In this post I want to report on our paper on Reactive Crowdsourcing presented at the WWW 2013 conference in Rio de Janeiro, Brasil.
Here is a quick summary of motivation and idea, together with some relevant materials:
Need for control
We believe that an essential aspect for building effective crowdsourcing computations is the ability of “controlling the crowd”, i.e. of dynamically adapting the behaviour of the crowdsourcing systems as response to the quantity and quality of completed tasks or to the availability and reliability of performers.
This new paper focuses on a machinery and methodology for deploying configurable, cross-platform, and adaptive crowdsourcing campaigns through a model-driven approach.
Control through declarative active rules
In the paper we present an approach to crowdsourcing which provides powerful and flexible crowd controls. We model each crowdsourcing application as composition of elementary task types and we progressively transform these high level specifications into the features of a reactive execution environment that supports task planning, assignment and completion as well as performer monitoring and exclusion. Controls are specified as declarative, active rules on top of data structures which are derived from the model of the application; rules can be added, dropped or modified, thus guaranteeing maximal exibility with limited effort. The paper applies modeling practices (as also explained in our book on model-driven software engineering).
Here is the presentation thatAlessandro Bozzon gave at WWW 2013:
Prototype and experiments
We have a prototype platform that implements the proposed framework. We have done extensive experiments with it. Our experimentations with different rule sets demonstrate how simple changes to the rules can substantially affect time, effort and quality involved in crowdsourcing activities.
Paper and related activities
The paper is a follow-up of our WWW2012 paper on Crowdsearcher, which focused on exploiting social networks and crowdsourcing platforms for improving search.
The paper nicely combines with another recent contribution of ours, presented at EDBT 2013, on finding the right crowd of experts on social networks for addressing a specific problem.