Abstract
Adequate crowd selection is an important factor in the success of crowdsourcing platforms, increasing the quality and relevance of crowd answers and their performance in different tasks. The optimal crowd selection can greatly vary depending on properties of the crowd and of the task. To this end, we present December, a declarative platform with novel capabilities for flexible crowd selection. December supports the personalized selection of crowd members via a dedicated query language Member-QL. This language enables specifying and combining common crowd selection criteria such as properties of a crowd member's profile and history, similarity between profiles in specific aspects and relevance of the member to a given task. This holistic, customizable approach differs from previous work that has mostly focused on dedicated algorithms for crowd selection in specific settings. To allow efficient query execution, we implement novel algorithms in December based on our generic, semantically-aware definitions of crowd member similarity and expertise. We demonstrate the effectiveness of December and Member-QL by using the VLDB community as crowd members and allowing conference participants to choose from among these members for different purposes and in different contexts.
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