优化实时众包的分析方法
Analytic Methods for Optimizing Realtime Crowdsourcing
摘要：
实时众包研究表明，通过管理一个小规模、反应迅速的工人群体，可以在几秒钟内招募到付费人群。实时人群使以交互速度响应的人群驱动系统成为可能：例如，相机，机器人和即时民意调查。到目前为止，这些技术主要是概念验证原型：研究尚未试图了解它们如何大规模工作或优化其成本/性能权衡。本文利用排队论分析了实时众包的预约模型，特别是其期望等待时间和成本的请求。我们提供了一个算法，允许请求者，以尽量减少其成本的性能要求。然后，我们提出并分析了三种技术来提高性能：推送通知，共享的保留池，和precruitment，这涉及到调用保留工人之前，任务实际到达。一项实验验证发现，预先确定的工作人员在发布任务后500毫秒开始执行任务，提供的结果低于终端用户保持流状态的一秒认知阈值。
英文摘要：
Realtime crowdsourcing research has demonstrated that it is possible to recruit paid crowds within seconds by managing a small, fast-reacting worker pool. Realtime crowds enable crowd-powered systems that respond at interactive speeds: for example, cameras, robots and instant opinion polls. So far, these techniques have mainly been proof-of-concept prototypes: research has not yet attempted to understand how they might work at large scale or optimize their cost/performance trade-offs. In this paper, we use queueing theory to analyze the retainer model for realtime crowdsourcing, in particular its expected wait time and cost to requesters. We provide an algorithm that allows requesters to minimize their cost subject to performance requirements. We then propose and analyze three techniques to improve performance: push notifications, shared retainer pools, and precruitment, which involves recalling retainer workers before a task actually arrives. An experimental validation finds that precruited workers begin a task 500 milliseconds after it is posted, delivering results below the one-second cognitive threshold for an end-user to stay in flow.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
