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This book provides an overview of crowdsourced data management. Covering all aspects including the workflow, algorithms and research potential, it particularly focuses on the latest techniques and recent advances. The authors identify three key aspects in determining the performance of crowdsourced data management: quality control, cost control and latency control. By surveying and synthesizing a wide spectrum of studies on crowdsourced data management, the book outlines important factors that need to be considered to improve crowdsourced data management. It also introduces a practical crowdsourced-database-system design and presents a number of crowdsourced operators. Self-contained and covering theory, algorithms, techniques and applications, it is a valuable reference resource for researchers and students new to crowdsourced data management with a basic knowledge of data structures and databases.

Crowdsourced Data Management: Hybrid Machine-Human Computing

1. Introduction.- 2. Crowdsourcing Background. 3. Quality Control.- 4. Cost Control.- 5. Latency Control.- 6. Crowdsourcing Database Systems and Optimization.- 7. Crowdsourced Operators.- Conclusion.