Analysts need the ability to intuitively explore their data before deciding how to clean it, model it, and present it to key decision makers. With the abundance of massive datasets in industry and science, analysts also need exploration systems that can process data quickly and efficiently, otherwise these systems will fail to keep pace with a user’s analytic flow. Addressing these challenges requires a deeper understanding of not only how system behavior influences user performance, but also how user behavior influences system performance.
In this talk, I will first discuss how system performance impacts the way people visually explore large datasets, in particular how system latency encourages user exploration bias. Then I will discuss how we can counteract these effects using behavior-driven optimizations, such as by learning user exploration patterns automatically, and exploiting these patterns to pre-fetch data ahead of users as they explore to reduce system latency. Then I will discuss how I synthesize evaluation methodology from HCI, visualization, and data management into executable benchmarks for testing database management systems under real-time interactive analysis scenarios. Finally, I will discuss my ongoing research to further characterize, optimize, and evaluate interactive data exploration systems to promote more reliable, rigorous, and engaging analyses.
Leilani Battle is an Assistant Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. She was previously an Assistant Professor at the University of Maryland, College Park. Her research spans the areas of data management, HCI, and data visualization. Her research interests focus on developing interactive data-intensive systems that can aid analysts in performing complex data exploration and analysis. Prof. Battle was named one of the 35 Innovators Under 35 by the MIT Technology Review in 2020. She is also an NSF Graduate Research Fellowship Recipient (2012), and her research is currently supported by an Adobe Research Award, a VMWare Early Career Faculty Grant, an NSF CISE CRII Award (2019-2021), and an ORAU Ralph E. Powe Junior Faculty Enhancement Award (2019-2020). In 2017, she completed a postdoc in the UW Interactive Data Lab. She holds an MS (2013) and PhD (2017) in Computer Science from MIT, where she was a member of the MIT Database Group, and a BS in Computer Engineering from UW (2011), where she was a member of the UW Database Group.