home libri books Fumetti ebook dvd top ten sconti 0 Carrello


Torna Indietro

li guoliang; wang jiannan; zheng yudian; fan ju; franklin michael j. - crowdsourced data management

Crowdsourced Data Management Hybrid Machine-Human Computing

; ; ; ;




Disponibilità: Normalmente disponibile in 15 giorni


PREZZO
108,98 €
NICEPRICE
103,53 €
SCONTO
5%



Questo prodotto usufruisce delle SPEDIZIONI GRATIS
selezionando l'opzione Corriere Veloce in fase di ordine.


Pagabile anche con Carta della cultura giovani e del merito, 18App Bonus Cultura e Carta del Docente


Facebook Twitter Aggiungi commento


Spese Gratis

Dettagli

Genere:Libro
Lingua: Inglese
Editore:

Springer

Pubblicazione: 02/2019
Edizione: Softcover reprint of the original 1st ed. 2018





Trama

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.





Sommario

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




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.











Altre Informazioni

ISBN:

9789811340123

Condizione: Nuovo
Dimensioni: 235 x 155 mm Ø 454 gr
Formato: Brossura
Illustration Notes:XII, 159 p. 66 illus., 42 illus. in color.
Pagine Arabe: 159
Pagine Romane: xii


Dicono di noi