DOOR: A data model for crowdsourcing with application to emergency response

Abstract

Crowdsourcing allows us to employ collective human intelligence and resources in completing tasks in a wide variety of domains, such as mapping, translation, emergency response, and even fund raising. It first involves identification of a problem that can be solved using crowdsourcing and then its decomposition into tasks that workers can finish in a timely manner. Worker engagement analysis and data quality analysis are done afterwards. Such analysis activities are not supported by current platforms and are done in an ad-hoc fashion leading to duplicate efforts. As a first step towards realizing such analysis mechanisms, we propose a Data mOdel for crOwdsouRcing (DOOR), which is based on a fuzzy Entity-Relationship model in order to capture the uncertainty that is inherent in any crowdsourcing process. To illustrate its application, we have chosen the problem of collection of data about incidents for emergency response.