Efficient and Flexible Crowdsourcing of Specialized Tasks With Precedence Constraints - 2018


Several companies currently use crowdsourcing to leverage external furthermore internal crowds to perform specialized work, and therefore strategies of improving efficiency are vital. Tasks in crowdsourcing systems with specialised work have multiple steps and each step needs multiple skills. Steps might have completely different flexibilities in terms of getting service from one or multiple agents because of varying levels of dependency among components of steps. Steps of a task might have precedence constraints among them. Moreover, there are variations in loads of different types of tasks requiring totally different ability sets and availabilities of agents with different talent sets. Considering these constraints together necessitate the look of novel schemes to allocate steps to agents. In addition, large crowdsourcing systems need allocation schemes that are easy, quick, decentralized, and offer customers (task requesters) the liberty to decide on agents. During this Project, we tend to study the performance limits of such crowdsourcing systems and propose efficient allocation schemes that provably meet the performance limits underneath these additional requirements. We demonstrate our algorithms on knowledge from a crowdsourcing platform run by a nonprofit company and show vital enhancements over current follow.

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