Application of Requirement-oriented Data Quality Evaluation Method

2018 ◽  
Author(s):  
Zhenyu Liu ◽  
Qiang Chen ◽  
Lizhi Cai
Keyword(s):  
Data Quality ◽  
Quality Evaluation ◽  
Evaluation Method

Acceleration data quality assessment for bridge structural health monitoring via statistical and deep-learning approach

2021 ◽  
Author(s):  
Huaqiang Zhong ◽  
Limin Sun ◽  
José Turmo ◽  
Ye Xia
Keyword(s):  
Structural Health Monitoring ◽  
Data Quality ◽  
Health Monitoring ◽  
Structural Damage ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Operating Conditions ◽  
Monitoring Data ◽  
Statistical Characteristics ◽  
Structural Health

<p>In recent years, the safety and comfort problems of bridges are not uncommon, and the operating conditions of in-service bridges have received widespread attention. Many large-span key bridges have installed structural health monitoring systems and collected massive amounts of data. Monitoring data is the basis of structural damage identification and performance evaluation, and it is of great significance to analyze and evaluate its quality. This paper takes the acceleration monitoring data of the main girder and arch rib of a long-span arch bridge as the research object, analyzes and summarizes the statistical characteristics of the data, summarizes 6 abnormal data conditions, and proposes a data quality evaluation method of convolutional neural network. This paper conducts frequency statistics on the acceleration vibration amplitude of the bridge in December 2018 in hours. In order to highlight the end effect of frequency statistics, the whole is amplified and used as network input for training and data quality evaluation. The results are good. It provides another new method for structural monitoring data quality evaluation and abnormal data elimination.</p>

scholarly journals RESEARCH ON EVALUATION METHOD OF REAL ESTATE REGISTRATION DATA QUALITY

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 749-754
Author(s):  
C. X. Chen ◽  
H. Zhang ◽  
K. Jiang ◽  
H. T. Zhao ◽  
W. Xie ◽  
...  
Keyword(s):  
Quality Control ◽  
Real Estate ◽  
Data Quality ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Evaluation Model ◽  
Data Quality Control ◽  
Registration Data ◽  
Quality Evaluation Model

Abstract. In recent years, China has promulgated the "Civil Code of the People's Republic of China", "Implementation Rules of the Provisional Regulations on Real Estate Registration" and other laws and regulations, which have protected citizens' rights and obligations in real estate from the legal system. It shows that the quality of real estate registration data is very important. At present, there is no set of standards for evaluating the quality of real estate registration data. This article sorts out the production process of real estate registration data and focuses on the four stages of production: digitization results, field surveys and surveying and mapping results, group building results, integration and association. As a result, the main points of real estate registration data quality control were put forward, and a quality evaluation model was developed. Taking Beijing's real estate registration historical archives integrated data quality inspection as an application case, it shows that the quality evaluation model has been successfully applied to actual projects, ensuring the quality of Beijing real estate registration data. It also provides a reference for the next step in China's quality control of the unified registration of natural resources confirmation.

A Research on Data Quality Evaluation Method of Repeat Lines in Airborne Gravity Survey

2008 ◽  
Vol 51 (5) ◽  
pp. 1093-1099 ◽  
Author(s):  
Zhi-Hong GUO ◽  
Sheng-Qing XIONG ◽  
Jian-Xin ZHOU ◽  
Xi-Hua ZHOU
Keyword(s):  
Data Quality ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Gravity Survey ◽  
Airborne Gravity ◽  
Airborne Gravity Survey

scholarly journals A PRAXIS ON DATA QUALITY EVALUATION OF UNDERGROUND PIPELINE

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 811-816
Author(s):  
H. T. Zhao ◽  
J. Zhou ◽  
C. F. Jing ◽  
X. F. Li
Keyword(s):  
Data Quality ◽  
Spatial Data ◽  
Quality Evaluation ◽  
Sampling Method ◽  
Evaluation Method ◽  
Underground Pipeline ◽  
Data Sampling ◽  
Vector Data ◽  
Business Applications ◽  
Underground Pipelines

Abstract. Underground pipelines are known as “life line”. With the rapid developing of city, more and more pipelines like power lines will move into underground. Facing the complex environment from underground and relationship with other kinds of pipeline, the data quality evaluation is very crucial for academic and business applications. This paper introduced our praxis on underground pipeline data quality on a real project. The datasets are mainly composing of vector data about 15 GB size, covers 3 counties, worked with 3 teams. The workflow, data sampling method and quality evaluation method were engaged in our work. This work can extend to other underground pipeline projects or similar spatial data quality evaluation projects.

Objective quality evaluation method of stereo image based on steerable pyramid

2013 ◽  
Vol 32 (3) ◽  
pp. 710-714
Author(s):  
Jin-jin WEI ◽  
Su-mei LI ◽  
Wen-juan LIU ◽  
Yan-jun ZANG
Keyword(s):  
Quality Evaluation ◽  
Evaluation Method ◽  
Stereo Image ◽  
Objective Quality ◽  
Steerable Pyramid

Methods for detecting and correcting contextual data quality problems

2021 ◽  
Vol 25 (4) ◽  
pp. 763-787
Author(s):  
Alladoumbaye Ngueilbaye ◽  
Hongzhi Wang ◽  
Daouda Ahmat Mahamat ◽  
Ibrahim A. Elgendy ◽  
Sahalu B. Junaidu
Keyword(s):  
Data Quality ◽  
Quality Evaluation ◽  
Web Applications ◽  
Support Vector ◽  
Similarity Metrics ◽  
Distributed Data ◽  
Contextual Data ◽  
Formal Taxonomy ◽  
E Learning ◽  
High Scalability

Knowledge extraction, data mining, e-learning or web applications platforms use heterogeneous and distributed data. The proliferation of these multifaceted platforms faces many challenges such as high scalability, the coexistence of complex similarity metrics, and the requirement of data quality evaluation. In this study, an extended complete formal taxonomy and some algorithms that utilize in achieving the detection and correction of contextual data quality anomalies were developed and implemented on structured data. Our methods were effective in detecting and correcting more data anomalies than existing taxonomy techniques, and also highlighted the demerit of Support Vector Machine (SVM). These proposed techniques, therefore, will be of relevance in detection and correction of errors in large contextual data (Big data).

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