Data visualization is often used as the first step while performing a variety of analytical tasks. With the advent of large, high-dimensional datasets and strong interest in data science, there is a need for tools that can support rapid visual analysis. In this paper we describe our vision for a new class of visualization recommendation systems that can automatically identify and interactively recommend visualizations relevant to an analytical task.
This paper presents BigDAWG, a reference implementation of a new architecture for "Big Data" applications. Such applications not only call for large-scale analytics, but also for real-time streaming support, smaller analytics at interactive speeds, data visualization, and cross-storage-system queries. Guided by the principle that "one size does not fit all", we build on top of a variety of storage engines, each designed for a specialized use case. To illustrate the promise of this approach, we demonstrate its effectiveness on a hospital application using data from an intensive care unit (ICU). This complex application serves the needs of doctors and researchers and provides real-time support for streams of patient data. It showcases novel approaches for querying across multiple storage engines, data visualization, and scalable real-time analytics.
Content creation is a large component of the cost of creating educational software. Estimates are that approximately 200 hours of development time are required for every hour of instruction. We present an authoring tool designed to reduce this cost as it helps to refine and maintain content. The ASSISTment Builder is a tool designed to effectively create, edit, test, and deploy tutor content. The Web-based interface simplifies the process of tutor construction to allow users with little or no programming experience to develop content. We show the effectiveness of our Builder at reducing the cost of content creation to 40 hours for every hour of instruction. We describe new features that work toward supporting the life cycle of ITS content creation through maintaining and improving content as it is being used by students. The Variabilization feature allows the user to reuse tutoring content across similar problems. The Student Comments feature provides a way to maintain and improve content based on feedback from users. The Most Common Wrong Answer feature provides a way to refine remediation based on the users' answers. This paper describes our attempt to support the life cycle of content creation.
Data analysts operating on large volumes of data often rely on visualizations to interpret the results of queries. However, finding the right visualization for a query is a laborious and time-consuming task. We demonstrate SEEDB, a system that partially automates this task: given a query, SEEDB explores the space of all possible visualizations, and automatically identifies and recommends to the analyst those visualizations it finds to be most "interesting" or "useful". In our demonstration, conference attendees will see SEEDB in action for a variety of queries on multiple real-world datasets.
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