A GIS with the Capacity for Managing Data quality information

2002 ◽  
pp. 264-284
Keyword(s):  
Data Quality ◽  
Quality Information

scholarly journals Data Cleaning in Knowledge Discovery Database-Data Mining (KDD-DM)

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2196-2199
Keyword(s):  
Data Mining ◽  
Information Systems ◽  
Data Quality ◽  
Knowledge Discovery ◽  
Information Management ◽  
Data Cleaning ◽  
Main Issue ◽  
Quality Information ◽  
Data Preparation ◽  
Paper Review

Data quality is a main issue in quality information management. Data quality problems occur anywhere in information systems. These problems are solved by Data Cleaning (DC). DC is a process used to determine inaccurate, incomplete or unreasonable data and then improve the quality through correcting of detected errors and omissions. Various process of DC have been discussed in the previous studies, but there is no standard or formalized the DC process. The Domain Driven Data Mining (DDDM) is one of the KDD methodology often used for this purpose. This paper review and emphasize the important of DC in data preparation. The future works was also being highlight.

scholarly journals Laying the Groundwork for Developing International Community Guidelines to Effectively Share and Reuse Digital Data Quality Information – Case Statement, Workshop Summary Report, and Path Forward

2020 ◽  
Author(s):  
Ge Peng ◽  
Carlo Lacagnina ◽  
Robert R. Downs ◽  
Ivana Ivanova ◽  
David F. Moroni ◽  
...  
Keyword(s):  
Data Quality ◽  
International Community ◽  
Digital Data ◽  
Quality Information ◽  
Summary Report ◽  
Workshop Summary ◽  
Fair Principles ◽  
Case Statement

This document provides background for and summarizes main takeaways of a workshop held virtually to kick off the development of community guidelines for consistently curating and representing dataset quality information in a way that is in line with the FAIR principles.

scholarly journals Perspectives on Citizen Science Data Quality

2021 ◽  
Vol 3 ◽  
Author(s):  
Robert R. Downs ◽  
Hampapuram K. Ramapriyan ◽  
Ge Peng ◽  
Yaxing Wei
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Quality Assessment ◽  
Citizen Science ◽  
Data Reuse ◽  
Quality Information ◽  
Science Data ◽  
Conducting Research

Information about data quality helps potential data users to determine whether and how data can be used and enables the analysis and interpretation of such data. Providing data quality information improves opportunities for data reuse by increasing the trustworthiness of the data. Recognizing the need for improving the quality of citizen science data, we describe quality assessment and quality control (QA/QC) issues for these data and offer perspectives on aspects of improving or ensuring citizen science data quality and for conducting research on related issues.

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