2017 IEEE 33rd International Conference on Data Engineering (2017)
San Diego, California, USA
April 19, 2017 to April 22, 2017
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDE.2017.26
Many important data management and analytics tasks cannot be completely addressed by automated processes. These tasks, such as entity resolution, sentiment analysis, and image recognition can be enhanced through the use of human cognitive ability. Crowdsouring is an effective way to harness the capabilities of people (i.e., the crowd) to apply human computation for such tasks. Thus, crowdsourced data management has become an area of increasing interest in research and industry. We identify three important problems in crowdsourced data management. (1) Quality Control: Workers may return noisy or incorrect results so effective techniques are required to achieve high quality, (2) Cost Control: The crowd is not free, and cost control aims to reduce the monetary cost, (3) Latency Control: The human workers can be slow, particularly compared to automated computing time scales, so latency-control techniques are required. There has been significant work addressing these three factors for designing crowdsourced tasks, developing crowdsourced data manipulation operators, and optimizing plans consisting of multiple operators. We survey and synthesize a wide spectrum of existing studies on crowdsourced data management.
Crowdsourcing, Quality control, Noise measurement, Conferences, Image resolution, Image recognition, Time factors
G. Li, J. Wang, Y. Zheng and M. Franklin, "Crowdsourced Data Management: A Survey," 2017 IEEE 33rd International Conference on Data Engineering(ICDE), San Diego, California, USA, 2017, pp. 39-40.