The physical and biological sciences are becoming more data driven, often due overwhelming quantities of data collected from satellites, telescopes, sequencers, and other sensors. One of the key issues for scientists who work with large datasets is efficient visualization of their data to extract patterns, observe anomalies, and debug their workflows. Though a variety of visualization tools exist to help people make sense of their data, these tools often rely on database management systems (or DBMSs) for data processing and storage; and unfortunately, DBMSs fail to process the data fast enough to support a fluid, interactive visualization experience.
My work blends optimization techniques from databases and methodology from HCI and visualization in order to support interactive exploration of large datasets. In this talk, I will first discuss Sculpin, a visual exploration system that learns user exploration patterns automatically, and exploits these patterns to pre-fetch data ahead of users as they explore. I will show that Sculpin’s pre-fetching techniques provide significant performance benefits compared to existing systems. I will then discuss ongoing work to extend the ideas behind Sculpin to more sophisticated analysis systems, such as Tableau Desktop, as well as ongoing efforts to standardize the way we evaluate visual data analysis systems in general.
Leilani Battle is joining the Univeristy of Maryland, College Park, as an Assistant Professor in the Computer Science Department, starting August 2018. Currently, she is completing a postdoc in the UW Interactive Data Lab with Prof. Jeffrey Heer. Her research interests focus on developing interactive data-intensive systems that can aid analysts in performing data exploration and analysis. Her current research is anchored in the field of databases, but utilizes research methodology and techniques from HCI and visualization to integrate data processing (databases) with interactive interfaces (HCI, visualization). She often collaborates with scientists, programmers and data analysts to both design and evaluate new visual exploration and analysis systems. She is also passionate about providing better infrastructure and support for underrepresented groups not only in STEM fields, but at all levels of academia. She is an NSF Graduate Research Fellowship Recipient (2012). She holds a PhD in Computer Science from MIT (2017) and a MS in Computer Science from MIT (2013) advised by Prof. Michael Stonebraker in the MIT Database Group, and a BS in Computer Engineering from UW (2011).