CrowdDB: Answering Queries with Crowdsourcing
                     Tim Kraska, UC Berkeley

Some queries cannot be answered by machines only. Processing such
queries requires human input, e.g., for providing information that is
missing from the database, for performing computationally difficult
functions and for matching, ranking, or aggregating results based on
fuzzy criteria. In this talk, I am going to describe the design and
implementation of CrowdDB, a database system that uses human input via
crowdsourcing to process queries that neither database systems nor
search engines can answer adequately. CrowdDB uses SQL both as a
language for posing complex queries and as a way to model data. While
CrowdDB leverages many concepts from traditional database systems,
there are also important differences. From a conceptual perspective,
the traditional closed-world assumption for query processing no longer
holds for human input, which is essentially unbounded. From an
implementation perspective, CrowdDB uses an operator-based query
engine but this engine is extended with special operators that
generate User Interfaces in order to solicit, integrate and cleanse
human input. Furthermore, performance and cost depend on a number of
different factors including worker affinity, training, fatigue,
motivation and location. Real-life experiments conducted using Amazon
Mechanical Turk show that CrowdDB is indeed able to process queries
that cannot be processed with traditional systems.