Crowd-powered find algorithms.

We consider the problem of using humans to find a bounded number of items satisfying certain properties, from a data set. For instance, we may want humans to identify a select number of travel photos from a data set of photos to display on a travel website, or a candidate set of resumes that meet certain requirements from a large pool of applicants. Since data sets can be enormous, and since monetary cost and latency of data processing with humans can be large, optimizing the use of humans for finding items is an important challenge. We formally define the problem using the metrics of cost and time, and design optimal algorithms that span the skyline of cost and time, i.e., we provide designers the ability to control the cost vs. time trade-off. We study the deterministic as well as error-prone human answer settings, along with multiplicative and additive approximations. Lastly, we study how we may design algorithms with specific expected cost and time measures.