Supporting Visual Analytics with Scalable Visualization Recommendations

Data scientists rely on visualizations to interpret the data returned by queries. However, when working on large datasets, identifying and generating visualizations that show relevant or desired trends in data can be tedious and time-consuming. We present a system, SeeDB, that intelligently explores the space of visualizations, evaluates promising visualizations, and recommends those that it deems "interesting" or "useful" . As part of this system, we are designing sampling-based algorithms for generating visualizations on very large datasets rapidly, while preserving visual properties essential for drawing correct insights. My talk will cover both our initial design for SeeDB, as well as one of our scalable visualization generation algorithms.
Aditya Parameswaran is an Assistant Professor in Computer Science at the University of Illinois (UIUC). He completed his PhD from Stanford in 2013, following which he spent a year at MIT and then started at Illinois in August 2014. He is broadly interested in data analytics, with research results in human computation, visual and interactive analytics, information extraction and integration, and recommender systems. Aditya is a recipient of the Arthur Samuel award for the best dissertation in CS at Stanford (2014), the SIGMOD Jim Gray dissertation award (2014), the SIGKDD dissertation award runner up (2014), a Google Faculty Research Award (2015), the Key Scientific Challenges Award from Yahoo! Research (2010), three best-of-conference citations (VLDB 2010, KDD 2012 and ICDE 2014), the Terry Groswith graduate fellowship at Stanford (2007), and the Gold Medal in Computer Science at IIT Bombay (2007). His research group is supported with funding from by the NIH, the NSF, and Google.