  By incorporating human workers into the query execution process crowd-enabled
databases facilitate intelligent, social capabilities like completing missing
data at query time or performing cognitive operators. But despite all their
flexibility, crowd-enabled databases still maintain rigid schemas. In this
paper, we extend crowd-enabled databases by flexible query-driven schema
expansion, allowing the addition of new attributes to the database at query
time. However, the number of crowd-sourced mini-tasks to fill in missing values
may often be prohibitively large and the resulting data quality is doubtful.
Instead of simple crowd-sourcing to obtain all values individually, we leverage
the user-generated data found in the Social Web: By exploiting user ratings we
build perceptual spaces, i.e., highly-compressed representations of opinions,
impressions, and perceptions of large numbers of users. Using few training
samples obtained by expert crowd sourcing, we then can extract all missing data
automatically from the perceptual space with high quality and at low costs.
Extensive experiments show that our approach can boost both performance and
quality of crowd-enabled databases, while also providing the flexibility to
expand schemas in a query-driven fashion.
