是时候了：专家众包中的在线Macrotask测序
It's about time: Online Macrotask Sequencing in Expert Crowdsourcing
摘要：
我们介绍了任务分配和排序（TAS）的问题，它增加了时间轴的角度专家众包优化。专家众包涉及宏任务，如文档编写、产品设计或Web开发，这些任务比典型的二进制微任务花费更多时间，需要专家技能，对主题具有不同程度的知识，并要求众包工作者建立在彼此的贡献之上。目前的作品通常假设离线优化模型，认为工人和任务到达已知，不考虑时间的元素。然而，实际上，时间是至关重要的：任务有最后期限，专家工作人员只能在特定的时间段，工作人员/任务的到达并不是先验的。我们的工作是第一个解决在线，异构，时间受限的宏任务的最佳任务排序的问题。我们提出tas在线，在线算法，旨在完成尽可能多的任务，尽可能在预算内，所需的质量和给定的时间轴，没有未来的输入信息，有关工作发布日期或工人的可用性。将tas-online与四个典型的基准测试进行比较，结果表明，该方法可以完成更多的作业，降低作业流时间，提高作业质量。这项工作对提高当前众包平台的服务质量具有实际意义，使他们能够为专家任务提供成本，质量和时间上的改进。
英文摘要：
We introduce the problem of Task Assignment and Sequencing (TAS), which adds the timeline perspective to expert crowdsourcing optimization. Expert crowdsourcing involves macrotasks, like document writing, product design, or web development, which take more time than typical binary microtasks, require expert skills, assume varying degrees of knowledge over a topic, and require crowd workers to build on each other's contributions. Current works usually assume offline optimization models, which consider worker and task arrivals known and do not take into account the element of time. Realistically however, time is critical: tasks have deadlines, expert workers are available only at specific time slots, and worker/task arrivals are not known a-priori. Our work is the first to address the problem of optimal task sequencing for online, heterogeneous, time-constrained macrotasks. We propose tas-online, an online algorithm that aims to complete as many tasks as possible within budget, required quality and a given timeline, without future input information regarding job release dates or worker availabilities. Results, comparing tas-online to four typical benchmarks, show that it achieves more completed jobs, lower flow times and higher job quality. This work has practical implications for improving the Quality of Service of current crowdsourcing platforms, allowing them to offer cost, quality and time improvements for expert tasks.
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