Fig. 1. User interface of OnSet. The list of sets is on the right (the orange bar indicates the number of elements in the set). Each set is represented as a rectangular region with the set elements being smaller interior rectangles. This view shows two individual sets (one is dimmed) on the left and two compositions of individual sets on the right.Abstract-Visualizing sets to reveal relationships between constituent elements is a complex representational problem. Recent research presents several automated placement and grouping techniques to highlight connections between set elements. However, these techniques do not scale well for sets with cardinality greater than one hundred elements. We present OnSet, an interactive, scalable visualization technique for representing large-scale binary set data. The visualization technique defines a single, combined domain of elements for all sets, and models each set by the elements that it both contains and does not contain. OnSet employs direct manipulation interaction and visual highlighting to support easy identification of commonalities and differences as well as membership patterns across different sets of elements. We present case studies to illustrate how the technique can be successfully applied across different domains such as bio-chemical metabolomics and task and event scheduling.

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.

UnitedUniverse is a second screen transmedia experience aimed at supporting understanding of a complex storyworld presented across media artifacts. Using the highly interconnected and allusive Marvel Cinematic Universe as a primary example, United Universe abstracts a story into the fundamental elements of characters, events, items, and locations, and presents them in a "glanceable" manner to the interactor. As significant story elements are referenced, the application provides explanatory information on the second screen. Drawing from the larger story world made up of multiple comic books, movies, games, and television shows, United Universe aims to provide clarity and background for the novice, and depth and engagement for more knowledgeable viewers.

In an increasingly global and competitive business landscape, firms must collaborate and partner with others to ensure survival, growth, and innovation. Understanding the evolutionary composition of a firm’s relationship portfolio and the underlying formation strategy is a difficult task given the multidimensional, temporal, and geospatial nature of the data. In collaboration with senior executives, we iteratively determine core design requirements and then design and implement an interactive visualization system that enables decision makers to gain both systemic (macro) and detailed (micro) insights into a firm’s alliance activities and discover patterns of multidimensional relationship formation. Our system provides both sequential and temporal representation modes, a rich set of additive cross-linked filters, the ability to stack multiple alliance portfolios, and a dynamically updated activity state model visualization to inform decision makers of past and likely future relationship moves. We illustrate our tool with examples of alliance activities of firms listed on the S8P 500. A controlled experiment and real-world evaluation with practitioners and researchers reveals significant evidence of the value of our visual analytic tool. Our design study contributes to design science by addressing a known problem (i.e., alliance portfolio analysis) with a novel solution (interactive, pixel-based multivariate visualization) and to the rapidly emerging area of data-driven visual decision support in corporate strategy contexts. We conclude with implications and future research opportunities.