scholarly journals Database Quality Plan

2020 ◽  
Author(s):  
Keyword(s):  
Database Quality

scholarly journals Development of a Database Quality Assurance Score (dQAS) to validate the Nigeria AIDS Indicator and Impact Survey (NAIIS) Database. (Preprint)

10.2196/25752 ◽  
2020 ◽  
Author(s):  
Hamisu M. Salihu ◽  
Zenab Yusuf ◽  
Deepa Dongarwar ◽  
Sani H. Aliyu ◽  
Rafeek A. Yusuf ◽  
...  
Keyword(s):  
Quality Assurance ◽  
Database Quality

The EUROCARE-5 study on cancer survival in Europe 1999–2007: Database, quality checks and statistical analysis methods

2015 ◽  
Vol 51 (15) ◽  
pp. 2104-2119 ◽  
Author(s):  
Silvia Rossi ◽  
Paolo Baili ◽  
Riccardo Capocaccia ◽  
Massimiliano Caldora ◽  
Eugenio Carrani ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Cancer Survival ◽  
Analysis Methods ◽  
Quality Checks ◽  
Statistical Analysis Methods ◽  
Database Quality

CDROM DATABASE QUALITY

1993 ◽  
Vol 17 (5) ◽  
pp. 310-312
Author(s):  
Michael Casale
Keyword(s):  
Database Quality

scholarly journals Shipowner’s Impact on Planned Maintenance System Database Quality Grades Resemblance Equalization

2018 ◽  
Vol 7 (1) ◽  
pp. 5-22
Author(s):  
Ladislav Stazić ◽  
Ivan Komar ◽  
Luka Mihanović ◽  
Antonija Mišura
Keyword(s):  
Data Quality ◽  
Development Process ◽  
Database Development ◽  
Maintenance System ◽  
System Database ◽  
Database Quality

This paper analyzes the shipowner’s role in the development process of ship’s computerized planned maintenance system database, paying specific attention to his/her impact on database quality grades resemblance equalization. The paper describes the database development process from the realization that the database is needed to the installation on board vessel, and all shipowner’s and developer’s actions in the process. The computerized databases of five shipping companies were tested using questionnaire developed for this purpose. The evaluation results are shown in several tables to facilitate overview and comparison of data. The paper provides the data of office user’s workload with regard to the administration and correction of data besides database quality grades: that data serves to portrait the relation between data quality in the databases and importance given to the process of development and maintenance of the databases. The analysis of the results yields numerical values as indicators of shipowner's input on the resemblance of grades of both scenarios and on the database quality grades resemblance equalization. The conclusion shows how the shipowner affects the ship's computerized planned maintenance system database and what actions should be performed to obtain the final product (database, i.e. data in the database) as good as possible and, thus, maintenance of the ship and the whole system. The expected results of the paper are:- To analyze in detail the database ordering and development process,- To pinpoint controlling areas for the database quality,- To analyze the shipowner’s impact on the database quality grades resemblance equalization.

Dimensions of Database Quality

2005 ◽  
pp. 886-891 ◽  
Author(s):  
John A. Hoxmeier
Keyword(s):  
Product Development ◽  
Product Development Process ◽  
Software Projects ◽  
Design And Implementation ◽  
Growing Body ◽  
Quality Dimensions ◽  
Analysis Design ◽  
Ultimate Objective ◽  
Information Domain ◽  
Database Quality

The ultimate objective of database analysis, design, and implementation is to establish an electronic repository that faithfully represents the conceptual and logical model of the manageable aspects of a user’s information domain. Enterprise and Web-enabled databases must satisfy a wide set of demands and constituents. Software engineering in general and database development in particular can be a complex, complicated process. There is probably no other product development process that faces the same amount of uncertainty, which may account for the high failure rate of software projects. This chapter expands on the growing body of literature in the area of data quality by proposing additions to a hierarchy of database quality dimensions that include model and behavioral factors in addition to the process and data factors.

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