Data analytics often involves data exploration, where a data set is repeatedly analyzed to understand root causes, find patterns, or extract insights. Such analysis is frequently bottlenecked by the underlying data processing system, as analysts wait for their queries to complete against a complex multilayered software stack. In this talk, I’ll describe some exploratory analytics applications we’ve build in the MIT database group over the past few years, and will then describe some of the challenges and opportunities that arise when building more efficient data exploration systems that will allow these applications to be come truly interactive, even when processing billions of data points.
Samuel Madden is a Professor of Electrical Engineering and Computer Science in MIT's Computer Science and Artificial Intelligence Laboratory. His research interests include databases, distributed computing, and networking. Madden is a leader in the emerging field of "Big Data", heading the Intel Science and Technology Center (ISTC) for Big Data, a multi-university collaboration on developing new tools for processing massive quantities of data. He also leads BigData@CSAIL, an industry-backed initiative to unite researchers at MIT and leaders from industry to investigate the issues related to systems and algorithms for data that is high rate, massive, or very complex.
Madden received his Ph.D. from the University of California at Berkeley in 2003 where he worked on the TinyDB system for data collection from sensor networks. Madden was named one of Technology Review's Top 35 Under 35 in 2005, and is the recipient of several awards, including an NSF CAREER Award in 2004, a Sloan Foundation Fellowship in 2007, best paper awards in VLDB 2004 and 2007, and a best paper award in MobiCom 2006.