IEEE VIS Publication Dataset

While the primary goal of visual analytics research is to improve the quality of insights and findings, a substantial amount of research in provenance has focused on the history of changes and advances throughout the analysis process. The term, provenance, has been used in a variety of ways to describe different types of records and histories related to visualization. The existing body of provenance research has grown to a point where the consolidation of design knowledge requires cross-referencing a variety of projects and studies spanning multiple domain areas. We present an organizational framework of the different types of provenance information and purposes for why they are desired in the field of visual analytics. Our organization is intended to serve as a framework to help researchers specify types of provenance and coordinate design knowledge across projects. We also discuss the relationships between these factors and the methods used to capture provenance information. In addition, our organization can be used to guide the selection of evaluation methodology and the comparison of study outcomes in provenance research.

The exploration and analysis of scientific literature collections is an important task for effective knowledge management. Past interest in such document sets has spurred the development of numerous visualization approaches for their interactive analysis. They either focus on the textual content of publications, or on document metadata including authors and citations. Previously presented approaches for citation analysis aim primarily at the visualization of the structure of citation networks and their exploration. We extend the state-of-the-art by presenting an approach for the interactive visual analysis of the contents of scientific documents, and combine it with a new and flexible technique to analyze their citations. This technique facilitates user-steered aggregation of citations which are linked to the content of the citing publications using a highly interactive visualization approach. Through enriching the approach with additional interactive views of other important aspects of the data, we support the exploration of the dataset over time and enable users to analyze citation patterns, spot trends, and track long-term developments. We demonstrate the strengths of our approach through a use case and discuss it based on expert user feedback.

Consider the emerging role of data science teams embedded in larger organizations. Individual analysts work on loosely related problems, and must share their findings with each other and the organization at large, moving results from exploratory data analyses (EDA) into automated visualizations, diagnostics and reports deployed for wider consumption. There are two problems with the current practice. First, there are gaps in this workflow: EDA is performed with one set of tools, and automated reports and deployments with another. Second, these environments often assume a single-developer perspective, while data scientist teams could get much benefit from easier sharing of scripts and data feeds, experiments, annotations, and automated recommendations, which are well beyond what traditional version control systems provide. We contribute and justify the following three requirements for systems built to support current data science teams and users: discoverability, technology transfer, and coexistence. In addition, we contribute the design and implementation of RCloud, a system that supports the requirements of collaborative data analysis, visualization and web deployment. About 100 people used RCloud for two years. We report on interviews with some of these users, and discuss design decisions, tradeoffs and limitations in comparison to other approaches.

We present a new visual analysis approach to support the comparative exploration of 2D vector-valued ensemble fields. Our approach enables the user to quickly identify the most similar groups of ensemble members, as well as the locations where the variation among the members is high. We further provide means to visualize the main features of the potentially multimodal directional distributions at user-selected locations. For this purpose, directional data is modelled using mixtures of probability density functions (pdfs), which allows us to characterize and classify complex distributions with relatively few parameters. The resulting mixture models are used to determine the degree of similarity between ensemble members, and to construct glyphs showing the direction, spread, and strength of the principal modes of the directional distributions. We also propose several similarity measures, based on which we compute pairwise member similarities in the spatial domain and form clusters of similar members. The hierarchical clustering is shown using dendrograms and similarity matrices, which can be used to select particular members and visualize their variations. A user interface providing multiple linked views enables the simultaneous visualization of aggregated global and detailed local variations, as well as the selection of members for a detailed comparison.

The wide-spread of social media provides unprecedented sources of written language that can be used to model and infer online demographics. In this paper, we introduce a novel visual text analytics system, DemographicVis, to aid interactive analysis of such demographic information based on user-generated content. Our approach connects categorical data (demographic information) with textual data, allowing users to understand the characteristics of different demographic groups in a transparent and exploratory manner. The modeling and visualization are based on ground truth demographic information collected via a survey conducted on Reddit.com. Detailed user information is taken into our modeling process that connects the demographic groups with features that best describe the distinguishing characteristics of each group. Features including topical and linguistic are generated from the user-generated contents. Such features are then analyzed and ranked based on their ability to predict the users' demographic information. To enable interactive demographic analysis, we introduce a web-based visual interface that presents the relationship of the demographic groups, their topic interests, as well as the predictive power of various features. We present multiple case studies to showcase the utility of our visual analytics approach in exploring and understanding the interests of different demographic groups. We also report results from a comparative evaluation, showing that the DemographicVis is quantitatively superior or competitive and subjectively preferred when compared to a commercial text analysis tool.

Event-based egocentric dynamic networks are an important class of networks widely seen in many domains. In this paper, we present a visual analytics approach for these networks by combining data-driven network simplifications with a novel visualization design - EgoNetCloud. In particular, an integrated data processing pipeline is proposed to prune, compress and filter the networks into smaller but salient abstractions. To accommodate the simplified network into the visual design, we introduce a constrained graph layout algorithm on the dynamic network. Through a real-life case study as well as conversations with the domain expert, we demonstrate the effectiveness of the EgoNetCloud design and system in completing analysis tasks on event-based dynamic networks. The user study comparing EgoNetCloud with a working system on academic search confirms the effectiveness and convenience of our visual analytics based approach.

Ego-network, which represents relationships between a specific individual, i.e., the ego, and people connected to it, i.e., alters, is a critical target to study in social network analysis. Evolutionary patterns of ego-networks along time provide huge insights to many domains such as sociology, anthropology, and psychology. However, the analysis of dynamic ego-networks remains challenging due to its complicated time-varying graph structures, for example: alters come and leave, ties grow stronger and fade away, and alter communities merge and split. Most of the existing dynamic graph visualization techniques mainly focus on topological changes of the entire network, which is not adequate for egocentric analytical tasks. In this paper, we present egoSlider, a visual analysis system for exploring and comparing dynamic ego-networks. egoSlider provides a holistic picture of the data through multiple interactively coordinated views, revealing ego-network evolutionary patterns at three different layers: a macroscopic level for summarizing the entire ego-network data, a mesoscopic level for overviewing specific individuals' ego-network evolutions, and a microscopic level for displaying detailed temporal information of egos and their alters. We demonstrate the effectiveness of egoSlider with a usage scenario with the DBLP publication records. Also, a controlled user study indicates that in general egoSlider outperforms a baseline visualization of dynamic networks for completing egocentric analytical tasks.

De novo design is a computational-chemistry method, where a computer program utilizes an optimization method, in our case an evolutionary algorithm, to design compounds with desired chemical properties. The optimization is performed with respect to a quantity called fitness, defined by the chemists. We present a tool that connects interactive visual analysis and evolutionary algorithm-based molecular design. We employ linked views to communicate different aspects of the data: the statistical distribution of molecule fitness, connections between individual molecules during the evolution and 3D molecular structure. The application is already used by chemists to explore and analyze the results of their evolution experiments and has proved to be highly useful.

Online news, microblogs and other media documents all contain valuable insight regarding events and responses to events. Underlying these documents is the concept of framing, a process in which communicators act (consciously or unconsciously) to construct a point of view that encourages facts to be interpreted by others in a particular manner. As media discourse evolves, how topics and documents are framed can undergo change, shifting the discussion to different viewpoints or rhetoric. What causes these shifts can be difficult to determine directly; however, by linking secondary datasets and enabling visual exploration, we can enhance the hypothesis generation process. In this paper, we present a visual analytics framework for event cueing using media data. As discourse develops over time, our framework applies a time series intervention model which tests to see if the level of framing is different before or after a given date. If the model indicates that the times before and after are statistically significantly different, this cues an analyst to explore related datasets to help enhance their understanding of what (if any) events may have triggered these changes in discourse. Our framework consists of entity extraction and sentiment analysis as lenses for data exploration and uses two different models for intervention analysis. To demonstrate the usage of our framework, we present a case study on exploring potential relationships between climate change framing and conflicts in Africa.

Machine learning requires an effective combination of data, features, and algorithms. While many tools exist for working with machine learning data and algorithms, support for thinking of new features, or feature ideation, remains poor. In this paper, we investigate two general approaches to support feature ideation: visual summaries and sets of errors. We present FeatureInsight, an interactive visual analytics tool for building new dictionary features (semantically related groups of words) for text classification problems. FeatureInsight supports an error-driven feature ideation process and provides interactive visual summaries of sets of misclassified documents. We conducted a controlled experiment evaluating both visual summaries and sets of errors in FeatureInsight. Our results show that visual summaries significantly improve feature ideation, especially in combination with sets of errors. Users preferred visual summaries over viewing raw data, and only preferred examining sets when visual summaries were provided. We discuss extensions of both approaches to data types other than text, and point to areas for future research.

The current proliferation of large displays and mobile devices presents a number of exciting opportunities for visual analytics and information visualization. The display ecology enables multiple displays to function in concert within a broader technological environment to accomplish visual analysis tasks. Based on a comprehensive survey of multi-display systems from a variety of fields, we propose four key considerations for visual analysis in display ecologies: 1) Display Composition, 2) Information Coordination/Transfer, 3) Information Connection, and 4) Display Membership. Different aspects of display ecologies stemming from these design considerations will enable users to transform and empower multiple displays as a display ecology for visual analysis.

The rate at which frames are rendered in a computer game directly influences both game playability and enjoyability. Players frequently have to deal with the trade-off between high frame rates and good resolution. Analyzing patterns in frame rate data and their correlation with the overall game performance is important in designing games (e.g., graphic card/display setting suggestion and game performance measurement). However, this task is challenging because game frame rates vary both temporally and spatially. In addition, players may adjust their display settings based on their gaming experience and hardware conditions, which further contributes to the unpredictability of frame rates. In this paper, we present a comprehensive visual analytics system FPSSeer, to help game designers gain insight into frame rate data. Our system consists of four major views: 1) a frame rate view to show the overall distribution in a geographic scale, 2) a grid view to show the frame rate distribution and grid element clusters based on their similarity, 3) a FootRiver view to reveal the temporal patterns in game condition changes and potential spatiotemporal correlations, and 4) a comparison view to evaluate game performance discrepancy under different game tests. The real-world case studies demonstrate the effectiveness of our system. The system has been applied to an online commercial game to monitor its performance and to provide feedbacks to designers and developers.

Topic model has been an active research area for many years, it can be used for discovering latent semantics and finding hidden knowledge in unstructured data corpus. In this paper, we investigated the problems in visualizing hierarchical topic and their evolution. The contribution of this paper is threefold, first we explore the static visualization of hierarchical topics using the `nested circle' layout, and then in order to present the topic evolution over time, we extended a hierarchical topic model and employ topic transformation visualizations to track the arising, splitting and disappearing of certain topics under the dynamic topical hierarchy. Finally, a Hierarchical Topic Model Visualization System (HTMVS) is designed to take advantage of both static and dynamic hierarchical topic visualization.

The Epidemic Simulation System (EpiSimS) is a scalable, complex modeling tool for analyzing disease within the United States. Due to its high input dimensionality, time requirements, and resource constraints, simulating over the entire parameter space is unfeasible. One solution is to take a granular sampling of the input space and use simpler predictive models (emulators) in between. The quality of the implemented emulator depends on many factors: robustness, sophistication, configuration, and suitability to the input data. Visual analytics can be leveraged to provide guidance and understanding of these things to the user. In this paper, we have implemented a novel interface and workflow for emulator building and use. We introduce a workflow to build emulators, make predictions, and then analyze the results. Our prediction process first predicts temporal time series, and uses these to derive predicted spatial densities. Integrated into the EpiSimS framework, we target users who are non-experts at statistical modeling. This approach allows for a high level of analysis into the state of the built emulators and their resultant predictions. We present our workflow, models, the associated system, and evaluate the overall utility with feedback from EpiSimS scientists.

Spinel group minerals are excellent indicators of geological environments (tectonic settings). In 2001, Barnes and Roeder defined a set of contours corresponding to compositional fields for spinel group minerals. Geologists typically use this contours to estimate the tectonic environment where a particular spinel composition could have been formed. This task is prone to errors and requires tedious manual comparison of overlapping diagrams. We introduce a semi-automatic, interactive detection of tectonic settings for an arbitrary dataset based on the Barnes and Roeder contours. The new approach integrates the mentioned contours and includes a novel interaction called contour brush. The new methodology is integrated in the Spinel Explorer system and it improves the scientist's workflow significantly.

Social media data with geotags can be used to track people's movements in their daily lives. By providing both rich text and movement information, visual analysis on social media data can be both interesting and challenging. In contrast to traditional movement data, the sparseness and irregularity of social media data increase the difficulty of extracting movement patterns. To facilitate the understanding of people's movements, we present an interactive visual analytics system to support the exploration of sparsely sampled trajectory data from social media. We propose a heuristic model to reduce the uncertainty caused by the nature of social media data. In the proposed system, users can filter and select reliable data from each derived movement category, based on the guidance of uncertainty model and interactive selection tools. By iteratively analyzing filtered movements, users can explore the semantics of movements, including the transportation methods, frequent visiting sequences and keyword descriptions. We provide two cases to demonstrate how our system can help users to explore the movement patterns.

Determining similar objects based upon the features of an object of interest is a common task for visual analytics systems. This process is called profiling, if the object of interest is a person with individual attributes. The profiling of musicians similar to a musician of interest with the aid of visual means became an interesting research question for musicologists working with the Bavarian Musicians Encyclopedia Online. This paper illustrates the development of a visual analytics profiling system that is used to address such research questions. Taking musicological knowledge into account, we outline various steps of our collaborative digital humanities project, priority (1) the definition of various measures to determine the similarity of musicians' attributes, and (2) the design of an interactive profiling system that supports musicologists in iteratively determining similar musicians. The utility of the profiling system is emphasized by various usage scenarios illustrating current research questions in musicology.

Multi-level simulation models, i.e., models where different components are simulated using sub-models of varying levels of complexity, belong to the current state-of-the-art in simulation. The existing analysis practice for multi-level simulation results is to manually compare results from different levels of complexity, amounting to a very tedious and error-prone, trial-and-error exploration process. In this paper, we introduce hierarchical visual steering, a new approach to the exploration and design of complex systems. Hierarchical visual steering makes it possible to explore and analyze hierarchical simulation ensembles at different levels of complexity. At each level, we deal with a dynamic simulation ensemble - the ensemble grows during the exploration process. There is at least one such ensemble per simulation level, resulting in a collection of dynamic ensembles, analyzed simultaneously. The key challenge is to map the multi-dimensional parameter space of one ensemble to the multi-dimensional parameter space of another ensemble (from another level). In order to support the interactive visual analysis of such complex data we propose a novel approach to interactive and semi-automatic parameter space segmentation and comparison. The approach combines a novel interaction technique and automatic, computational methods - clustering, concave hull computation, and concave polygon overlapping - to support the analysts in the cross-ensemble parameter space mapping. In addition to the novel parameter space segmentation we also deploy coordinated multiple views with standard plots. We describe the abstract analysis tasks, identified during a case study, i.e., the design of a variable valve actuation system of a car engine. The study is conducted in cooperation with experts from the automotive industry. Very positive feedback indicates the usefulness and efficiency of the newly proposed approach.

Scatterplots are effective visualization techniques for multidimensional data that use two (or three) axes to visualize data items as a point at its corresponding x and y Cartesian coordinates. Typically, each axis is bound to a single data attribute. Interactive exploration occurs by changing the data attributes bound to each of these axes. In the case of using scatterplots to visualize the outputs of dimension reduction techniques, the x and y axes are combinations of the true, high-dimensional data. For these spatializations, the axes present usability challenges in terms of interpretability and interactivity. That is, understanding the axes and interacting with them to make adjustments can be challenging. In this paper, we present InterAxis, a visual analytics technique to properly interpret, define, and change an axis in a user-driven manner. Users are given the ability to define and modify axes by dragging data items to either side of the x or y axes, from which the system computes a linear combination of data attributes and binds it to the axis. Further, users can directly tune the positive and negative contribution to these complex axes by using the visualization of data attributes that correspond to each axis. We describe the details of our technique and demonstrate the intended usage through two scenarios.

Using transport smart card transaction data to understand the homework dynamics of a city for urban planning is emerging as an alternative to traditional surveys which may be conducted every few years are no longer effective and efficient for the rapidly transforming modern cities. As commuters travel patterns are highly diverse, existing rule-based methods are not fully adequate. In this paper, we present iVizTRANS - a tool which combines an interactive visual analytics (VA) component to aid urban planners to analyse complex travel patterns and decipher activity locations for single public transport commuters. It is coupled with a machine learning component that iteratively learns from the planners classifications to train a classifier. The classifier is then applied to the city-wide smart card data to derive the dynamics for all public transport commuters. Our evaluation shows it outperforms the rule-based methods in previous work.