scholarly journals Cloud–edge cooperation for meteorological radar big data: a review of data quality control

2021 ◽  
Author(s):  
Zhichen Hu ◽  
Xiaolong Xu ◽  
Yulan Zhang ◽  
Hongsheng Tang ◽  
Yong Cheng ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Quality Control ◽  
Data Quality ◽  
Real Time ◽  
Rapid Development ◽  
Production Factor ◽  
Weather Data ◽  
Data Quality Control ◽  
Cloud Computing Service ◽  
Convective Weather

AbstractWith the rapid development of information technology construction, increasing specialized data in the field of informatization have become a hot spot for research. Among them, meteorological data, as one of the foundations and core contents of meteorological informatization, is the key production factor of meteorology in the era of digital economy as well as the basis of meteorological services for people and decision-making services. However, the existing centralized cloud computing service model is unable to satisfy the performance demand of low latency, high reliability and high bandwidth for weather data quality control. In addition, strong convective weather is characterized by rapid development, small convective scale and short life cycle, making the complexity of real-time weather data quality control increased to provide timely strong convective weather monitoring services. In order to solve the above problems, this paper proposed the cloud–edge cooperation approach, whose core idea is to effectively combine the advantages of edge computing and cloud computing by taking full advantage of the computing resources distributed at the edge to provide service environment for users to satisfy the real-time demand. The powerful computing and storage resources of the cloud data center are utilized to provide users with massive computing services to fulfill the intensive computing demands.

Intelligent Data Quality Control of Real-time Rig Data

2013 ◽  
Author(s):  
Arghad Arnaout ◽  
Philipp Zoellner ◽  
Gerhard Thonhauser ◽  
Neil Johnstone
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Real Time ◽  
Data Quality Control

Fault detection in a real-time monitoring network for water quality in the lagoon of Venice (Italy)

2004 ◽  
Vol 50 (11) ◽  
pp. 51-58 ◽  
Author(s):  
S. Ciavatta ◽  
R. Pastres ◽  
Z. Lin ◽  
M.B. Beck ◽  
C. Badetti ◽  
...  
Keyword(s):  
Water Quality ◽  
Quality Control ◽  
Data Quality ◽  
Real Time ◽  
Monitoring Network ◽  
Recursive Estimation ◽  
Data Quality Control ◽  
Real Time Monitoring ◽  
Lagoon Of Venice ◽  
On Line

In the context of monitoring water quality in natural ecosystems in real time, on-line data quality control is a very important issue for effective system surveillance and for optimizing maintenance of the monitoring network. This paper presents some applications of recursive state-parameter estimation algorithms to real-time detection of signal drift in high-frequency observations. Two continuous-discrete recursive estimation schemes, namely the Extended Kalman Filter and the Recursive Prediction Error algorithm, were applied to assuring the quality of the dissolved oxygen (DO) time series, as obtained from the Lagoon of Venice (Italy) during August 2002, through the real-time monitoring network of the Magistrato alle Acque (the Venice Water Authority). Results demonstrate the effectiveness of the methodology in early detection of a probable drift in the DO signal. Comparison of these results with those obtained from the application of a related recursive scheme (a Dynamic Linear Regression procedure) suggests the strong benefits of approaching the problem of on-line data quality control with several (not merely a single) independent such estimation methods.

Real time data quality control applied on an IOT sensor water quality network

2020 ◽  
Author(s):  
Matthias Maeyens ◽  
Brianna Pagán ◽  
Piet Seuntjens ◽  
Bino Maiheu ◽  
Nele Desmet ◽  
...  
Keyword(s):  
Water Quality ◽  
Quality Control ◽  
Surface Water ◽  
Data Quality ◽  
Real Time ◽  
Water Quality Monitoring ◽  
Data Quality Control ◽  
Time Data ◽  
Data Set ◽  
Real Time Data

<p>In recent years, extend periods of drought have been affecting the water quality and availability in  the Flanders region in Belgium. Especially the coastal region experienced an increased salinization of ground and surface water. The Flemish government therefore decided to invest in a dense IoT water quality monitoring network aiming to deploy 2500 water quality sensors  primarily in surface water but also in ground water and sewers. The goal of this "Internet of Water" project is to establish an operational state of the art monitoring and prediction system in support of future water policy in Flanders. </p><p>Since Flanders is a relatively small region (13,522 km²), placing this many sensors will result in one of the most dense surface water quality sensor networks in the world. Each sensor will continuously measure several indicators of water quality and transmit the data wirelessly. This allows us to continuously monitor the water quality and build a big enough data set to be able to use a more data driven approach to predicting changes  in water quality. However, as with any sensor system, the quality of the data can vary in time due to problems with the sensors, incorrect calibration or unforeseen issues. Real-time data quality control is crucial to prevent unsound decisions due to faulty data.</p><p>This contribution will give a general overview of the network and it’s specifications, but mainly focus on the implementation of the data stream as well as methods that are implemented to guarantee good data quality. More specifically the architecture and setup of a real-time data quality control system is described. Which will add quality control flags to measurements.  This system is  integrated with the NGSI API introduced by FIWARE, which forces us to make specific design decisions to acommodate to the NGSI API.</p>

scholarly journals Implementation of Weigh-in-Motion Data Quality Control and Real-Time Dashboard Development

2019 ◽  
Author(s):  
Wayne A. Bunnell ◽  
Howell Li ◽  
Michael Reed ◽  
Timothy Wells ◽  
Dwayne Harris ◽  
...  
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Real Time ◽  
Data Quality Control ◽  
Motion Data ◽  
Weigh In Motion
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