Crowdsourcing systems, such as Amazon Mechanical Turk and CrowdFlower, utilize human power to perform difficult tasks, such as entity resolution, search, filtering, image matching, or clustering. The important issues of collecting and managing the large volume of data in these applications have attracted plenty of attention from the database community. Typically, data obtained from crowdsourcing platforms are to be considered as uncertain, because of various levels of quality obtained by crowd workers.
Uncertain data management has also received considerable attention in the data management community. Models, algorithms, systems, for fuzzy databases, probabilistic databases, or reasoning under incompleteness, have been proposed and numerous applications, from information integration to information extraction and data cleaning, have been identified. The objective of this workshop is to explore the connection between uncertain data management and crowdsourcing. How can the crowd reduce uncertainty in data obtained from automatic processes, such as schema matching or machine learning? How can uncertain data management techniques be applied to the modeling of the crowd?
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