CSE alumnus and postdoctoral researcher Yongjoo Park (CSE PhD 2017) has been selected as a runner-up for the ACM SIGMOD Jim Gray Doctoral Dissertation Award for his dissertation, "Fast Data Analytics by Learning."
The annual SIGMOD Jim Gray Doctoral Dissertation Award recognizes excellent research by doctoral candidates in the database field.
In his dissertation, Dr. Park addresses the issue of increased query latencies when performing analytics over terabytes and petabytes of data. In these cases, horizontal scaling alone is not sufficient for achieving real-time data analytics, especially when the size of data grows faster than the computational power. Approximate query processing (AQP) intends to produce query answers in real-time at the cost of small quality losses in query answers. AQP is useful when we prefer obtaining approximate answers (e.g., with 1% error) within a few seconds compared to obtaining exact answers in hours.
Park shows that we can greatly speed up this AQP by learning from past computations and data. Specifically, his work work enhances three types of AQP – aggregation, searching, and visualization – by exploiting past computations and by building task-aware data synopses. For exploiting past computations and building task-aware synopses, this work incorporates statistical inference and optimizations techniques into the data analytics systems. The contributions in his work resulted in up to 20x speedups for many data analytics tasks.
Posted: May 1, 2018