Reliably Managing Unreliable Data

We've all seen news articles where data errors have severe implications on the
real world, like a 12-year old being labeled a terrorist by "the system" and
denied boarding on a flight with his parents. Heuristic guesses like this are
often treated as facts because databases simply have no better way of
representing them. In this talk, I outline our efforts to make uncertain data
management easier through a probabilistic database system called Mimir. First,
I will outline how rethinking uncertainty as a form of provenance metadata
facilitates more natural and more efficient interactions with the database.
Then, I will introduce Mimir's declarative approach to uncertainty in data,
which allows it to decouple query processing from from more mechanical concerns
like how to present uncertainty in query results, or which algorithms to use.

(This work is supported by NSF Award ACI-1640864, NPS Award #N00244-16-1-0022,
and gifts from Oracle)
Oliver Kennedy has been an assistant professor at the University at Buffalo,
SUNY since 2012. He earned his PhD from Cornell University in 2011. Oliver's
Online Data Interactions (ODIn) lab operates at the intersection of database
and programming language techniques. The ODIn lab is currently exploring
issues related to the usability of uncertain data management systems, making
index structures adapt to changing workloads, and modeling SQL workloads.