Fan Du

Coping with Volume and Variety in Temporal Event Sequences: Strategies for Sharpening Analytic Focus

The growing volume and variety of data presents both opportunities and challenges for visual analytics. Addressing these challenges is needed for big data to provide valuable insights and novel solutions for business, security, social media, and healthcare. In the case of temporal event sequence analytics it is the number of events in the data and variety of temporal sequence patterns that challenges users of visual analytic tools. This paper describes 15 strategies for sharpening analytic focus that analysts can use to reduce the data volume and pattern variety. Four groups of strategies are proposed: (1) extraction strategies, (2) temporal folding, (3) pattern simplification strategies, and (4) iterative strategies. For each strategy, we provide examples of the use and impact of this strategy on volume and/or variety. Examples are selected from 20 case studies gathered from either our own work, the literature, or based on email interviews with individuals who conducted the analyses and developers who observed analysts using the tools. Finally, we discuss how these strategies might be combined and report on the feedback from 10 senior event sequence analysts.

Visual analysis of set relations in a graph

Many applications can be modeled as a graph with additional attributes attached to the nodes. For example, a graph can be used to model the relationship of people in a social media website or a bibliographical dataset. Meanwhile, additional information is often available, such as the topics people are interested in and the music they listen to. Based on this additional information, different set relationships may exist among people. Revealing the set relationships in a network can help people gain social insight and better understand their roles within a community. In this paper, we present a visualization system for exploring set relations in a graph. Our system is designed to reveal three different relationships simultaneously: the social relationship of people, the set relationship among peoples items of interest, and the similarity relationship of the items. We propose two novel visualization designs: a) a glyph‐based visualization to reveal peoples set relationships in the context of their social networks; b) an integration of visual links and a contour map to show people and their items of interest which are clustered into different groups. The effectiveness of the designs has been demonstrated by the case studies on two representative datasets including one from a social music service website and another from an academic collaboration network.

High-Volume Hypothesis Testing: Systematic Exploration of Event Sequence Comparisons

Cohort comparison studies have traditionally been hypothesis driven and conducted in carefully controlled environments (such as clinical trials). Given two groups of event sequence data, researchers test a single hypothesis (e.g., does the group taking Medication A exhibit more deaths than the group taking Medication B?). Recently, however, researchers have been moving toward more exploratory methods of retrospective analysis with existing data. In this article, we begin by showing that the task of cohort comparison is specific enough to support automatic computation against a bounded set of potential questions and objectives, a method that we refer to as High-Volume Hypothesis Testing (HVHT). From this starting point, we demonstrate that the diversity of these objectives, both across and within different domains, as well as the inherent complexities of real-world datasets, still requires human involvement to determine meaningful insights. We explore how visualization and interaction better support the task of exploratory data analysis and the understanding of HVHT results (how significant they are, why they are meaningful, and whether the entire dataset has been exhaustively explored). Through interviews and case studies with domain experts, we iteratively design and implement visualization and interaction techniques in a visual analytics tool, CoCo. As a result of our evaluation, we propose six design guidelines for enabling users to explore large result sets of HVHT systematically and flexibly in order to glean meaningful insights more quickly. Finally, we illustrate the utility of this method with three case studies in the medical domain.

Episogram: Visual Summarization of Egocentric Social Interactions

The key challenges of visualizing social interaction data include the difficulties of understanding the general structure of social interactions and representing the data in the context of various user activities to reveal different behavior patterns. The design of the proposed interactive visualization tool Episogram is based on an anatomy of social interaction process in which the actors and objects involved can be formally represented as a time-varying tripartite network. The authors show the effectiveness of the proposed technique using real-world datasets and user studies.

Finding Similar People to Guide Life Choices: Challenge, Design, and Evaluation

People often seek examples of similar individuals to guide their own life choices. For example, students making academic plans refer to friends; patients refer to acquaintances with similar conditions, physicians mention past cases seen in their practice. How would they want to search for similar people in databases? We discuss the challenge of finding similar people to guide life choices and report on a need analysis based on 13 interviews. Our PeerFinder prototype enables users to find records that are similar to a seed record, using both record attributes and temporal events found in the records. A user study with 18 participants and four experts shows that users are more engaged and more confident about the value of the results to provide useful evidence to guide life choices when provided with more control over the search process and more context for the results, even at the cost of added complexity.

Z-Glyph: Visualizing outliers in multivariate data:

Outlier analysis techniques are extensively used in many domains such as intrusion detection. Today, even with the most advanced statistical learning techniques, human judgment still plays an important role in outlier analysis tasks due to the difficulty of defining and collecting outlier examples. This work seeks to tackle this problem by introducing a new visualization design, “Z-Glyph,” a family of glyphs designed to facilitate human judgment in outlier analysis of multivariate data. By employing a location-scale transformation, a Z-Glyph represents the “normal” data using regular shapes (e.g. straight line and circle), such that the abnormal data can be revealed when deviating from the regular shapes. Extensive controlled experiment and case studies based on real-world datasets indicate the superior performance of the Z-Glyph family, compared with the baselines, suggesting that the proposed design is able to leverage human perceptional features with statistical characterization. This study contributes to a more fundamental understanding about designing visual representations for revealing outliers in multivariate data, which can be applied as a building block in many domain-specific anomaly detection applications.

Simplifying Overviews of Temporal Event Sequences

Beginning the analysis of new data is often difficult as modern datasets can be overwhelmingly large. With visual analytics in particular, displays of large datasets quickly become crowded and unclear. Through observing the practices of analysts working with the event sequence visualization tool EventFlow, we identified three techniques to reduce initial visual complexity by reducing the number of event categories resulting in a simplified overview. For novice users, we suggest an initial pair of event categories to display. For advanced users, we provide six ranking metrics and display all pairs in a ranked list. Finally, we present the Event Category Matrix (ECM), which simultaneously displays overviews of every event category pair. In this work, we report on the development of these techniques through two formative usability studies and the improvements made as a result. The goal of our work is to investigate strategies that help users overcome the challenges associated with initial visual complexity and to motivate the use of simplified overviews in temporal event sequence analysis.

Personalizable and Interactive Sequence Recommender System

Sequence recommender systems assist people in making decisions, such as which product to purchase and what places to visit on vacation. Despite their ubiquity, most sequence recommender systems are black boxes and do not offer justifications for their recommendations or provide user controls for steering the algorithm. In this paper, we design and develop an interactive sequence recommender system (SeRIES) prototype that uses visualizations to explain and justify the recommendations and provides controls so that users may personalize the recommendations. We conducted a user study comparing SeRIES to a black-box system with 12 participants using real visitor trajectory data in Melbourne and show that SeRIES users are more informed about how the recommendations are generated, more confident in following the recommendations, and more engaged in the decision process.

iSphere: Focus+Context Sphere Visualization for Interactive Large Graph Exploration

Interactive exploration plays a critical role in large graph visualization. Existing techniques, such as zoom-and-pan on a 2D plane and hyperbolic browser facilitate large graph exploration by showing both the details of a focal area and its surrounding context that guides the exploration process. However, existing techniques for large graph exploration are limited in either providing too little context or presenting graphs with too much distortion. In this paper, we propose a novel focus+context technique, iSphere, to address the limitation. iSphere maps a large graph onto a Riemann Sphere that better preserves graph structures and shows greater context information. We conduct extensive experiment studies on different graph exploration tasks under various conditions. The results show that iSphere performs the best in task completion time compared to the baseline techniques in link and path exploration tasks. This research also contributes to understanding large graph exploration on small screens.