Barzan Mozafari and Collaborators Chosen for Best Demo at ACM SIGMOD
The demo was of their Analytical Bootstrap (ABS) System, described in their paper, ABS: a System for Scalable Approximate Queries with Accuracy Guarantees. ABS is a fast error estimation system for a technique called Approximate Query Processing (AQP), which is used with data sampling to support timely and cost-effective analytic of big data. The ABS system is designed and developed to bridge the gap between the two existing approaches by dovetailing their merits while avoiding their limitations, inheriting general and automatic operation that allows for application to more general queries, but without the need for computationally-demanding simulations that would impact efficiency. These merits of ABS enable complex exploratory data analysis on large volumes of data.
Prof. Mozafari is passionate about building large-scale data-intensive systems that are more scalable, more robust, and more predictable, with a particular interest in database-as-a-service clouds, distributed systems, and crowdsourcing. In his research, he draws on advanced mathematical models to deliver practical database solutions, adapting concepts and tools from applied statistics, complexity theory, automata theory, and machine learning.
Prof. Mozafari received his PhD in Computer Science from the University of California Los Angeles in 2011. He joined the faculty of CSE at the University of Michigan in 2013 after two years as a postdoctoral researcher at Massachusetts Institute of Technology in the CSAIL Lab. Prof. Mozafari has won several awards and fellowships, including Best Paper Awards at SIGMOD 2012 and EuroSys 2013. He is affiliated with the Michigan Database Group and the Software Systems Lab in CSE.