December: A declarative tool for crowd member selection

Yael Amsterdamer, Tova Milo, Amit Somech, Brit Youngmann

Research output: Contribution to journalConference articlepeer-review

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 exible 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, semanticallyaware 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.

Original languageEnglish
Pages (from-to)1485-1488
Number of pages4
JournalProceedings of the VLDB Endowment
Volume9
Issue number13
DOIs
StatePublished - 2015
Event42nd International Conference on Very Large Data Bases, VLDB 2016 - New Delhi, India
Duration: 5 Sep 20169 Sep 2016

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