Articoli correlati a Modeling Fuzzy Spatiotemporal Data With Xml: 894

Modeling Fuzzy Spatiotemporal Data With Xml: 894 - Rilegato

Libro 392 di 538: Studies in Computational Intelligence

Ma, Zongmin; Bai, Luyi; Yan, Li

 
9783030419981: Modeling Fuzzy Spatiotemporal Data With Xml: 894
Rilegato
ISBN 10: 3030419983 ISBN 13: 9783030419981
Casa editrice: Springer Nature, 2020

Sinossi

This book offers in-depth insights into the rapidly growing topic of technologies and approaches to modeling fuzzy spatiotemporal data with XML. The topics covered include representation of fuzzy spatiotemporal XML data, topological relationship determination for fuzzy spatiotemporal XML data, mapping between the fuzzy spatiotemporal relational database model and fuzzy spatiotemporal XML data model, and consistencies in fuzzy spatiotemporal XML data updating. Offering a comprehensive guide to the latest research on fuzzy spatiotemporal XML data management, the book is intended to provide state-of-the-art information for researchers, practitioners, and graduate students of Web intelligence, as well as data and knowledge engineering professionals confronted with non-traditional applications that make the use of conventional approaches difficult or impossible. 

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Dalla quarta di copertina

This book offers in-depth insights into the rapidly growing topic of technologies and approaches to modeling fuzzy spatiotemporal data with XML. The topics covered include representation of fuzzy spatiotemporal XML data, topological relationship determination for fuzzy spatiotemporal XML data, mapping between the fuzzy spatiotemporal relational database model and fuzzy spatiotemporal XML data model, and consistencies in fuzzy spatiotemporal XML data updating. Offering a comprehensive guide to the latest research on fuzzy spatiotemporal XML data management, the book is intended to provide state-of-the-art information for researchers, practitioners, and graduate students of Web intelligence, as well as data and knowledge engineering professionals confronted with non-traditional applications that make the use of conventional approaches difficult or impossible. 

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Editore
Springer Nature
Data di pubblicazione
2020
Lingua
Inglese
ISBN 10 
3030419983
ISBN 13 
9783030419981
Rilegatura
Copertina rigida
Numero edizione
1
Numero di pagine
212
Contatto del produttore
GPSR Kontakt
ProductSafety@springernature.com

Slevogtstraße 3-5
Heidelberg
69126
Germania