Crowdy a framework for supporting socio-technical software ecosystems with stream-based human computation

The scale of collaboration between people and computers has expanded leading to new era of computation called crowdsourcing. A variety of problems can be solved with this new approach by employing people to complete tasks that cannot be computerized. However, the existing approaches are focused on simplicity and independency of tasks that fall short to solve complex and sophisticated problems. We present Crowdy, a general-purpose and extensible crowdsourcing platform that lets users perform computations to solve complex problems using both computers and human workers. The platform is developed based on the stream-processing paradigm in which operators execute on the continuos stream of data elements. The proposed architecture provides a standard toolkit of operators for computation and configuration support to control and coordinate resources. There is no rigid structure or requirement that could limit the problem-set, which can be solved with the stream-based approach. The stream- based human-computation approach is implemented and verified over different scenarios. Results show that sophisticated problems can be easily solved without significant amount of work for implementation. Also possible improvements are discussed and identified that is a promising future work for the existing work.