Pushing the Boundaries of Crowd-enabled Databases with Query-driven Schema Expansion

TL;DR

This paper introduces a method for dynamic schema expansion in crowd-enabled databases by leveraging social web data and perceptual spaces, reducing crowd-sourcing costs and improving data quality.

Contribution

It presents a novel approach that combines user-generated social data with expert crowd-sourcing to automatically and efficiently expand database schemas at query time.

Findings

Enhanced schema flexibility with minimal crowd-sourcing

High-quality data extraction from perceptual spaces

Improved performance and data quality in crowd-enabled databases

Abstract

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.