several fisheries scientists have used Vismon to communicate with policy makers, and it is scheduled for deployment to policy makers in Alaska.

Abstract We provide two contributions, a taxonomy of visual cluster separation factors in scatterplots, and an in‐depth qualitative evaluation of two recently proposed and validated separation measures. We initially intended to use these measures to provide guidance for the use of dimension reduction (DR) techniques and visual encoding (VE) choices, but found that they failed to produce reliable results. To understand why, we conducted a systematic qualitative data study covering a broad collection of 75 real and synthetic high‐dimensional datasets, four DR techniques, and three scatterplot‐based visual encodings. Two authors visually inspected over 800 plots to determine whether or not the measures created plausible results. We found that they failed in over half the cases overall, and in over two‐thirds of the cases involving real datasets. Using open and axial coding of failure reasons and separability characteristics, we generated a taxonomy of visual cluster separability factors. We iteratively refined its explanatory clarity and power by mapping the studied datasets and success and failure ranges of the measures onto the factor axes. Our taxonomy has four categories, ordered by their ability to influence successors: Scale, Point Distance, Shape, and Position. Each category is split into Within‐Cluster factors such as density, curvature, isotropy, and clumpiness, and Between‐Cluster factors that arise from the variance of these properties, culminating in the overarching factor of class separation. The resulting taxonomy can be used to guide the design and the evaluation of cluster separation measures.

We present a network visualization design study focused on supporting automotive engineers who need to specify and optimize traffic patterns for in-car communication networks. The task and data abstractions that we derived support actively making changes to an overlay network, where logical communication specifications must be mapped to an underlying physical network. These abstractions are very different from the dominant use case in visual network analysis, namely identifying clusters and central nodes, that stems from the domain of social network analysis. Our visualization tool RelEx was created and iteratively refined through a full user-centered design process that included a full problem characterization phase before tool design began, paper prototyping, iterative refinement in close collaboration with expert users for formative evaluation, deployment in the field with real analysts using their own data, usability testing with non-expert users, and summative evaluation at the end of the deployment. In the summative post-deployment study, which entailed domain experts using the tool over several weeks in their daily practice, we documented many examples where the use of RelEx simplified or sped up their work compared to previous practices.

Design studies are an increasingly popular form of problem-driven visualization research, yet there is little guidance available about how to do them effectively. In this paper we reflect on our combined experience of conducting twenty-one design studies, as well as reading and reviewing many more, and on an extensive literature review of other field work methods and methodologies. Based on this foundation we provide definitions, propose a methodological framework, and provide practical guidance for conducting design studies. We define a design study as a project in which visualization researchers analyze a specific real-world problem faced by domain experts, design a visualization system that supports solving this problem, validate the design, and reflect about lessons learned in order to refine visualization design guidelines. We characterize two axes - a task clarity axis from fuzzy to crisp and an information location axis from the domain expert's head to the computer - and use these axes to reason about design study contributions, their suitability, and uniqueness from other approaches. The proposed methodological framework consists of 9 stages: learn, winnow, cast, discover, design, implement, deploy, reflect, and write. For each stage we provide practical guidance and outline potential pitfalls. We also conducted an extensive literature survey of related methodological approaches that involve a significant amount of qualitative field work, and compare design study methodology to that of ethnography, grounded theory, and action research.

Many graph visualization systems use graph hierarchies to organize a large input graph into logical components. These approaches detect features globally in the data and place these features inside levels of a hierarchy. However, this feature detection is a global process and does not consider nodes of the graph near a feature of interest. TugGraph is a system for exploring paths and proximity around nodes and subgraphs in a graph. The approach modifies a pre-existing hierarchy in order to see how a node or subgraph of interest extends out into the larger graph. It is guaranteed to create path-preserving hierarchies, so that the abstraction shown is meaningful with respect to the underlying structure of the graph. The system works well on graphs of hundreds of thousands of nodes and millions of edges. TugGraph is able to present views of this proximal information in the context of the entire graph in seconds, and does not require a layout of the full graph as input.

Abstract Biologists pioneering the new field of comparative functional genomics attempt to infer the mechanisms of gene regulation by looking for similarities and differences of gene activity over time across multiple species. They use three kinds of data: functional data such as gene activity measurements, pathway data that represent a series of reactions within a cellular process, and phylogenetic relationship data that describe the relatedness of species. No existing visualization tool can visually encode the biologically interesting relationships between multiple pathways, multiple genes, and multiple species. We tackle the challenge of visualizing all aspects of this comparative functional genomics dataset with a new interactive tool called Pathline. In addition to the overall characterization of the problem and design of Pathline, our contributions include two new visual encoding techniques. One is a new method for linearizing metabolic pathways that provides appropriate topological information and supports the comparison of quantitative data along the pathway. The second is the curvemap view, a depiction of time series data for comparison of gene activity and metabolite levels across multiple species. Pathline was developed in close collaboration with a team of genomic scientists. We validate our approach with case studies of the biologists’ use of Pathline and report on how they use the tool to confirm existing findings and to discover new scientific insights.

Cells in an organism share the same genetic information in their DNA, but have very different forms and behavior because of the selective expression of subsets of their genes. The widely used approach of measuring gene expression over time from a tissue sample using techniques such as microarrays or sequencing do not provide information about the spatial position within the tissue where these genes are expressed. In contrast, we are working with biologists who use techniques that measure gene expression in every individual cell of entire fruitfly embryos over an hour of their development, and do so for multiple closely-related subspecies of Drosophila. These scientists are faced with the challenge of integrating temporal gene expression data with the spatial location of cells and, moreover, comparing this data across multiple related species. We have worked with these biologists over the past two years to develop MulteeSum, a visualization system that supports inspection and curation of data sets showing gene expression over time, in conjunction with the spatial location of the cells where the genes are expressed--it is the first tool to support comparisons across multiple such data sets. MulteeSum is part of a general and flexible framework we developed with our collaborators that is built around multiple summaries for each cell, allowing the biologists to explore the results of computations that mix spatial information, gene expression measurements over time, and data from multiple related species or organisms. We justify our design decisions based on specific descriptions of the analysis needs of our collaborators, and provide anecdotal evidence of the efficacy of MulteeSum through a series of case studies.

Many graph visualization systems use graph hierarchies to organize a large input graph into logical components. These approaches detect features globally in the data and place these features inside levels of a hierarchy. However, this feature detection is a global process and does not consider nodes of the graph near a feature of interest. TugGraph is a system for exploring paths and proximity around nodes and subgraphs in a graph. The approach modifies a pre-existing hierarchy in order to see how a node or subgraph of interest extends out into the larger graph. It is guaranteed to create path-preserving hierarchies, so that the abstraction shown is meaningful with respect to the structure of the graph. The system works well on graphs of hundreds of thousands of nodes and millions of edges. TugGraph is able to present views of this proximal information in the context of the entire graph in seconds, and does not require a layout of the full graph as input.

We present Glimmer, a new multilevel algorithm for multidimensional scaling designed to exploit modern graphics processing unit (GPU) hardware. We also present GPU-SF, a parallel, force-based subsystem used by Glimmer. Glimmer organizes input into a hierarchy of levels and recursively applies GPU-SF to combine and refine the levels. The multilevel nature of the algorithm makes local minima less likely while the GPU parallelism improves speed of computation. We propose a robust termination condition for GPU-SF based on a filtered approximation of the normalized stress function. We demonstrate the benefits of Glimmer in terms of speed, normalized stress, and visual quality against several previous algorithms for a range of synthetic and real benchmark datasets. We also show that the performance of Glimmer on GPUs is substantially faster than a CPU implementation of the same algorithm.

In the field of comparative genomics, scientists seek to answer questions about evolution and genomic function by comparing the genomes of species to find regions of shared sequences. Conserved syntenic blocks are an important biological data abstraction for indicating regions of shared sequences. The goal of this work is to show multiple types of relationships at multiple scales in a way that is visually comprehensible in accordance with known perceptual principles. We present a task analysis for this domain where the fundamental questions asked by biologists can be understood by a characterization of relationships into the four types of proximity/location, size, orientation, and similarity/strength, and the four scales of genome, chromosome, block, and genomic feature. We also propose a new taxonomy of the design space for visually encoding conservation data. We present MizBee, a multiscale synteny browser with the unique property of providing interactive side-by-side views of the data across the range of scales supporting exploration of all of these relationship types. We conclude with case studies from two biologists who used MizBee to augment their previous automatic analysis work flow, providing anecdotal evidence about the efficacy oft he system for the visualization of syntenic data, the analysis of conservation relationships, and the communication of scientific insights.

We present a nested model for the visualization design process with four layers: characterize the problem domain, abstract into operations on data types, design visual encoding and interaction techniques, and create algorithms to execute techniques efficiently. The output from a level above is input to the level below, bringing attention to the design challenge that an upstream error inevitably cascades to all downstream levels. This model provides prescriptive guidance for determining appropriate evaluation approaches by identifying threats to validity unique to each level. We call attention to specific steps in the design and evaluation process that are often given short shrift. We also provide three recommendations motivated by this model:authors should distinguish between these levels when claiming contributions at more than one of them, authors should explicitly state upstream assumptions at levels above the focus of a paper, and visualization venues should accept more papers on domain characterization.

We present LiveRAC, a visualization system that supports the analysis of large collections of system management time-series data consisting of hundreds of parameters across thousands of network devices. LiveRAC provides high information density using a reorderable matrix of charts, with semantic zooming adapting each chart's visual representation to the available space. LiveRAC allows side-by-side visual comparison of arbitrary groupings of devices and parameters at multiple levels of detail. A staged design and development process culminated in the deployment of LiveRAC in a production environment. We conducted an informal longitudinal evaluation of LiveRAC to better understand which proposed visualization techniques were most useful in the target environment.

Several previous systems allow users to interactively explore a large input graph through cuts of a superimposed hierarchy. This hierarchy is often created using clustering algorithms or topological features present in the graph. However, many graphs have domain-specific attributes associated with the nodes and edges, which could be used to create many possible hierarchies providing unique views of the input graph. GrouseFlocks is a system for the exploration of this graph hierarchy space. By allowing users to see several different possible hierarchies on the same graph, the system helps users investigate graph hierarchy space instead of a single fixed hierarchy. GrouseFlocks provides a simple set of operations so that users can create and modify their graph hierarchies based on selections. These selections can be made manually or based on patterns in the attribute data provided with the graph. It provides feedback to the user within seconds, allowing interactive exploration of this space.

Systems biologists use interaction graphs to model the behavior of biological systems at the molecular level. In an iterative process, such biologists observe the reactions of living cells under various experimental conditions, view the results in the context of the interaction graph, and then propose changes to the graph model. These graphs ser ve as a form of dynamic knowledge representation of the biological system being studied and evolve as new insight is gained from the experimental data. While numerous graph layout and drawing packages are available, these tools did not fully meet the needs of our immunologist collaborators. In this paper, we describe the data information display needs of these immunologists and translate them into design decisions. These decisions led us to create Cerebral, a system that uses a biologically guided graph layout and incorporates experimental data directly into the graph display. Small multiple views of different experimental conditions and a data-driven parallel coordinates view enable correlations between experimental conditions to be analyzed at the same time that the data is viewed in the graph context. This combination of coordinated views allows the biologist to view the data from many different perspectives simultaneously. To illustrate the typical analysis tasks performed, we analyze two datasets using Cerebral. Based on feedback from our collaborators we conclude that Cerebral is a valuable tool for analyzing experimental data in the context of an interaction graph model.

Grouse is a feature-based approach to steerable exploration of a graph and an associated hierarchy. Steerability allows exploration to begin immediately, rather than requiring a costly layout of the entire graph as an initial step. In a feature-based approach, the subgraph inside a metanode of the graph hierarchy is laid out with a well- chosen algorithm appropriate for its topological structure. Grouse preserves the input hierarchy, which provides meaningful information to the user when its metanodes correspond to features of interest. When a metanode in the hierarchy is opened, a limited number of metanodes are laid out again along the path between the opened node and the root. We demonstrate the effectiveness of Grouse on datasets from IMDB, the Internet Movie Database, where nodes are actors and cliques represent movies. The combination of feature-based layout and limited relayout computation does not fragment features in the hierarchy and improves the number of levels in the hierarchy that can be seen at once over previous approaches.

UNLABELLED: Cerebral (Cell Region-Based Rendering And Layout) is an open-source Java plugin for the Cytoscape biomolecular interaction viewer. Given an interaction network and subcellular localization annotation, Cerebral automatically generates a view of the network in the style of traditional pathway diagrams, providing an intuitive interface for the exploration of a biological pathway or system. The molecules are separated into layers according to their subcellular localization. Potential products or outcomes of the pathway can be shown at the bottom of the view, clustered according to any molecular attribute data-protein function-for example. Cerebral scales well to networks containing thousands of nodes. AVAILABILITY: http://www.pathogenomics.ca/cerebral

We describe TopoLayout, a feature-based, multilevel algorithm that draws undirected graphs based on the topological features they contain. Topological features are detected recursively inside the graph, and their subgraphs are collapsed into single nodes, forming a graph hierarchy. Each feature is drawn with an algorithm tuned for its topology. As would be expected from a feature-based approach, the runtime and visual quality of TopoLayout depends on the number and types of topological features present in the graph. We show experimental results comparing speed and visual quality for TopoLayout against four other multilevel algorithms on a variety of data sets with a range of connectivities and sizes. TopoLayout frequently improves the results in terms of speed and visual quality on these data sets.

In interfaces that provide multiple visual information resolutions (VIR), low-VIR overviews typically sacrifice visual details for display capacity, with the assumption that users can select regions of interest to examine at higher VIRs. Designers can create low-VIRs based on multi-level structure inherent in the data, but have little guidance with single-level data. To better guide design tradeoff between display capacity and visual target perceivability, we looked at overview use in two multiple-VIR interfaces with high-VIR displays either embedded within, or separate from, the overviews. We studied two visual requirements for effective overview and found that participants would reliably use the low-VIR overviews only when the visual targets were simple and had small visual spans. Otherwise, at least 20% chose to use the high-VIR view exclusively. Surprisingly, neither of the multiple-VIR interfaces provided performance benefits when compared to using the high-VIR view alone. However, we did observe benefits in providing side-by-side comparisons for target matching. We conjecture that the high cognitive load of multiple-VIR interface interactions, whether real or perceived, is a more considerable barrier to their effective use than was previously considered.

Spatializations represent non-spatial data using a spatial layout similar to a map. We present an experiment comparing different visual representations of spatialized data, to determine which representations are best for a non-trivial search and point estimation task. Primarily, we compare point-based displays to 2D and 3D information landscapes. We also compare a colour (hue) scale to a grey (lightness) scale. For the task we studied, point-based spatializations were far superior to landscapes, and 2D landscapes were superior to 3D landscapes. Little or no benefit was found for redundantly encoding data using colour or greyscale combined with landscape height. 3D landscapes with no colour scale (height-only) were particularly slow and inaccurate. A colour scale was found to be better than a greyscale for all display types, but a greyscale was helpful compared to height-only. These results suggest that point-based spatializations should be chosen over landscape representations, at least for tasks involving only point data itself rather than derived information about the data space.

We present a study that evaluates conventional Pan and Zoom Navigation and Rubber Sheet Navigation, a rectilinear Focus+Context technique. Each of the two navigation techniques was evaluated both with and without an overview. All interfaces guaranteed that regions of interest would remain visible, at least as a compressed landmark, independent of navigation actions. Interfaces implementing these techniques were used by 40 subjects to perform a task that involved navigating a large hierarchical tree dataset and making topological comparisons between nodes in the tree. Our results show that Pan and Zoom Navigation was significantly faster and required less mental effort than Rubber Sheet Navigation, independent of the presence or absence of an overview. Also, overviews did not appear to improve performance, but were still perceived as beneficial by users. We discuss the implications of our task and guaranteed visibility on the results and the limitations of our study, and we propose preliminary design guidelines and recommendations for future work.

Quasi-trees, namely graphs with tree-like structure, appear in many application domains, including bioinformatics and computer networks. Our new SPF approach exploits the structure of these graphs with a two-level approach to drawing, where the graph is decomposed into a tree of biconnected components. The low-level biconnected components are drawn with a force-directed approach that uses a spanning tree skeleton as a starting point for the layout. The higher-level structure of the graph is a true tree with meta-nodes of variable size that contain each biconnected component. That tree is drawn with a new area-aware variant of a tree drawing algorithm that handles high-degree nodes gracefully, at the cost of allowing edge-node overlaps. SPF performs an order of magnitude faster than the best previous approaches, while producing drawings of commensurate or improved quality.

We present the first scalable algorithm that supports the composition of successive rectilinear deformations. Earlier systems that provided stretch and squish navigation could only handle small datasets. More recent work featuring rubber sheet navigation for large datasets has focused on rendering and on application-specific issues. However, no algorithm has yet been presented for carrying out such navigation methods; our paper addresses this problem. For maximum flexibility with large datasets, a stretch and squish navigation algorithm should allow for millions of potentially deformable regions. However, typical usage only changes the extents of a small subset k of these n regions at a time. The challenge is to avoid computations that are linear in n, because a single deformation can affect the absolute screen-space location of every deformable region. We provide an O(klogn) algorithm that supports any application that can lay out a dataset on a generic grid, and show an implementation that allows navigation of trees and gene sequences with millions of items in sub-millisecond time.

The TreeJuxtaposer system [MGT*03] allowed visual comparison of large trees with guaranteed visibility of landmarks and Focus+Context navigation. While that system allowed exploration and comparison of larger datasets than previous work, it was limited to a single tree of 775,000 nodes by a large memory footprint. In this paper, we describe the theoretical limitations to TreeJuxtaposer's architecture that severely restrict its scalability. We provide two scalable, robust solutions to these limitations: TJC and TJC-Q. TJC is a system that supports browsing trees up to 15 million nodes by exploiting leading-edge graphics hardware while TJC-Q allows browsing trees up to 5 million nodes on commodity platforms. Both of these systems use a fast new algorithm for drawing and culling and benefit from a complete redesign of all data structures for more efficient memory usage and reduced preprocessing time.

HREE papers in this issue of TVCG are expanded versions of ones presented at InfoVis 2004. These examples of the cutting edge of information visualization research show the maturing of the field, using novel methods for designing, developing, and evaluating tools that help users solve real problems on large and complex data sets, rather than just creating interesting displays. “Knowledge Precepts for Design and Evaluation of Information Visualizations” by Robert A. Amar and John T. Stasko won the InfoVis 2004 Best Paper Award. In this paper, the authors argue that efforts to date to design effective visualization tools often fail because they concentrate mostly on presenting data, as opposed to supporting analysis. Limitations of existing systems are grouped into two gaps in the analysis process; the Worldview Gap consists of the difference between what is shown and what is needed to make decisions, while the Rationale Gap consists of the difference between relationships viewed and the certainty and utility of the relationship. Analysis of existing tools using this framework reveals many ways in which techniques can be improved to better support high-level analysis. “An Insight-Based Methodology for Evaluating Bioinformatics Visualizations” by Purvi Saraiya, Chris North, and Karen Duca presents a novel method for assessing and comparing the effectiveness of visualization tools, namely, the number and type of insights discovered. The authors define an insight as a unit of discovery and identify several attributes and categories of insights. They then report the results from controlled experiments used to evaluate five software tools for visual exploration of microarray data. The results shed many insights into what makes a visualization tool effective.

Three papers in this issue of TVCG are expanded versions of ones presented at the IEEE Symposium on Information Visualization (InfoVis) 2003. These examples convey the breadth of work in the InfoVis community, attacking practically motivated problems from sophisticated mathematical and scientific perspectives. They demonstrate an interaction technique for navigation, a mathematically well-founded approach to the difficult problem of dimensionality reduction, and an evaluation of a particular channel of visual perception. These are briefly summarized.

Must we continue to define a difference between information and scientific visualization? Scientific visualization evolved first in the late 1980’s while information visualization matured in the mid-1990’s. Scientific visualization is frequently considered to focus on the visual display of spatial data associated with scientific processes such as the bonding of molecules in computational chemistry. Information visualization examines developing visual metaphors for non-inherently spatial data such as the exploration of text-based document databases. This panel examines the effective, productive, and perhaps confusing tension between these subfields of visualization by highlighting the following issues:

Structural comparison of large trees is a difficult task that is only partially supported by current visualization techniques, which are mainly designed for browsing. We present TreeJuxtaposer, a system designed to support the comparison task for large trees of several hundred thousand nodes. We introduce the idea of "guaranteed visibility", where highlighted areas are treated as landmarks that must remain visually apparent at all times. We propose a new methodology for detailed structural comparison between two trees and provide a new nearly-linear algorithm for computing the best corresponding node from one tree to another. In addition, we present a new rectilinear Focus+Context technique for navigation that is well suited to the dynamic linking of side-by-side views while guaranteeing landmark visibility and constant frame rates. These three contributions result in a system delivering a fluid exploration experience that scales both in the size of the dataset and the number of pixels in the display. We have based the design decisions for our system on the needs of a target audience of biologists who must understand the structural details of many phylogenetic, or evolutionary, trees. Our tool is also useful in many other application domains where tree comparison is needed, ranging from network management to call graph optimization to genealogy.

No abstract available.

No abstract available.