Supporting Scalable Online Statistical Processing

July 20, 2007
Chris Jermaine | University of Florida

Data warehousing and analytic processing have been active areas of database research and development for nearly two decades, and many experts now consider these problems to be “solved”, especially with regard to performance. However, an argument can be made that users and databases have simply reached an uneasy truce with regard to analytic processing. If users avoid ad-hoc, exploratory queries that might take days to execute, then the database performs just fine.

In this talk, I will describe query processing in database system called DBO that is designed from the ground up to support interactive analytic processing. Like traditional relational database systems, DBO can run database queries from start to finish and produce exact answers in a scalable fashion. Our initial results show that DBO has all of performance of a traditional system when processing analytic queries. However, unlike any existing research or production system, DBO is able to produce statistically meaningful approximate answers at all times throughout query execution. These answers are continuously updated from start to finish, even for “huge” queries requiring arbitrary quantities of temporary secondary storage. Thus, a user can stop execution whenever satisfied with the query accuracy, which may translate to dramatic time savings during exploratory processing.

Speaker Details

Chris Jermaine is an assistant professor of computer science at the University of Florida, where he has been since earning his PhD degree from Georgia Tech in 2002. His research is generally concerned with databases and data analysis. With his students and colleagues at UF he has published many papers in forums such as VLDB, SIGMOD, and SIGKDD. He is the recipient of an NSF CAREER award for his research on approximate query processing.