Yuxin Ma

A Survey of Visual Analytic Pipelines

Visual analytics has been widely studied in the past decade. One key to make visual analytics practical for both research and industrial applications is the appropriate definition and implementation of the visual analytics pipeline which provides effective abstractions for designing and implementing visual analytics systems. In this paper we review the previous work on visual analytics pipelines and individual modules from multiple perspectives: data, visualization, model and knowledge. In each module we discuss various representations and descriptions of pipelines inside the module, and compare the commonalities and the differences among them.

Mobility Viewer: An Eulerian Approach for Studying Urban Crowd Flow

Studying human movement citywide is important for understanding mobility and transportation patterns. Rather than investigating the trajectories of individuals, we employ an Eulerian approach to analyze the crowd flows among a geographical network and a social network, which are extracted from mobile phone data. We design a suite of visualization techniques to illustrate the dynamic evolutions of the flow over the networks. We contribute the design and implementation of a visual analytics system, which is called Mobility Viewer, that supports situation-aware understanding and visual reasoning of human mobility. We exemplify our approach with a real citywide data set of seven million users in two months.

Recent progress and trends in predictive visual analytics

A wide variety of predictive analytics techniques have been developed in statistics, machine learning and data mining; however, many of these algorithms take a black-box approach in which data is input and future predictions are output with no insight into what goes on during the process. Unfortunately, such a closed system approach often leaves little room for injecting domain expertise and can result in frustration from analysts when results seem spurious or confusing. In order to allow for more human-centric approaches, the visualization community has begun developing methods to enable users to incorporate expert knowledge into the prediction process at all stages, including data cleaning, feature selection, model building and model validation. This paper surveys current progress and trends in predictive visual analytics, identifies the common framework in which predictive visual analytics systems operate, and develops a summarization of the predictive analytics workflow.

A visual analytical approach for transfer learning in classification

Abstract Classification can be highly challenging when the dataset is extremely large, or when the training data in the underlying domain are difficult to obtain. One feasible solution to this challenge is transfer learning, which extracts the knowledge from source tasks and applies the knowledge to target tasks. Extant transfer learning schemes typically assume that similarities between the source task and the target task to some degree. This assumption does not hold in certain actual applications; analysts unfamiliar with the learning strategy can be frustrated by the complicated transfer relations and the non-intuitive transfer process. This paper presents a suite of visual communication and interaction techniques to support the transfer learning process. Furthermore, a pioneering visual-assisted transfer learning methodology is proposed in the context of classification. Our solution includes a visual communication interface that allows for comprehensive exploration of the entire knowledge transfer process and the relevance among tasks. With these techniques and the methodology, the analysts can intuitively choose relevant tasks and data, as well as iteratively incorporate their experience and expertise into the analysis process. We demonstrate the validity and efficiency of our visual design and the analysis approach with examples of text classification.

The design space of construction tools for information visualization: A survey

Abstract Information visualization has been widely used to convey information from data and assist communication. There are enormous needs of efficient visualization design for users from diverse fields to leverage the power of data. As a result, emerging construction tools for information visualization focus on providing solutions with different aspects including expressiveness, accessibility, and efficiency. In this paper, we review existing works on declarative specifications and user interfaces for visualization construction. By summarizing their methods for producing information visualizations and efforts on improving usability, we express the design patterns in terms of a design space which describes the tools in several different aspects. We discuss how the design space can be applied to support further exploration of potential research topics in the future.

A Visual Analysis Approach for Community Detection of Multi-Context Mobile Social Networks

The problem of detecting community structures of a social network has been extensively studied over recent years, but most existing methods solely rely on the network structure and neglect the context information of the social relations. The main reason is that a context-rich network offers too much flexibility and complexity for automatic or manual modulation of the multifaceted context in the analysis process. We address the challenging problem of incorporating context information into the community analysis with a novel visual analysis mechanism. Our approach consists of two stages: interactive discovery of salient context, and iterative context-guided community detection. Central to the analysis process is a context relevance model (CRM) that visually characterizes the influence of a given set of contexts on the variation of the detected communities, and discloses the community structure in specific context configurations. The extracted relevance is used to drive an iterative visual reasoning process, in which the community structures are progressively discovered. We introduce a suite of visual representations to encode the community structures, the context as well as the CRM. In particular, we propose an enhanced parallel coordinates representation to depict the context and community structures, which allows for interactive data exploration and community investigation. Case studies on several datasets demonstrate the efficiency and accuracy of our approach.

EasySVM: A visual analysis approach for open-box support vector machines

Support vector machines (SVMs) are supervised learning models traditionally employed for classification and regression analysis. In classification analysis, a set of training data is chosen, and each instance in the training data is assigned a categorical class. An SVM then constructs a model based on a separating plane that maximizes the margin between different classes. Despite being one of the most popular classification models because of its strong performance empirically, understanding the knowledge captured in an SVM remains difficult. SVMs are typically applied in a black-box manner where the details of parameter tuning, training, and even the final constructed model are hidden from the users. This is natural since these details are often complex and difficult to understand without proper visualization tools. However, such an approach often brings about various problems including trial-and-error tuning and suspicious users who are forced to trust these models blindly.The contribution of this paper is a visual analysis approach for building SVMs in an open-box manner. Our goal is to improve an analyst’s understanding of the SVM modeling process through a suite of visualization techniques that allow users to have full interactive visual control over the entire SVM training process. Our visual exploration tools have been developed to enable intuitive parameter tuning, training data manipulation, and rule extraction as part of the SVM training process. To demonstrate the efficacy of our approach, we conduct a case study using a real-world robot control dataset.

A survey of network anomaly visualization

Network anomaly analysis is an emerging subtopic of network security. Network anomaly refers to the unusual behavior of network devices or suspicious network status. A number of intelligent visual tools are developed to enhance the ability of network security analysts in understanding the original data, ultimately solving network security problems. This paper surveys current progress and trends in network anomaly visualization. By providing an overview of network anomaly data, visualization tasks, and applications, we further elaborate on existing methods to depict various data features of network alerts, anomalous traffic, and attack patterns data. Directions for future studies are outlined at the end of this paper.

VisComposer: A Visual Programmable Composition Environment for Information Visualization

Abstract As the amount of data being collected has increased, the need for tools that can enable the visual exploration of data has also grown. This has led to the development of a variety of widely used programming frameworks for information visualization. Unfortunately, such frameworks demand comprehensive visualization and coding skills and require users to develop visualization from scratch. An alternative is to create interactive visualization design environments that require little to no programming. However, these tools only supports a small portion of visual forms. We present a programmable integrated development environment (IDE), VisComposer, that supports the development of expressive visualization using a drag-and-drop visual interface. VisComposer exposes the programmability by customizing desired components within a modularized visualization composition pipeline, effectively balancing the capability gap between expert coders and visualization artists. The implemented system empowers users to compose comprehensive visualizations with real-time preview and optimization features, and supports prototyping, sharing and reuse of the effects by means of an intuitive visual composer. Visual programming and textual programming integrated in our system allow users to compose more complex visual effects while retaining the simplicity of use. We demonstrate the performance of VisComposer with a variety of examples and an informal user evaluation.