Crowd-powered find algorithms

Anish Das Sarma, Aditya Parameswaran, Hector Garcia-Molina, Alon Halevy

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publication2014 IEEE 30th International Conference on Data Engineering, ICDE 2014
PublisherIEEE Computer Society
Number of pages12
ISBN (Print)9781479925544
StatePublished - 2014
Externally publishedYes
Event30th IEEE International Conference on Data Engineering, ICDE 2014 - Chicago, IL, United States
Duration: Mar 31 2014Apr 4 2014

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627


Other30th IEEE International Conference on Data Engineering, ICDE 2014
Country/TerritoryUnited States
CityChicago, IL

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems


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