Difference between revisions of "Visualization of disagreement-based quality metrics of crowdsourcing data"

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(New page: {{Masterproject |Master name=AI and Communication |Student name=Cristea Tatiana |Project start date=2014/04/01 |Project end date=2014/08/31 |Supervisor= Lora Aroyo |Thesis title=Visualizat...)
 
 
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|Project start date=2014/04/01
 
|Project start date=2014/04/01
 
|Project end date=2014/08/31
 
|Project end date=2014/08/31
|Supervisor= Lora Aroyo
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|Supervisor=Lora Aroyo
 
|Thesis title=Visualization of disagreement-based quality metrics of crowdsourcing data
 
|Thesis title=Visualization of disagreement-based quality metrics of crowdsourcing data
|Finished=No
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|Finished=Yes
 
|Thesis=Thesis.pdf
 
|Thesis=Thesis.pdf
 
|Poster=Posternaam.pdf
 
|Poster=Posternaam.pdf
 
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In this research we propose a visual-analytics model in the context of Crowd-Watson: a framework that uses Crowdsourcing methods to generate gold standard training and evaluation data for machine learning purposes. The proposed visual-analytic model aims to optimize the Crowdsourcing process and improve its quality by efficiently dealing with large and noisy amounts of data  obtained as result of the  Crowdsourcing process. Requirements for the dynamic, scalable and interactive visualizations were extracted from literature and through interviews with users of the framework.
 
In this research we propose a visual-analytics model in the context of Crowd-Watson: a framework that uses Crowdsourcing methods to generate gold standard training and evaluation data for machine learning purposes. The proposed visual-analytic model aims to optimize the Crowdsourcing process and improve its quality by efficiently dealing with large and noisy amounts of data  obtained as result of the  Crowdsourcing process. Requirements for the dynamic, scalable and interactive visualizations were extracted from literature and through interviews with users of the framework.

Latest revision as of 07:25, 9 December 2014


has title::Visualization of disagreement-based quality metrics of crowdsourcing data
status: finished
Master: project within::AI and Communication
Student name: student name::Cristea Tatiana
Dates
Start start date:=2014/04/01
End end date:=2014/08/31
Supervision
Supervisor: Lora Aroyo
Thesis: has thesis::Media:Thesis.pdf
Poster: has poster::Media:Posternaam.pdf

Signature supervisor



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Abstract

In this research we propose a visual-analytics model in the context of Crowd-Watson: a framework that uses Crowdsourcing methods to generate gold standard training and evaluation data for machine learning purposes. The proposed visual-analytic model aims to optimize the Crowdsourcing process and improve its quality by efficiently dealing with large and noisy amounts of data obtained as result of the Crowdsourcing process. Requirements for the dynamic, scalable and interactive visualizations were extracted from literature and through interviews with users of the framework.