In this work, we present a data science system to model and visualize student flow patterns based on electronic student data of a university. Our system is called eCamp. The datasets used by eCamp were previously disconnected and only maintained and accessed in a siloed manner by independent campus offices. At a campus-level, our models and visualization show how students make choices among hundreds of potential majors, as students gradually progress towards their sophomore, junior, and senior year. At a department-level, the student flow patterns revealed by eCamp show how each course plays a different role within a curriculum. eCamp further dives down to the granularity of the exact classes offered in each semester. At that level, eCamp shows how students navigate from one set of classes in one semester to another set in a subsequent semester. Previously, comprehensive information about student progression patterns at all of these level was simply unavailable. To that end, we also demonstrate how insights into such student flow patterns can support analytical tasks involving student outcomes, student retention, and curriculum design.

Interactive visualization has become a powerful insight-revealing medium. However, the close dependency of interactive visualization on its data inhibits its shareability. Users have to choose between the two extremes of (i) sharing non-interactive dataless formats such as images and videos, or (ii) giving access to their data and software to others with no control over how the data will be used. In this work, we fill the gap between the two extremes and present a new system, called Loom. Loom captures interactive visualizations as standalone dataless objects. Users can interact with Loom objects as if they still have the original software and data that created those visualizations. Yet, Loom objects are completely independent and can therefore be shared online without requiring the data or the visualization software. Loom objects are efficient to store and use, and provide privacy preserving mechanisms. We demonstrate Loom's efficacy with examples of scientific visualization using Paraview, information visualization using Tableau, and journalistic visualization from New York Times.

In this work, we propose a framework to simplify the creation of Augmented Reality (AR) extensions for web applications, without modifying the original web applications. We implemented the framework in an open source package called Alpaca. AR extensions developed using Alpaca appear as a web-browser extension, and automatically bridge the Document Object Model (DOM) of the web with the SceneGraph model of AR. To transform the web application into a multi-device, mixed-space web application, we designed a restrictive and minimized interface for cross-device event handling. We demonstrate our approach to develop mixed-space applications using three examples. These applications are, respectively, for exploring Google Books, exploring biodiversity distribution hosted by the National Park Service of the United States, and exploring YouTube’s recommendation engine. The first two cases show how a 3rd-party developer can create AR extensions without making any modifications to the original web applications. The last case serves as an example of how to create AR extensions when a developer creates a web application from scratch. Alpaca works on the iPhone X, the Google Pixel, and the Microsoft HoloLens.

In this paper, we propose using a decoupled architecture to create a microservice that can deliver scientific visualization remotely with efficiency, scalability, and superior availability, affordability and accessibility. Through our effort, we have created an open source platform, Tapestry, which can be deployed on Amazon AWS as a production use microservice. The applications we use to demonstrate the efficacy of the Tapestry microservice in this work are: (1) embedding interactive visualizations into lightweight web pages, (2) creating scientific visualization movies that are fully controllable by the viewers, (3) serving as a rendering engine for high-end displays such as power-walls, and (4) embedding data-intensive visualizations into augmented reality devices efficiently. In addition, we show results of an extensive performance study, and suggest how applications can make optimal use of microservices such as Tapestry.