Multimodal biometric fusion using data quality information

2005 ◽  
Author(s):  
Yu-Chiang Wang ◽  
David Casasent
Keyword(s):  
Data Quality ◽  
Quality Information ◽  
Using Data ◽  
Biometric Fusion

scholarly journals Using Data to Improve Programs: Assessment of a Data Quality and Use Intervention Package for Integrated Community Case Management in Malawi

2017 ◽  
Vol 5 (3) ◽  
pp. 355-366
Author(s):  
Elizabeth Hazel ◽  
Emmanuel Chimbalanga ◽  
Tiyese Chimuna ◽  
Humphreys Nsona ◽  
Angella Mtimuni ◽  
...  
Keyword(s):  
Case Management ◽  
Data Quality ◽  
Community Case Management ◽  
Intervention Package ◽  
Integrated Community Case Management ◽  
Using Data

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 Promoting Sustainable Data-Based Decision-Making in the Korean Educational Information Disclosure System

2020 ◽  
Vol 12 (17) ◽  
pp. 6762
Author(s):  
Young Hyeo Joo
Keyword(s):  
Decision Making ◽  
Data Quality ◽  
Information Disclosure ◽  
School Context ◽  
Data Use ◽  
Institutional Support ◽  
School Teachers ◽  
Using Data ◽  
Educational Information ◽  
Quality School

This study investigates the Korean Educational Information Disclosure System (KEIDS) and suggests sustainable development policies for KEIDS to improve school-level data-based decision-making (DBDM) from the educational administration’s perspective. It also raises the following questions: What are the barriers impeding effective data use by the KEIDS? How do school teachers, who are directly involved in using data, effectively prepare for DBDM using the KEIDS? How can the KEIDS be improved for DBDM concerning quality data, school context, and institutional support? To answer these questions, the study reviewed KEIDS-related documents and interviewed 24 school teachers through an interpretive case study approach while using a research framework of data quality, school contexts, and institutional support. Its results highlight important issues with the KEIDS and sustainable DBDM, in other words, teachers and administrators are not always conscious of the need for using data; the lack of data use understanding creates issues among principal leadership and teachers’ involvement and cooperation; the quality of the student data in the Schoolinfo system is questionable; and the central education authority focuses on simply disclosing student data rather than pursuing the goal of the KEIDS. The study suggests facilitating DBDM through the KEIDS in terms of data quality, school context, and institutional support.

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