Supporting Scaleable Online Statistical Processing
          Christopher 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 scaleable 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.