IEEE VIS Publication Dataset

Spatiotemporal analysis of sensor logs is a challenging research field due to three facts: a) traditional two-dimensional maps do not support multiple events to occur at the same spatial location, b) three-dimensional solutions introduce ambiguity and are hard to navigate, and c) map distortions to solve the overlap problem are unfamiliar to most users. This paper introduces a novel approach to represent spatial data changing over time by plotting a number of non-overlapping pixels, close to the sensor positions in a map. Thereby, we encode the amount of time that a subject spent at a particular sensor to the number of plotted pixels. Color is used in a twofold manner; while distinct colors distinguish between sensor nodes in different regions, the colors' intensity is used as an indicator to the temporal property of the subjects' activity. The resulting visualization technique, called growth ring maps, enables users to find similarities and extract patterns of interest in spatiotemporal data by using humans' perceptual abilities. We demonstrate the newly introduced technique on a dataset that shows the behavior of healthy and Alzheimer transgenic, male and female mice. We motivate the new technique by showing that the temporal analysis based on hierarchical clustering and the spatial analysis based on transition matrices only reveal limited results. Results and findings are cross-validated using multidimensional scaling. While the focus of this paper is to apply our visualization for monitoring animal behavior, the technique is also applicable for analyzing data, such as packet tracing, geographic monitoring of sales development, or mobile phone capacity planning.

Trees and graphs are relevant to many online tasks such as visualizing social networks, product catalogs, educational portals, digital libraries, the semantic web, concept maps and personalized information management. SpicyNodes is an information-visualization technology that builds upon existing research on radial tree layouts and graph structures. Users can browse a tree, clicking from node to node, as well as successively viewing a node, immediately related nodes and the path back to the ldquohomerdquo nodes. SpicyNodes' layout algorithms maintain balanced layouts using a hybrid mixture of a geometric layout (a succession of spanning radial trees) and force-directed layouts to minimize overlapping nodes, plus several other improvements over prior art. It provides XML-based API and GUI authoring tools. The goal of the SpicyNodes project is to implement familiar principles of radial maps and focus+context with an attractive and inviting look and feel in an open system that is accessible to virtually any Internet user.

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.

A great corpus of studies reports empirical evidence of how information visualization supports comprehension and analysis of data. The benefits of visualization for synchronous group knowledge work, however, have not been addressed extensively. Anecdotal evidence and use cases illustrate the benefits of synchronous collaborative information visualization, but very few empirical studies have rigorously examined the impact of visualization on group knowledge work. We have consequently designed and conducted an experiment in which we have analyzed the impact of visualization on knowledge sharing in situated work groups. Our experimental study consists of evaluating the performance of 131 subjects (all experienced managers) in groups of 5 (for a total of 26 groups), working together on a real-life knowledge sharing task. We compare (1) the control condition (no visualization provided), with two visualization supports: (2) optimal and (3) suboptimal visualization (based on a previous survey). The facilitator of each group was asked to populate the provided interactive visual template with insights from the group, and to organize the contributions according to the group consensus. We have evaluated the results through both objective and subjective measures. Our statistical analysis clearly shows that interactive visualization has a statistically significant, objective and positive impact on the outcomes of knowledge sharing, but that the subjects seem not to be aware of this. In particular, groups supported by visualization achieved higher productivity, higher quality of outcome and greater knowledge gains. No statistically significant results could be found between an optimal and a suboptimal visualization though (as classified by the pre-experiment survey). Subjects also did not seem to be aware of the benefits that the visualizations provided as no difference between the visualization and the control conditions was found for the self-reported measures of satisfaction a- - nd participation. An implication of our study for information visualization applications is to extend them by using real-time group annotation functionalities that aid in the group sense making process of the represented data.

Modern programmable GPUs represent a vast potential in terms of performance and visual flexibility for information visualization research, but surprisingly few applications even begin to utilize this potential. In this paper, we conjecture that this may be due to the mismatch between the high-level abstract data types commonly visualized in our field, and the low-level floating-point model supported by current GPU shader languages. To help remedy this situation, we present a refinement of the traditional information visualization pipeline that is amenable to implementation using GPU shaders. The refinement consists of a final image-space step in the pipeline where the multivariate data of the visualization is sampled in the resolution of the current view. To concretize the theoretical aspects of this work, we also present a visual programming environment for constructing visualization shaders using a simple drag-and-drop interface. Finally, we give some examples of the use of shaders for well-known visualization techniques.

In serial computation, program profiling is often helpful for optimization of key sections of code. When moving to parallel computation, not only does the code execution need to be considered but also communication between the different processes which can induce delays that are detrimental to performance. As the number of processes increases, so does the impact of the communication delays on performance. For large-scale parallel applications, it is critical to understand how the communication impacts performance in order to make the code more efficient. There are several tools available for visualizing program execution and communications on parallel systems. These tools generally provide either views which statistically summarize the entire program execution or process-centric views. However, process-centric visualizations do not scale well as the number of processes gets very large. In particular, the most common representation of parallel processes is a Gantt chart with a row for each process. As the number of processes increases, these charts can become difficult to work with and can even exceed screen resolution. We propose a new visualization approach that affords more scalability and then demonstrate it on systems running with up to 16,384 processes.

Social photos, which are taken during family events or parties, represent individuals or groups of people. We show in this paper how a Hasse diagram is an efficient visualization strategy for eliciting different groups and navigating through them. However, we do not limit this strategy to these traditional uses. Instead we show how it can also be used for assisting in indexing new photos. Indexing consists of identifying the event and people in photos. It is an integral phase that takes place before searching and sharing. In our method we use existing indexed photos to index new photos. This is performed through a manual drag and drop procedure followed by a content fusion process that we call 'propagation'. At the core of this process is the necessity to organize and visualize the photos that will be used for indexing in a manner that is easily recognizable and accessible by the user. In this respect we make use of an object Galois sub-hierarchy and display it using a Hasse diagram. The need for an incremental display that maintains the user's mental map also leads us to propose a novel way of building the Hasse diagram. To validate the approach, we present some tests conducted with a sample of users that confirm the interest of this organization, visualization and indexation approach. Finally, we conclude by considering scalability, the possibility to extract social networks and automatically create personalised albums.

Visualizing the intellectual structure of scientific domains using co-cited units such as references or authors has become a routine for domain analysis. In previous studies, paper-reference matrices are usually transformed into reference-reference matrices to obtain co-citation relationships, which are then visualized in different representations, typically as node-link networks, to represent the intellectual structures of scientific domains. Such network visualizations sometimes contain tightly knit components, which make visual analysis of the intellectual structure a challenging task. In this study, we propose a new approach to reveal co-citation relationships. Instead of using a reference-reference matrix, we directly use the original paper-reference matrix as the information source, and transform the paper-reference matrix into an FP-tree and visualize it in a Java-based prototype system. We demonstrate the usefulness of our approach through visual analyses of the intellectual structure of two domains: information visualization and Sloan Digital Sky Survey (SDSS). The results show that our visualization not only retains the major information of co-citation relationships, but also reveals more detailed sub-structures of tightly knit clusters than a conventional node-link network visualization.

Visual analytics has become an important tool for gaining insight on large and complex collections of data. Numerous statistical tools and data transformations, such as projections, binning and clustering, have been coupled with visualization to help analysts understand data better and faster. However, data is inherently uncertain, due to error, noise or unreliable sources. When making decisions based on uncertain data, it is important to quantify and present to the analyst both the aggregated uncertainty of the results and the impact of the sources of that uncertainty. In this paper, we present a new framework to support uncertainty in the visual analytics process, through statistic methods such as uncertainty modeling, propagation and aggregation. We show that data transformations, such as regression, principal component analysis and k-means clustering, can be adapted to account for uncertainty. This framework leads to better visualizations that improve the decision-making process and help analysts gain insight on the analytic process itself.

This paper presents a working graph analytics model that embraces the strengths of the traditional top-down and bottom-up approaches with a resilient crossover concept to exploit the vast middle-ground information overlooked by the two extreme analytical approaches. Our graph analytics model is co-developed by users and researchers, who carefully studied the functional requirements that reflect the critical thinking and interaction pattern of a real-life intelligence analyst. To evaluate the model, we implement a system prototype, known as GreenHornet, which allows our analysts to test the theory in practice, identify the technological and usage-related gaps in the model, and then adapt the new technology in their work space. The paper describes the implementation of GreenHornet and compares its strengths and weaknesses against the other prevailing models and tools.

Intelligence analysts in the areas of defense and homeland security are now faced with the difficult problem of discerning the relevant details amidst massive data stores. We propose a component-based visualization architecture that is built specifically to encourage the flexible exploration of geospatial event databases. The proposed system is designed to deploy on a variety of display layouts, from a single laptop screen to a multi-monitor tiled-display. By utilizing a combination of parallel coordinates, principal components plots, and other data views, analysts may reduce the dimensionality of a data set to its most salient features. Of particular value to our target applications are understanding correlations between data layers, both within a single view and across multiple views. Our proposed system aims to address the limited scalability associated with coordinated multiple views (CMVs) through the implementation of an efficient core application which is extensible by the end-user.

Radio frequency (RF) fingerprinting-based techniques for localization are a promising approach for ubiquitous positioning systems, particularly indoors. By finding unique fingerprints of RF signals received at different locations within a predefined area beforehand, whenever a similar fingerprint is subsequently seen again, the localization system will be able to infer a user's current location. However, developers of these systems face the problem of finding reliable RF fingerprints that are unique enough and adequately stable over time. We present a visual analytics system that enables developers of these localization systems to visually gain insight on whether their collected datasets and chosen fingerprint features have the necessary properties to enable a reliable RF fingerprinting-based localization system. The system was evaluated by testing and debugging an existing localization system.

Space- and time-referenced data published on the Web by general people can be viewed in a dual way: as independent spatio-temporal events and as trajectories of people in the geographical space. These two views suppose different approaches to the analysis, which can yield different kinds of valuable knowledge about places and about people. We define possible types of analysis tasks related to the two views of the data and present several analysis methods appropriate for these tasks. The methods are suited to large amounts of the data.

While many visualization tools exist that offer sophisticated functions for charting complex data, they still expect users to possess a high degree of expertise in wielding the tools to create an effective visualization. This poster presents Articulate, an attempt at a semi-automated visual analytic model that is guided by a conversational user interface. The goal is to relieve the user of the physical burden of having to directly craft a visualization through the manipulation of a complex user-interface, by instead being able to verbally articulate what the user wants to see, and then using natural language processing and heuristics to semi-automatically create a suitable visualization.

In this poster paper, we present BEADS, a high dimensional data cluster visualization by having a 2-D representation of shape and spread of the cluster. The Cluster Division component, the Bead Shape Identification and Cluster Shape Composition form the core of the system. BEADS visualization consists of a 2-D plot, standard 2-D shapes which are used as metaphors to represent corresponding high-dimensional shapes of beads. The final resulting images convey the relative placement of beads with respect to the cluster center, the shape of the beads. We give a textual summary of the beads and their 2-D placement on the Beads plot in tabular format along with the image.

Visual analytics tools provide powerful visual representations in order to support the sense-making process. In this process, analysts typically iterate through sequences of steps many times, varying parameters each time. Few visual analytics tools support this process well, nor do they provide support for visualizing and understanding the analysis process itself. To help analysts understand, explore, reference, and reuse their analysis process, we present a visual analytics system named CzSaw (See-Saw) that provides an editable and re-playable history navigation channel in addition to multiple visual representations of document collections and the entities within them (in a manner inspired by Jigsaw). Conventional history navigation tools range from basic undo and redo to branching timelines of user actions. In CzSaw's approach to this, first, user interactions are translated into a script language that drives the underlying scripting-driven propagation system. The latter allows analysts to edit analysis steps, and ultimately to program them. Second, on this base, we build both a history view showing progress and alternative paths, and a dependency graph showing the underlying logic of the analysis and dependency relations among the results of each step. These tools result in a visual model of the sense-making process, providing a way for analysts to visualize their analysis process, to reinterpret the problem, explore alternative paths, extract analysis patterns from existing history, and reuse them with other related analyses.

Visual exploration of multivariate data typically requires projection onto lower-dimensional representations. The number of possible representations grows rapidly with the number of dimensions, and manual exploration quickly becomes ineffective or even unfeasible. This paper proposes automatic analysis methods to extract potentially relevant visual structures from a set of candidate visualizations. Based on features, the visualizations are ranked in accordance with a specified user task. The user is provided with a manageable number of potentially useful candidate visualizations, which can be used as a starting point for interactive data analysis. This can effectively ease the task of finding truly useful visualizations and potentially speed up the data exploration task. In this paper, we present ranking measures for class-based as well as non class-based Scatterplots and Parallel Coordinates visualizations. The proposed analysis methods are evaluated on different datasets.

Processing is a very powerful visualization language which combines software concepts with principles of visual form and interaction. Artists, designers and architects use it but it is also a very effective programming language in the area of visual analytics. In the following contribution Processing is utilized in order to visually analyze data provided by IEEE VAST 2009 Mini Challenge Badge and Network Traffic. The applied process is iterative and each stage of the analytical reasoning process is accompanied by customized software development. The visual model, the process and the technical solution will be briefly introduced.

In visual analytics, menu systems are commonly adopted as supporting tools because of the complex nature of data. However, it is still unknown how much the interaction implicit to the interface impacts the performance of visual analysis. To show the effectiveness of two interface tools, one a floating text-based menu (Floating Menu) and the other a more interactive iconic tool (Interactive-Icon), we evaluated the use and human performance of both tools within one highly interactive visual analytics system. We asked participants to answer similarly constructed, straightforward questions in a genomic visualization, first with one tool, and then the other. During task performance we tracked completion times, task errors, and captured coarse-grained interactive behaviors. Based on the participants accuracy, speed, behaviors and post-task qualitative feedback, we observed that although the Interactive-Icon tool supports continuous interactions, task-oriented user evaluation did not find a significant difference between the two tools because there is a familiarity effect on the performance of solving the task questions with using Floating-Menu interface tool.

During visual analysis, users must often connect insights discovered at various points of time. This process is often called ldquoconnecting the dots.rdquo When analysts interactively explore complex datasets over multiple sessions, they may uncover a large number of findings. As a result, it is often difficult for them to recall the past insights, views and concepts that are most relevant to their current line of inquiry. This challenge is even more difficult during collaborative analysis tasks where they need to find connections between their own discoveries and insights found by others. In this paper, we describe a context-based retrieval algorithm to identify notes, views and concepts from users' past analyses that are most relevant to a view or a note based on their line of inquiry. We then describe a related notes recommendation feature that surfaces the most relevant items to the user as they work based on this algorithm. We have implemented this recommendation feature in HARVEST, a Web based visual analytic system. We evaluate the related notes recommendation feature of HARVEST through a case study and discuss the implications of our approach.