Crowdsourcing technologies such as Amazon's Mechanical Turk ("MTurk") service have exploded in popularity in recent years. These services are increasingly used for complex human-reliant data processing tasks, such as labelling a collection of images, combining two sets of images to identify people that appear in both, or extracting sentiment from a corpus of text snippets. There are several challenges in designing a workflow that filters, aggregates, sorts and joins human-generated data sources. Currently, crowdsourcingbased workflows are hand-built, resulting in increasingly complex programs. Additionally, developers must hand-optimize tradeoffs among monetary cost, accuracy, and time to completion of results. These challenges are well-suited to a declarative query interface that allows developers to describe their worflow at a high level and automatically optimizes workflow and tuning parameters. In this demonstration, we will present Qurk, a novel query system that allows humanbased processing for relational databases. The audience will interact with the system to build queries and monitor their progress. The audience will also see Qurk from an MTurk user's perspective, and complete several tasks to better understand how a query is processed.
Qurk REF provides an interactive environment for humans to build queries and monitor their progress.
Demonstration of Qurk: a query processor for humanoperators
SIGMOD '11
Computer Science