Barzan Mozafari, Department of Computer Science and Engineering
University of Michigan
Approximate Query Processing (AQP) has been a subject of academic research for over 25 years now. However, until recently, it has had little success in terms of commercial adoption. In talk, we explain the interface and deployment barriers that have historically slowed down the adoption of AQP by database vendors and enterprise users alike. We then discuss some of the recent advances that have successfully overcome some of these barriers. We also introduce several research directions and exciting opportunities that would not be possible in a database with precise answers. In particular, we explore several opportunities at the intersection of statistics and data management, including our Database Learning — a database system that learns and becomes smarter over time — as well as novel abstractions for speeding up machine learning workloads through approximate operators and error-computation tradeoffs.
Bio: Barzan Mozafari is a Morris Wellman Assistant Professor of Computer Science and Engineering at the University of Michigan, Ann Arbor, where he leads a research group designing the next generation of scalable databases using advanced statistical models. Prior to that, he was a Postdoctoral Associate at MIT. He earned his Ph.D. in Computer Science from UCLA in 2011. His research career has led to several open-source projects, including DBSeer (an automated database diagnosis tool), BlinkDB (a massively parallel approximate query engine), and SnappyData (an HTAP engine that empowers Apache Spark with transactions and real-time analytics). He has won the National Science Foundation CAREER award, as well as several best paper awards in ACM SIGMOD and EuroSys. He is the founder of Michigan Software Experts, and a strategic advisor to SnappyData, a company that commercializes the ideas introduced by BlinkDB.
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