Crowdsourced Data Management: Hybrid Machine-Human Computing - Brossura

Li, Guoliang; Wang, Jiannan; Zheng, Yudian; Fan, Ju; Franklin, Michael J.

Brossura

ISBN 10: 9811340129 ISBN 13: 9789811340123

Casa editrice: Springer, 2019

Riassunto

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.

Su questo libro

Informazioni sull?autore

Guoliang Li is an associate professor at the Department of Computer Science, Tsinghua University, Beijing, China. His research interests include crowdsourced data management, big spatio-temporal data analytics, large-scale data cleaning and integration. He has published more than 100 papers at leading conferences and in journals, such as SIGMOD, VLDB, ICDE, SIGKDD, SIGIR, TODS, VLDB Journal, and TKDE. He is a PC co-chair of WAIM 2014, WebDB 2014, and NDBC 2016. He servers as associate editor for IEEE Transactions and Data Engineering, the VLDB Journal, BigData Research, IEEE Data Engineering Bulletin. He has regularly served as a PC member for several conferences, such as SIGMOD, VLDB, KDD, ICDE, WWW, IJCAI, and AAAI. His papers have been cited more than 4500 times. He received the VLDB 2017 Early Research Contribution Award, IEEE TCDE Early Career Award 2014, The national youth talent support program 2016, Young ChangJiang Scholar 2016, NSFC Excellent Young Scholars Award 2014, and the CCF Young Scientist award 2014.

Prof. Michael J. Franklin is the inaugural holder of the Liew Family Chair of Computer Science at the University of Chicago. An authority on databases, data analytics, data management and distributed systems, he also serves as senior advisor to the provost on computation and data science. Most recently he was the Thomas M. Siebel Professor of Computer Science and chair of the Computer Science Division of the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, where he currently is an adjunct professor. He co-founded and directs Berkeley’s Algorithms, Machines and People Laboratory (AMPLab), a leading academic big data analytics research center. The AMPLab won a National Science Foundation CISE "Expeditions in Computing" award, which was announced as part of the White House Big Data Research initiative in March 2012, and has received support from over 30 industrial sponsors. AMPLab has created industry-changing open source big data software including Apache Spark and BDAS, the Berkeley Data Analytics Stack.   At Berkeley Professor Franklin also served as an executive committee member for the Berkeley Institute for Data Science, a campus-wide initiative to advance data science environments. He is a fellow of the Association for Computing Machinery and two-time recipient of the ACM SIGMOD.

Dalla quarta di copertina

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.

Dettagli dell?edizione

• Editore: Springer
• Data di pubblicazione: 2019
• ISBN 10: 9811340129
• ISBN 13: 9789811340123
• Rilegatura: Copertina flessibile
• Lingua: Inglese
• Numero di pagine: 172
• Contatto del produttore: non disponibile