FROG: A Fast and Reliable Crowdsourcing Framework (Technical Report)

For decades, the crowdsourcing has gained much attention from both academia and industry, which outsources a number of tasks to human workers. Existing works considered improving the task accuracy through voting or learning methods, they usually did not fully take into account reducing the latency of the task completion. When a task requester posts a group of tasks (e.g., sentiment analysis), and one can only obtain answers of all tasks after the last task is accomplished. As a consequence, the time delay of even one task in this group could delay the next step of the task requester's work from minutes to days, which is quite undesirable for the task requester. Inspired by the importance of the task accuracy and latency, in this paper, we will propose a novel crowdsourcing framework, namely Fast and Reliable crOwdsourcinG framework (FROG), which intelligently assigns tasks to workers, such that the latencies of tasks are reduced and the expected accuracies of tasks are met. Specifically, our FROG framework consists of two important components, task scheduler and notification modules. For the task scheduler module, we formalize a FROG task scheduling (FROG-TS) problem, in which the server actively assigns workers with tasks with high reliability and low latency. We prove that the FROG-TS problem is NP-hard. Thus, we design two heuristic approaches, request-based and batch-based scheduling. For the notification module, we define an efficient worker notifying (EWN) problem, which only sends task invitations to those workers with high probabilities of accepting the tasks. To tackle the EWN problem, we propose a smooth kernel density estimation approach to estimate the probability that a worker accepts the task invitation. Through extensive experiments, we demonstrate the effectiveness and efficiency of our proposed FROG platform on both real and synthetic data sets.