Interactive Visual Analytics System Research Articles

TimeCluster: dimension reduction applied to temporal data for visual analytics

There is a need for solutions which assist users to understand long time-series data by observing its changes over time, finding repeated patterns, detecting outliers, and effectively labeling data instances. Although these tasks are quite distinct and are usually tackled separately, we present an interactive visual analytics system and approach that can address these issues in a single system. It enables users to visualize, understand and explore univariate or multivariate long time-series data in one image using a connected scatter plot. It supports interactive analysis and exploration for pattern discovery and outlier detection. Different dimensionality reduction techniques are used and compared in our system. Because of its power of extracting features, deep learning is used for multivariate time-series along with 2D reduction techniques for rapid and easy interpretation and interaction with large amount of time-series data. We deploy our system with different time-series datasets and report two real-world case studies that are used to evaluate our system.

Clone-World: A visual analytic system for large scale software clones

With the era of big data approaching, the number of software systems, their dependencies, as well as the complexity of the individual system is becoming larger and more intricate. Understanding these evolving software systems is thus a primary challenge for cost-effective software management and maintenance. In this paper we perform a case study with evolving code clones. The programmers often need to manually analyze the co-evolution of clone fragments to decide about refactoring, tracking, and bug removal. However, manual analysis is time consuming, and nearly infeasible for a large number of clones, e.g., with millions of similarity pairs, where clones are evolving over hundreds of software revisions.We propose an interactive visual analytics system, Clone-World, which leverages big data visualization approach to manage code clones in large software systems. Clone-World, gives an intuitive yet powerful solution to the clone analytic problems. Clone-World combines multiple information-linked zoomable views, where users can explore and analyze clones through interactive exploration in real time. User studies and experts’ reviews suggest that Clone-World may assist developers in many real-life software development and maintenance scenarios. We believe that Clone-World will ease the management and maintenance of clones, and inspire future innovation to adapt visual analytics to manage big software systems.

Uncertainty-aware visual analysis of radiofrequency ablation simulations

Radiofrequency (RF) ablation is a medical procedure for treating tumors by generating heat with RF current using needle-like probes that are inserted into the tumor. During RF ablation the entire tumor shall be destroyed to avoid recurrence while keeping the destruction of surrounding healthy tissue minimal. As the ablation area is affected by surrounding structures such as vessels, developing biomedical simulations that support treatment planning is an active research field. The simulations assess the area based on patient-specific imaging data to lower the risk of under- or over-ablation. However, there are several simulation parameters whose precise values are unknown. We capture and propagate these uncertainties by deploying a stochastic simulation model and computing the uncertainties in the resulting ablation probabilities. We propose an uncertainty-aware interactive visual analysis system of the probabilities based on coordinated views of volume visualizations, a slice-based visualization of 2D uncertainty glyphs with a perceptually uniform color-coding, and parameter analysis plots. Using our interactive system, simulation experts can analyze the influence of their parameter choices on the ablation probabilities and their uncertainties. Moreover, our glyph encoding provides an inherent continuous level-of-detail approach during zooming interaction that makes it suitable for application in clinical settings. We apply our techniques to RF ablation simulations for liver tumor treatment and document their value within a controlled user study.

Exploring the Evolutionary Patterns of Urban Activity Areas Based on Origin-Destination Data

An understanding of the evolutionary patterns in areas of urban activity is crucial for official decision makers and urban planners. The origin-destination (OD) datasets generated by human daily travel behavior reflect urban dynamics. Previous spatio-temporal analysis methods utilize these datasets to extract popular city areas, with the ignorance of the flow relationships between areas. Several methods have been unable to determine time steps with similar spatial characteristics automatically or failed to recognize the evolutionary patterns of various modalities for a city. In this paper, we propose a new methodology to discover the hidden semantic-level city dynamics from OD data. The method carries out spatial simplification and constructs a sequence of location networks at first. Then, the hourly network is studied as a document consisting of trip relationships among location clusters, enabling a semantic analysis of the OD dataset as a document corpus. Hidden themes, namely, traffic topics, are identified through a topic modeling technique in an unsupervised manner. Finally, an interactive visual analytics system is designed to intuitively demonstrate the probability-based thematic information and the evolutionary activity patterns of a city. The feasibility and validity of our method are demonstrated via case studies with two kinds of real-world datasets: bike-sharing system (BSS) dataset and taxi dataset. Semantic-level city structures and recurrent behaviors representing the life of a large set of users, as well as the differences in BSS usage patterns of two cities are discovered. We also discover how people use different means of transportation for one city.

Visual Analytics of Stratigraphic Correlation for Multi-Attribute Well-Logging Data Exploration

Stratigraphic correlation based on well-logging data is able to help geological interpreters analogize and deduce underground sedimentary morphology. A great deal of traditional methods has been studied to automatically or manually achieve stratigraphic correlation, the courses of which are usually time-consuming and not intuitive. Many uncertainties are easily generated to reduce the effectiveness of stratigraphic correlation and the accuracy of geological interpretation. To address this issue, this paper introduces an interactive visual analytics system for identifying correlation patterns and improving correlation accuracies using large-scale well-logging data. First, we propose a novel stratigraphic correlation model with the composition of multi-log curve integration, layer identification, and layer matching. Then, a visualization framework is designed by working closely with domain experts in an iterative manner to get deeper insights into the course of stratigraphic correlation based on a few visual interfaces such as map view, correlation view, matrix view and attribute view. Also, a rich set of interactions is provided allowing interpreters to refine the results of stratigraphic correlation according to domain knowledge and user requirements. Furthermore, case studies based on real-world datasets and interviews with domain experts have demonstrated the effectiveness of our system for the stratigraphic correlation and geological interpretation.

EmoCo: Visual Analysis of Emotion Coherence in Presentation Videos.

Emotions play a key role in human communication and public presentations. Human emotions are usually expressed through multiple modalities. Therefore, exploring multimodal emotions and their coherence is of great value for understanding emotional expressions in presentations and improving presentation skills. However, manually watching and studying presentation videos is often tedious and time-consuming. There is a lack of tool support to help conduct an efficient and in-depth multi-level analysis. Thus, in this paper, we introduce EmoCo, an interactive visual analytics system to facilitate efficient analysis of emotion coherence across facial, text, and audio modalities in presentation videos. Our visualization system features a channel coherence view and a sentence clustering view that together enable users to obtain a quick overview of emotion coherence and its temporal evolution. In addition, a detail view and word view enable detailed exploration and comparison from the sentence level and word level, respectively. We thoroughly evaluate the proposed system and visualization techniques through two usage scenarios based on TED Talk videos and interviews with two domain experts. The results demonstrate the effectiveness of our system in gaining insights into emotion coherence in presentations.

BitExTract: Interactive Visualization for Extracting Bitcoin Exchange Intelligence.

The emerging prosperity of cryptocurrencies, such as Bitcoin, has come into the spotlight during the past few years. Cryptocurrency exchanges, which act as the gateway to this world, now play a dominant role in the circulation of Bitcoin. Thus, delving into the analysis of the transaction patterns of exchanges can shed light on the evolution and trends in the Bitcoin market, and participants can gain hints for identifying credible exchanges as well. Not only Bitcoin practitioners but also researchers in the financial domains are interested in the business intelligence behind the curtain. However, the task of multiple exchanges exploration and comparisons has been limited owing to the lack of efficient tools. Previous methods of visualizing Bitcoin data have mainly concentrated on tracking suspicious transaction logs, but it is cumbersome to analyze exchanges and their relationships with existing tools and methods. In this paper, we present BitExTract, an interactive visual analytics system, which, to the best of our knowledge, is the first attempt to explore the evolutionary transaction patterns of Bitcoin exchanges from two perspectives, namely, exchange versus exchange and exchange versus client. In particular, BitExTract summarizes the evolution of the Bitcoin market by observing the transactions between exchanges over time via a massive sequence view. A node-link diagram with ego-centered views depicts the trading network of exchanges and their temporal transaction distribution. Moreover, BitExTract embeds multiple parallel bars on a timeline to examine and compare the evolution patterns of transactions between different exchanges. Three case studies with novel insights demonstrate the effectiveness and usability of our system.

Shadow Accrual Maps: Efficient Accumulation of City-Scale Shadows Over Time.

Large scale shadows from buildings in a city play an important role in determining the environmental quality of public spaces. They can be both beneficial, such as for pedestrians during summer, and detrimental, by impacting vegetation and by blocking direct sunlight. Determining the effects of shadows requires the accumulation of shadows over time across different periods in a year. In this paper, we propose a simple yet efficient class of approach that uses the properties of sun movement to track the changing position of shadows within a fixed time interval. We use this approach to extend two commonly used shadow techniques, shadow maps and ray tracing, and demonstrate the efficiency of our approach. Our technique is used to develop an interactive visual analysis system, Shadow Profiler, targeted at city planners and architects that allows them to test the impact of shadows for different development scenarios. We validate the usefulness of this system through case studies set in Manhattan, a dense borough of New York City.

HomeSeeker: A visual analytics system of real estate data

In this paper, we present HomeSeeker, an interactive visual analytics system to serve users with different backgrounds of the local real estate market and meet different degrees of user requirements. As a result, HomeSeeker augments existing commercial systems to help users discover hidden patterns, link various location-centered data to the price, as well as explore, filter and compare the properties, in order to easily find their preferred properties. In particular, we make the following contributions: (1) We present a problem abstraction for designing visualizations that help home buyers analyse the real estate data. Specifically, our data abstraction integrates heterogeneous data from different channels into a location-centred integrated real estate dataset. (2) We propose an interactive visual analytic procedure to help less informed users gradually learn about the local real estate market, upon which users exploit this learned knowledge to specify their individual requirements in property seeking. (3) We propose a series of designs to visualize properties/suburbs in different dimensions and in different granularities. We have collected, integrated and cleaned last 10 year’s real estate sold records in Australia as well as their location-related education, facility and transportation profiles, to generate a real multi-dimensional data repository, and implemented a system prototype for public access (http://115.146.89.158). At last, we present case studies based on real-world datasets and real scenario to demonstrate the usefulness and effectiveness of our system.

StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views.

Urban forms at human-scale, i.e., urban environments that individuals can sense (e.g., sight, smell, and touch) in their daily lives, can provide unprecedented insights on a variety of applications, such as urban planning and environment auditing. The analysis of urban forms can help planners develop high-quality urban spaces through evidence-based design. However, such analysis is complex because of the involvement of spatial, multi-scale (i.e., city, region, and street), and multivariate (e.g., greenery and sky ratios) natures of urban forms. In addition, current methods either lack quantitative measurements or are limited to a small area. The primary contribution of this work is the design of StreetVizor, an interactive visual analytics system that helps planners leverage their domain knowledge in exploring human-scale urban forms based on street view images. Our system presents two-stage visual exploration: 1) an AOI Explorer for the visual comparison of spatial distributions and quantitative measurements in two areas-of-interest (AOIs) at city- and region-scales; 2) and a Street Explorer with a novel parallel coordinate plot for the exploration of the fine-grained details of the urban forms at the street-scale. We integrate visualization techniques with machine learning models to facilitate the detection of street view patterns. We illustrate the applicability of our approach with case studies on the real-world datasets of four cities, i.e., Hong Kong, Singapore, Greater London and New York City. Interviews with domain experts demonstrate the effectiveness of our system in facilitating various analytical tasks.

VIGOR: Interactive Visual Exploration of Graph Query Results

Finding patterns in graphs has become a vital challenge in many domains from biological systems, network security, to finance (e.g., finding money laundering rings of bankers and business owners). While there is significant interest in graph databases and querying techniques, less research has focused on helping analysts make sense of underlying patterns within a group of subgraph results. Visualizing graph query results is challenging, requiring effective summarization of a large number of subgraphs, each having potentially shared node-values, rich node features, and flexible structure across queries. We present VIGOR, a novel interactive visual analytics system, for exploring and making sense of query results. VIGOR uses multiple coordinated views, leveraging different data representations and organizations to streamline analysts sensemaking process. VIGOR contributes: (1) an exemplar-based interaction technique, where an analyst starts with a specific result and relaxes constraints to find other similar results or starts with only the structure (i.e., without node value constraints), and adds constraints to narrow in on specific results; and (2) a novel feature-aware subgraph result summarization. Through a collaboration with Symantec, we demonstrate how VIGOR helps tackle real-world problems through the discovery of security blindspots in a cybersecurity dataset with over 11,000 incidents. We also evaluate VIGOR with a within-subjects study, demonstrating VIGOR's ease of use over a leading graph database management system, and its ability to help analysts understand their results at higher speed and make fewer errors.

Supporting Handoff in Asynchronous Collaborative Sensemaking Using Knowledge-Transfer Graphs.

During asynchronous collaborative analysis, handoff of partial findings is challenging because externalizations produced by analysts may not adequately communicate their investigative process. To address this challenge, we developed techniques to automatically capture and help encode tacit aspects of the investigative process based on an analyst's interactions, and streamline explicit authoring of handoff annotations. We designed our techniques to mediate awareness of analysis coverage, support explicit communication of progress and uncertainty with annotation, and implicit communication through playback of investigation histories. To evaluate our techniques, we developed an interactive visual analysis system, KTGraph, that supports an asynchronous investigative document analysis task. We conducted a two-phase user study to characterize a set of handoff strategies and to compare investigative performance with and without our techniques. The results suggest that our techniques promote the use of more effective handoff strategies, help increase an awareness of prior investigative process and insights, as well as improve final investigative outcomes.

The visual analysis of flow pattern for public bicycle system

Public Bicycle System(PBS) is an increasingly popular mode of public transit, with the advantage of pollution-free and flexibility. In this paper, we present an interactive visual analytic system for exploring complex flows generated by PBS. Four inter-linked visualization views are designed to illustrate multiple perspectives of data, such as the spatial-temporal changes, the relationships and differences between flow OD pairs and the multi-dimensional factors(weather condition, calendar events) influencing on the rental numbers. A new presentation “Parallel coordinates with line and set” combined with flexible interaction schemes is proposed to support the exploration of multivariate association. We exemplify our approach with a real citywide PBS dataset. The results of case study demonstrate that our system is helpful for visually classifying stations with different flow patterns, speculating in-depth reasons, as well as investigating abnormal behaviors, helping decision makers to gain a better understanding of the large dataset.

Visual analytics for supply network management: System design and evaluation

We propose a visual analytic system to augment and enhance decision-making processes of supply chain managers. Several design requirements drive the development of our integrated architecture and lead to three primary capabilities of our system prototype. First, a visual analytic system must integrate various relevant views and perspectives that highlight different structural aspects of a supply network. Second, the system must deliver required information on-demand and update the visual representation via user-initiated interactions. Third, the system must provide both descriptive and predictive analytic functions for managers to gain contingency intelligence. Based on these capabilities we implement an interactive web-based visual analytic system. Our system enables managers to interactively apply visual encodings based on different node and edge attributes to facilitate mental map matching between abstract attributes and visual elements. Grounded in cognitive fit theory, we demonstrate that an interactive visual system that dynamically adjusts visual representations to the decision environment can significantly enhance decision-making processes in a supply network setting. We conduct multi-stage evaluation sessions with prototypical users that collectively confirm the value of our system. Our results indicate a positive reaction to our system. We conclude with implications and future research opportunities.

SmartAdP: Visual Analytics of Large-scale Taxi Trajectories for Selecting Billboard Locations.

The problem of formulating solutions immediately and comparing them rapidly for billboard placements has plagued advertising planners for a long time, owing to the lack of efficient tools for in-depth analyses to make informed decisions. In this study, we attempt to employ visual analytics that combines the state-of-the-art mining and visualization techniques to tackle this problem using large-scale GPS trajectory data. In particular, we present SmartAdP, an interactive visual analytics system that deals with the two major challenges including finding good solutions in a huge solution space and comparing the solutions in a visual and intuitive manner. An interactive framework that integrates a novel visualization-driven data mining model enables advertising planners to effectively and efficiently formulate good candidate solutions. In addition, we propose a set of coupled visualizations: a solution view with metaphor-based glyphs to visualize the correlation between different solutions; a location view to display billboard locations in a compact manner; and a ranking view to present multi-typed rankings of the solutions. This system has been demonstrated using case studies with a real-world dataset and domain-expert interviews. Our approach can be adapted for other location selection problems such as selecting locations of retail stores or restaurants using trajectory data.

Visual Analysis of MOOC Forums with iForum.

Discussion forums of Massive Open Online Courses (MOOC) provide great opportunities for students to interact with instructional staff as well as other students. Exploration of MOOC forum data can offer valuable insights for these staff to enhance the course and prepare the next release. However, it is challenging due to the large, complicated, and heterogeneous nature of relevant datasets, which contain multiple dynamically interacting objects such as users, posts, and threads, each one including multiple attributes. In this paper, we present a design study for developing an interactive visual analytics system, called iForum, that allows for effectively discovering and understanding temporal patterns in MOOC forums. The design study was conducted with three domain experts in an iterative manner over one year, including a MOOC instructor and two official teaching assistants. iForum offers a set of novel visualization designs for presenting the three interleaving aspects of MOOC forums (i.e., posts, users, and threads) at three different scales. To demonstrate the effectiveness and usefulness of iForum, we describe a case study involving field experts, in which they use iForum to investigate real MOOC forum data for a course on JAVA programming.

TelCoVis: Visual Exploration of Co-occurrence in Urban Human Mobility Based on Telco Data.

Understanding co-occurrence in urban human mobility (i.e. people from two regions visit an urban place during the same time span) is of great value in a variety of applications, such as urban planning, business intelligence, social behavior analysis, as well as containing contagious diseases. In recent years, the widespread use of mobile phones brings an unprecedented opportunity to capture large-scale and fine-grained data to study co-occurrence in human mobility. However, due to the lack of systematic and efficient methods, it is challenging for analysts to carry out in-depth analyses and extract valuable information. In this paper, we present TelCoVis, an interactive visual analytics system, which helps analysts leverage their domain knowledge to gain insight into the co-occurrence in urban human mobility based on telco data. Our system integrates visualization techniques with new designs and combines them in a novel way to enhance analysts' perception for a comprehensive exploration. In addition, we propose to study the correlations in co-occurrence (i.e. people from multiple regions visit different places during the same time span) by means of biclustering techniques that allow analysts to better explore coordinated relationships among different regions and identify interesting patterns. The case studies based on a real-world dataset and interviews with domain experts have demonstrated the effectiveness of our system in gaining insights into co-occurrence and facilitating various analytical tasks.

Interactive Visual Discovering of Movement Patterns from Sparsely Sampled Geo-tagged Social Media Data.

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.

#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.

Interactive Visual Analytics of Databases and Frequent Sets

In numerous real-life applications, large databases can be easily generated. Implicitly embedded in these databases is previously unknown and potentially useful knowledge such as frequently occurring sets of items, merchandise, or events. Different algorithms have been proposed for managing and retrieving useful information from these databases. Various algorithms have also been proposed for mining these databases to find frequent sets, which are usually presented in a lengthy textual list. As “a picture is worth a thousand words”, the use of visual representations can enhance user understanding of the inherent relationships among the mined frequent sets. Many of the existing visualizers were not designed to visualize these mined frequent sets. In this journal article, an interactive visual analytic system is proposed for providing visual analytic solutions to the frequent set mining problem. The system enables the management, visualization, and advanced analysis of the original transaction databases as well as the frequent sets mined from these databases.