Visual sedimentation

Towards Systematic Design Considerations for Visualizing Cross-View Data Relationships

Due to the scale of data and the complexity of analysis tasks, insight discovery often requires coordinating multiple visualizations (views), with each view displaying different parts of data or the same data from different perspectives. For example, to analyze car sales records, a marketing analyst uses a line chart to visualize the trend of car sales, a scatterplot to inspect the price and horsepower of different cars, and a matrix to compare the transaction amounts in types of deals. To explore related information across multiple views, current visual analysis tools heavily rely on brushing and linking techniques, which may require a significant amount of user effort (e.g., many trial-and-error attempts). There may be other efficient and effective ways of displaying cross-view data relationships to support data analysis with multiple views, but currently there are no guidelines to address this design challenge. In this article, we present systematic design considerations for visualizing cross-view data relationships, which leverages descriptive aspects of relationships and usable visual context of multi-view visualizations. We discuss pros and cons of different designs for showing cross-view data relationships, and provide a set of recommendations for helping practitioners make design decisions.

MiningVis: Visual Analytics of the Bitcoin Mining Economy

We present a visual analytics tool, MiningVis, to explore the long-term historical evolution and dynamics of the Bitcoin mining ecosystem. Bitcoin is a cryptocurrency that attracts much attention but remains difficult to understand. Particularly important to the success, stability, and security of Bitcoin is a component of the system called "mining." Miners are responsible for validating transactions and are incentivized to participate by the promise of a monetary reward. Mining pools have emerged as collectives of miners that ensure a more stable and predictable income. MiningVis aims to help analysts understand the evolution and dynamics of the Bitcoin mining ecosystem, including mining market statistics, multi-measure mining pool rankings, and pool hopping behavior. Each of these features can be compared to external data concerning pool characteristics and Bitcoin news. In order to assess the value of MiningVis, we conducted online interviews and insight-based user studies with Bitcoin miners. We describe research questions tackled and insights made by our participants and illustrate practical implications for visual analytics systems for Bitcoin mining.

A Visualization Approach for Monitoring Order Processing in E-Commerce Warehouse

The efficiency of warehouses is vital to e-commerce. Fast order processing at the warehouses ensures timely deliveries and improves customer satisfaction. However, monitoring, analyzing, and manipulating order processing in the warehouses in real time are challenging for traditional methods due to the sheer volume of incoming orders, the fuzzy definition of delayed order patterns, and the complex decision-making of order handling priorities. In this paper, we adopt a data-driven approach and propose OrderMonitor, a visual analytics system that assists warehouse managers in analyzing and improving order processing efficiency in real time based on streaming warehouse event data. Specifically, the order processing pipeline is visualized with a novel pipeline design based on the sedimentation metaphor to facilitate real-time order monitoring and suggest potentially abnormal orders. We also design a novel visualization that depicts order timelines based on the Gantt charts and Marey's graphs. Such a visualization helps the managers gain insights into the performance of order processing and find major blockers for delayed orders. Furthermore, an evaluating view is provided to assist users in inspecting order details and assigning priorities to improve the processing performance. The effectiveness of OrderMonitor is evaluated with two case studies on a real-world warehouse dataset.

ATOM: A Grammar for Unit Visualizations

Unit visualizations are a family of visualizations where every data item is represented by a unique visual mark-a visual unit-during visual encoding. For certain datasets and tasks, unit visualizations can provide more information, better match the user's mental model, and enable novel interactions compared to traditional aggregated visualizations. Current visualization grammars cannot fully describe the unit visualization family. In this paper, we characterize the design space of unit visualizations to derive a grammar that can express them. The resulting grammar is called ATOM, and is based on passing data through a series of layout operations that divide the output of previous operations recursively until the size and position of every data point can be determined. We evaluate the expressive power of the grammar by both using it to describe existing unit visualizations, as well as to suggest new unit visualizations.

MultiStream: A Multiresolution Streamgraph Approach to Explore Hierarchical Time Series

Multiple time series are a set of multiple quantitative variables occurring at the same interval. They are present in many domains such as medicine, finance, and manufacturing for analytical purposes. In recent years, streamgraph visualization (evolved from ThemeRiver) has been widely used for representing temporal evolution patterns in multiple time series. However, streamgraph as well as ThemeRiver suffer from scalability problems when dealing with several time series. To solve this problem, multiple time series can be organized into a hierarchical structure where individual time series are grouped hierarchically according to their proximity. In this paper, we present a new streamgraph-based approach to convey the hierarchical structure of multiple time series to facilitate the exploration and comparisons of temporal evolution. Based on a focus+context technique, our method allows time series exploration at different granularities (e.g., from overview to details). To illustrate our approach, two usage examples are presented.

StreamExplorer: A Multi-Stage System for Visually Exploring Events in Social Streams

Analyzing social streams is important for many applications, such as crisis management. However, the considerable diversity, increasing volume, and high dynamics of social streams of large events continue to be significant challenges that must be overcome to ensure effective exploration. We propose a novel framework by which to handle complex social streams on a budget PC. This framework features two components: 1) an online method to detect important time periods (i.e., subevents), and 2) a tailored GPU-assisted Self-Organizing Map (SOM) method, which clusters the tweets of subevents stably and efficiently. Based on the framework, we present StreamExplorer to facilitate the visual analysis, tracking, and comparison of a social stream at three levels. At a macroscopic level, StreamExplorer uses a new glyph-based timeline visualization, which presents a quick multi-faceted overview of the ebb and flow of a social stream. At a mesoscopic level, a map visualization is employed to visually summarize the social stream from either a topical or geographical aspect. At a microscopic level, users can employ interactive lenses to visually examine and explore the social stream from different perspectives. Two case studies and a task-based evaluation are used to demonstrate the effectiveness and usefulness of StreamExplorer.Analyzing social streams is important for many applications, such as crisis management. However, the considerable diversity, increasing volume, and high dynamics of social streams of large events continue to be significant challenges that must be overcome to ensure effective exploration. We propose a novel framework by which to handle complex social streams on a budget PC. This framework features two components: 1) an online method to detect important time periods (i.e., subevents), and 2) a tailored GPU-assisted Self-Organizing Map (SOM) method, which clusters the tweets of subevents stably and efficiently. Based on the framework, we present StreamExplorer to facilitate the visual analysis, tracking, and comparison of a social stream at three levels. At a macroscopic level, StreamExplorer uses a new glyph-based timeline visualization, which presents a quick multi-faceted overview of the ebb and flow of a social stream. At a mesoscopic level, a map visualization is employed to visually summarize the social stream from either a topical or geographical aspect. At a microscopic level, users can employ interactive lenses to visually examine and explore the social stream from different perspectives. Two case studies and a task-based evaluation are used to demonstrate the effectiveness and usefulness of StreamExplorer.

Dynamic Composite Data Physicalization Using Wheeled Micro-Robots

This paper introduces dynamic composite physicalizations, a new class of physical visualizations that use collections of self-propelled objects to represent data. Dynamic composite physicalizations can be used both to give physical form to well-known interactive visualization techniques, and to explore new visualizations and interaction paradigms. We first propose a design space characterizing composite physicalizations based on previous work in the fields of Information Visualization and Human Computer Interaction. We illustrate dynamic composite physicalizations in two scenarios demonstrating potential benefits for collaboration and decision making, as well as new opportunities for physical interaction. We then describe our implementation using wheeled micro-robots capable of locating themselves and sensing user input, before discussing limitations and opportunities for future work.

Graphicle: Exploring Units, Networks, and Context in a Blended Visualization Approach

Many real-world datasets are large, multivariate, and relational in nature and relevant associated decisions frequently require a simultaneous consideration of both attributes and connections. Existing visualization systems and approaches, however, often make an explicit trade-off between either affording rich exploration of individual data units and their attributes or exploration of the underlying network structure. In doing so, important analysis opportunities and insights are potentially missed. In this study, we aim to address this gap by (1) considering visualizations and interaction techniques that blend the spectrum between unit and network visualizations, (2) discussing the nature of different forms of contexts and the challenges in implementing them, and (3) demonstrating the value of our approach for visual exploration of multivariate, relational data for a real-world use case. Specifically, we demonstrate through a system called Graphicle how network structure can be layered on top of unit visualization techniques to create new opportunities for visual exploration of physician characteristics and referral data. We report on the design, implementation, and evaluation of the system and effectiveness of our blended approach.

Structuring Visualization Mock-Ups at the Graphical Level by Dividing the Display Space

Mock-ups are rapid, low fidelity prototypes, that are used in many design-related fields to generate and share ideas. While their creation is supported by many mature methods and tools, surprisingly few are suited for the needs of information visualization. In this article, we introduce a novel approach to creating visualizations mock-ups, based on a dialogue between graphic design and parametric toolkit explorations. Our approach consists in iteratively subdividing the display space, while progressively informing each division with realistic data. We show that a wealth of mock-ups can easily be created using only temporary data attributes, as we wait for more realistic data to become available. We describe the implementation of this approach in a D3-based toolkit, which we use to highlight its generative power, and we discuss the potential for transitioning towards higher fidelity prototypes.

Exploring the Possibilities of Embedding Heterogeneous Data Attributes in Familiar Visualizations

Heterogeneous multi-dimensional data are now sufficiently common that they can be referred to as ubiquitous. The most frequent approach to visualizing these data has been to propose new visualizations for representing these data. These new solutions are often inventive but tend to be unfamiliar. We take a different approach. We explore the possibility of extending well-known and familiar visualizations through including Heterogeneous Embedded Data Attributes (HEDA) in order to make familiar visualizations more powerful. We demonstrate how HEDA is a generic, interactive visualization component that can extend common visualization techniques while respecting the structure of the familiar layout. HEDA is a tabular visualization building block that enables individuals to visually observe, explore, and query their familiar visualizations through manipulation of embedded multivariate data. We describe the design space of HEDA by exploring its application to familiar visualizations in the D3 gallery. We characterize these familiar visualizations by the extent to which HEDA can facilitate data queries based on attribute reordering.

Online Visual Analytics of Text Streams

We present an online visual analytics approach to helping users explore and understand hierarchical topic evolution in high-volume text streams. The key idea behind this approach is to identify representative topics in incoming documents and align them with the existing representative topics that they immediately follow (in time). To this end, we learn a set of streaming tree cuts from topic trees based on user-selected focus nodes. A dynamic Bayesian network model has been developed to derive the tree cuts in the incoming topic trees to balance the fitness of each tree cut and the smoothness between adjacent tree cuts. By connecting the corresponding topics at different times, we are able to provide an overview of the evolving hierarchical topics. A sedimentation-based visualization has been designed to enable the interactive analysis of streaming text data from global patterns to local details. We evaluated our method on real-world datasets and the results are generally favorable.

#FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media

We present FluxFlow, an interactive visual analysis system for revealing and analyzing anomalous information spreading in social media. Everyday, millions of messages are created, commented, and shared by people on social media websites, such as Twitter and Facebook. This provides valuable data for researchers and practitioners in many application domains, such as marketing, to inform decision-making. Distilling valuable social signals from the huge crowd's messages, however, is challenging, due to the heterogeneous and dynamic crowd behaviors. The challenge is rooted in data analysts' capability of discerning the anomalous information behaviors, such as the spreading of rumors or misinformation, from the rest that are more conventional patterns, such as popular topics and newsworthy events, in a timely fashion. FluxFlow incorporates advanced machine learning algorithms to detect anomalies, and offers a set of novel visualization designs for presenting the detected threads for deeper analysis. We evaluated FluxFlow with real datasets containing the Twitter feeds captured during significant events such as Hurricane Sandy. Through quantitative measurements of the algorithmic performance and qualitative interviews with domain experts, the results show that the back-end anomaly detection model is effective in identifying anomalous retweeting threads, and its front-end interactive visualizations are intuitive and useful for analysts to discover insights in data and comprehend the underlying analytical model.

Progressive Visual Analytics: User-Driven Visual Exploration of In-Progress Analytics

As datasets grow and analytic algorithms become more complex, the typical workflow of analysts launching an analytic, waiting for it to complete, inspecting the results, and then re-Iaunching the computation with adjusted parameters is not realistic for many real-world tasks. This paper presents an alternative workflow, progressive visual analytics, which enables an analyst to inspect partial results of an algorithm as they become available and interact with the algorithm to prioritize subspaces of interest. Progressive visual analytics depends on adapting analytical algorithms to produce meaningful partial results and enable analyst intervention without sacrificing computational speed. The paradigm also depends on adapting information visualization techniques to incorporate the constantly refining results without overwhelming analysts and provide interactions to support an analyst directing the analytic. The contributions of this paper include: a description of the progressive visual analytics paradigm; design goals for both the algorithms and visualizations in progressive visual analytics systems; an example progressive visual analytics system (Progressive Insights) for analyzing common patterns in a collection of event sequences; and an evaluation of Progressive Insights and the progressive visual analytics paradigm by clinical researchers analyzing electronic medical records.

Constructing Visual Representations: Investigating the Use of Tangible Tokens

The accessibility of infovis authoring tools to a wide audience has been identified as a major research challenge. A key task in the authoring process is the development of visual mappings. While the infovis community has long been deeply interested in finding effective visual mappings, comparatively little attention has been placed on how people construct visual mappings. In this paper, we present the results of a study designed to shed light on how people transform data into visual representations. We asked people to create, update and explain their own information visualizations using only tangible building blocks. We learned that all participants, most of whom had little experience in visualization authoring, were readily able to create and talk about their own visualizations. Based on our observations, we discuss participants' actions during the development of their visual representations and during their analytic activities. We conclude by suggesting implications for tool design to enable broader support for infovis authoring.

Visual sedimentation

We introduce Visual Sedimentation, a novel design metaphor for visualizing data streams directly inspired by the physical process of sedimentation. Visualizing data streams (e. g., Tweets, RSS, Emails) is challenging as incoming data arrive at unpredictable rates and have to remain readable. For data streams, clearly expressing chronological order while avoiding clutter, and keeping aging data visible, are important. The metaphor is drawn from the real-world sedimentation processes: objects fall due to gravity, and aggregate into strata over time. Inspired by this metaphor, data is visually depicted as falling objects using a force model to land on a surface, aggregating into strata over time. In this paper, we discuss how this metaphor addresses the specific challenge of smoothing the transition between incoming and aging data. We describe the metaphor's design space, a toolkit developed to facilitate its implementation, and example applications to a range of case studies. We then explore the generative capabilities of the design space through our toolkit. We finally illustrate creative extensions of the metaphor when applied to real streams of data.