| Title | Development of a Crowd-Powered System Architecture for Knowledge Discovery in Scientific Domains |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Correia, A., Fonseca, B., Paredes, H., Schneider, D., Jameel, S. |
| Conference Name | 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) |
| Date Published | Oct. 2019 |
| Publisher | IEEE |
| ISBN Number | 978-1-7281-4569-3 |
| Keywords | Classification algorithms, Collaboration, collaborative conceptual modeling, complementary strengths, compositionality, computer algorithms, Computer architecture, Crowd Behavior, crowd science, crowd workers, crowd-machine hybrid interaction, crowd-powered human-machine hybrid system, crowd-powered system architecture, crowdsourcing, data mining, global scientific output, human computer interaction, human crowds, human factors, human-centered AI, Knowledge discovery, learning (artificial intelligence), massively collaborative science, metadata, Metadata Discovery Problem, multidisciplinary views, natural sciences computing, pubcrawl, resilience, Resiliency, Scalability, scientific domains, scientific knowledge discovery, Task Analysis |
| Abstract | A substantial amount of work is often overlooked due to the exponential rate of growth in global scientific output across all disciplines. Current approaches for addressing this issue are usually limited in scope and often restrict the possibility of obtaining multidisciplinary views in practice. To tackle this problem, researchers can now leverage an ecosystem of citizens, volunteers and crowd workers to perform complex tasks that are either difficult for humans and machines to solve alone. Motivated by the idea that human crowds and computer algorithms have complementary strengths, we present an approach where the machine will learn from crowd behavior in an iterative way. This approach is embodied in the architecture of SciCrowd, a crowd-powered human-machine hybrid system designed to improve the analysis and processing of large amounts of publication records. To validate the proposal's feasibility, a prototype was developed and an initial evaluation was conducted to measure its robustness and reliability. We conclude this paper with a set of implications for design. |
| URL | https://ieeexplore.ieee.org/document/8914637 |
| DOI | 10.1109/SMC.2019.8914637 |
| Citation Key | correia_development_2019 |
Development of a Crowd-Powered System Architecture for Knowledge Discovery in Scientific Domains