<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In the information age, with the continuous development of Internet technology, information data occupies every field of contemporary society. The development of the big data age makes these data more and more prominent. While users read the information they need from these massive data, data quality has also become a concern of users. A large number of data are preprocessed before data analysis, such as some duplicate values, missing values deal with inaccurate and other abnormal data, and filter the data through the data cleaning system to improve the standardization of the data, so as to improve the analysis efficiency of the data, reduce some unnecessary expenses, and save time and effort. The data cleaning system in this paper is implemented based on flash framework. Taking Python as the main language for data cleaning, technical cleaning and standard integration are carried out for some structural problems, duplication problems and missing problems of some different source data. Through the processing of abnormal data, the data quality and data analysis efficiency are greatly improved.</p>
</abstract>

In the information age, with the continuous development of Internet technology, information data occupies every field of contemporary society. The development of the big data age makes these data more and more prominent. While users read the information they need from these massive data, data quality has also become a concern of users. A large number of data are preprocessed before data analysis, such as some duplicate values, missing values deal with inaccurate and other abnormal data, and filter the data through the data cleaning system to improve the standardization of the data, so as to improve the analysis efficiency of the data, reduce some unnecessary expenses, and save time and effort. The data cleaning system in this paper is implemented based on flash framework. Taking Python as the main language for data cleaning, technical cleaning and standard integration are carried out for some structural problems, duplication problems and missing problems of some different source data. Through the processing of abnormal data, the data quality and data analysis efficiency are greatly improved.

Research on Oil Well Data Cleaning System

School of Computer Science and Engineering

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Research on Oil Well Data Cleaning System"}

In the information age, with the continuous development of Internet technology, information data occupies every field of contemporary society. The development...

Research on Oil Well Data Cleaning System

Online veröffentlicht: 24. Mai 2023

Seitenbereich: 43 - 51

DOI: https://doi.org/10.2478/ijanmc-2022-0026

Schlüsselwörter
Data Cleaning, Cleaning System, Oil Well, Outlier Handling

© 2022 Yao Feng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Research on Oil Well Data Cleaning System

Online veröffentlicht: 24. Mai 2023

Seitenbereich: 43 - 51

DOI: https://doi.org/10.2478/ijanmc-2022-0026

SchlüsselwörterData Cleaning, Cleaning System, Oil Well, Outlier Handling

© 2022 Yao Feng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Data Cleaning, Cleaning System, Oil Well, Outlier Handling