Syndetics cover image
Image from Syndetics

Crowdsourced Data Management [electronic resource] : Hybrid Machine-Human Computing / by Guoliang Li, Jiannan Wang, Yudian Zheng, Ju Fan, Michael J. Franklin.

By: Contributor(s): Material type: TextPublisher: Singapore : Springer Nature Singapore : Imprint: Springer, 2018Edition: 1st ed. 2018Description: 1 online resource (XII, 159 p. 66 illus.). 42 illus. in colorContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9789811078477
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 005.7 23
LOC classification:
  • QA76.9.B45
Online resources:
Contents:
1. Introduction -- 2. Crowdsourcing Background. 3. Quality Control -- 4. Cost Control -- 5. Latency Control -- 6. Crowdsourcing Database Systems and Optimization -- 7. Crowdsourced Operators -- Conclusion.
In: Springer Nature eBookSummary: 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.
No physical items for this record

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

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.

Print version record.

Licensed e-book