Real-time data backup in Geo-distributed data center networks against progressive disaster

2021 ◽  
Author(s):  
Lisheng Ma ◽  
Wei Su ◽  
Bin Wu ◽  
Xiaohong Jiang
Keyword(s):  
Real Time ◽  
Data Center ◽  
Distributed Data ◽  
Data Center Networks ◽  
Time Data ◽  
Data Backup ◽  
Real Time Data

scholarly journals Aesthetic Archives

2016 ◽  
Vol 41 (1) ◽  
pp. 11-23
Author(s):  
Michael Takeo Magruder ◽  
Jeremy Pilcher
Keyword(s):  
New Media ◽  
Real Time ◽  
Emerging Technologies ◽  
Distributed Data ◽  
Time Data ◽  
The Real ◽  
New Media Art ◽  
Data Flows ◽  
Real Time Data ◽  
Visual Artist

Michael Takeo Magruder, visual artist and researcher, discusses his digital and new media art and practice with Jeremy Pilcher, lawyer and academic, whose research engages with the intersection of art and law. Takeo's work asks viewers to question their relationship both to and within the real-time data flows generated by emerging technologies and the implications these have for archives. His art concerns the way institutions use such systems to create narratives that structure societies. This conversation discusses how Takeo's practice invites us, as individuals, to critically reflect on the implications of the stories that are both told to and about us by using gathered and distributed data.

scholarly journals Forecast Model Update Based on a Real-Time Data Processing Lambda Architecture for Estimating Partial Discharges in Hydrogenerator

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7242
Author(s):  
Fabio Henrique Pereira ◽  
Francisco Elânio Bezerra ◽  
Diego Oliva ◽  
Gilberto Francisco Martha de Souza ◽  
Ivan Eduardo Chabu ◽  
...  
Keyword(s):  
Real Time ◽  
Prediction Models ◽  
Forecast Model ◽  
Partial Discharges ◽  
Forecasting Model ◽  
Distributed Data ◽  
Time Data ◽  
Lambda Architecture ◽  
Real Time Data ◽  
Real Time Data Processing

The prediction of partial discharges in hydrogenerators depends on data collected by sensors and prediction models based on artificial intelligence. However, forecasting models are trained with a set of historical data that is not automatically updated due to the high cost to collect sensors’ data and insufficient real-time data analysis. This article proposes a method to update the forecasting model, aiming to improve its accuracy. The method is based on a distributed data platform with the lambda architecture, which combines real-time and batch processing techniques. The results show that the proposed system enables real-time updates to be made to the forecasting model, allowing partial discharge forecasts to be improved with each update with increasing accuracy.

Sign in / Sign up

Export Citation Format

Share Document