I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
Combining fault-tolerant persistence and low-latency stream..:
, In:
2023 IEEE International Conference on Big Data (BigData)
,
Militone, Gabriele Scaffidi
;
Apiletti, Daniele
;
Malnati, Giovanni
- p. 3149-3153 , 2023
Link:
https://doi.org/10.1109/BigData59044.2023.10386896
RT T1
2023 IEEE International Conference on Big Data (BigData)
: T1
Combining fault-tolerant persistence and low-latency streaming access to binary data for AI models
UL https://suche.suub.uni-bremen.de/peid=ieee-10386896&Exemplar=1&LAN=DE A1 Militone, Gabriele Scaffidi A1 Apiletti, Daniele A1 Malnati, Giovanni YR 2023 K1 Soft sensors K1 Streaming media K1 Media K1 Throughput K1 Video surveillance K1 Data models K1 Artificial intelligence K1 Data Management K1 Microservice Architecture K1 Data processing architectures K1 Transactionality K1 BigData SP 3149 OP 3153 LK http://dx.doi.org/https://doi.org/10.1109/BigData59044.2023.10386896 DO https://doi.org/10.1109/BigData59044.2023.10386896 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)