Provenance-driven Representation of Crowdsourcing Data for Efficient Data Analysis

C. Martinez-Ortiz, L Aroyo, O. Inel, S. Champilomatis, A. Dumitrache, B. Timmermans

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review


Crowdsourcing has proved to be a feasible way of harnessing human computation for solving complex problems. However, crowdsourcing frequently faces various challenges: data handling, task reusability, and platform selection. Domain scientists rely on eScientists to find solutions for these challenges. CrowdTruth is a framework that builds on existing crowdsourcing platforms and provides an enhanced way to manage crowdsourcing tasks across platforms, offering solutions to commonly faced challenges. Provenance modeling proves means for documenting and examining scientific workflows. CrowdTruth keeps a provenance trace of the data flow through the framework, thus allowing to trace how data was transformed and by whom to reach its final state. In this way, eScientists have a tool to determine the impact that crowdsourcing has on enhancing their data.
Original languageEnglish
Title of host publication2015 IEEE 11th International Conference on e-Science
Publication statusPublished - 2015
Event2015 IEEE e-Science Conference -
Duration: 1 Jan 20151 Jan 2015


Conference2015 IEEE e-Science Conference


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