Meta Data Quality Control Architecture in Data Warehousing

2012 ◽  
Vol 2 (4) ◽  
pp. 15-24 ◽  
Author(s):  
Ramesh Babu Palepu
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Data Warehousing ◽  
Control Architecture ◽  
Data Quality Control ◽  
Meta Data

scholarly journals Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning

2021 ◽  
pp. 106239
Author(s):  
Antonella D. Pontoriero ◽  
Giovanna Nordio ◽  
Rubaida Easmin ◽  
Alessio Giacomel ◽  
Barbara Santangelo ◽  
...  
Keyword(s):  
Quality Control ◽  
Deep Learning ◽  
Data Quality ◽  
Pet Imaging ◽  
Data Quality Control ◽  
Brain Pet

scholarly journals An interactive graphical system for XBT data quality control and visualization

2001 ◽  
Vol 27 (7) ◽  
pp. 867-876 ◽  
Author(s):  
Pankajakshan Thadathil ◽  
Aravind K Ghosh ◽  
J.S Sarupria ◽  
V.V Gopalakrishna
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Data Quality Control ◽  
Graphical System

Implementation of Marine Environment Monitoring Data Quality Control System

2014 ◽  
Vol 926-930 ◽  
pp. 4254-4257 ◽  
Author(s):  
Jin Xu ◽  
Da Tao Yu ◽  
Zhong Jie Yuan ◽  
Bo Li ◽  
Zi Zhou Xu
Keyword(s):  
Quality Control ◽  
Control System ◽  
Data Quality ◽  
Marine Environment ◽  
Quality Control System ◽  
Monitoring Data ◽  
Environment Monitoring ◽  
Data Quality Control ◽  
Labor Costs ◽  
Marine Environment Monitoring

Traditional artificial perception quality control methods of marine environment monitoring data have many disadvantages, including high labor costs and mistakes of data review. Based on GIS spatial analysis technology, Marine Environment Monitoring Data Quality Control System is established according to the Bohai Sea monitoring regulation. In the practical application process, it plays the role of improving efficiency of quality control, saving the manpower and financial resources. It also provides an important guarantee for the comprehensive analysis and management of marine environment data.

Data quality control in a multi-insitutional cancer trial

1980 ◽  
Vol 1 (2) ◽  
pp. 171-172
Author(s):  
M.M. Koretz ◽  
M. Kohler ◽  
E. McGuigan ◽  
J.F. Hannigan ◽  
B.W. Brown
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Data Quality Control ◽  
Cancer Trial

scholarly journals Monitoring the impacts of weather radar data quality control for quantitative application at the continental scale

2020 ◽  
Vol 27 (4) ◽  
Author(s):  
Daniel Michelson ◽  
Bjarne Hansen ◽  
Dominik Jacques ◽  
François Lemay ◽  
Peter Rodriguez
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Weather Radar ◽  
Radar Data ◽  
Data Quality Control ◽  
Continental Scale

scholarly journals A Data Quality Control Method for Seafloor Observatories: The Application of Observed Time Series Data in the East China Sea

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2628 ◽  
Author(s):  
Yusheng Zhou ◽  
Rufu Qin ◽  
Huiping Xu ◽  
Shazia Sadiq ◽  
Yang Yu
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
East China Sea ◽  
East China ◽  
Series Data ◽  
Data Quality Control ◽  
The East China Sea ◽  
China Sea ◽  
Observatory Data ◽  
Seafloor Observatory

With the construction and deployment of seafloor observatories around the world, massive amounts of oceanographic measurement data were gathered and transmitted to data centers. The increase in the amount of observed data not only provides support for marine scientific research but also raises the requirements for data quality control, as scientists must ensure that their research outcomes come from high-quality data. In this paper, we first analyzed and defined data quality problems occurring in the East China Sea Seafloor Observatory System (ECSSOS). We then proposed a method to detect and repair the data quality problems of seafloor observatories. Incorporating data statistics and expert knowledge from domain specialists, the proposed method consists of three parts: a general pretest to preprocess data and provide a router for further processing, data outlier detection methods to label suspect data points, and a data interpolation method to fill up missing and suspect data. The autoregressive integrated moving average (ARIMA) model was improved and applied to seafloor observatory data quality control by using a sliding window and cleaning the input modeling data. Furthermore, a quality control flag system was also proposed and applied to describe data quality control results and processing procedure information. The real observed data in ECSSOS were used to implement and test the proposed method. The results demonstrated that the proposed method performed effectively at detecting and repairing data quality problems for seafloor observatory data.

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