Pushing the Boundaries of Crowd-Enabled Databases with Query-Driven Schema Expansion
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, the authors 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 crowdsourcing to obtain all values individually, they leverage the usergenerated data found in the Social Web: By exploiting user ratings they build perceptual spaces, i.e., highly-compressed representations of opinions, impressions, and perceptions of large numbers of users.