Purpose: This paper relates the definition of data quality procedures for knowledge organizations such as Higher Education Institutions. The main purpose is to present the flexible approach developed for monitoring th...Purpose: This paper relates the definition of data quality procedures for knowledge organizations such as Higher Education Institutions. The main purpose is to present the flexible approach developed for monitoring the data quality of the European Tertiary Education Register(ETER) database, illustrating its functioning and highlighting the main challenges that still have to be faced in this domain.Design/methodology/approach: The proposed data quality methodology is based on two kinds of checks, one to assess the consistency of cross-sectional data and the other to evaluate the stability of multiannual data. This methodology has an operational and empirical orientation. This means that the proposed checks do not assume any theoretical distribution for the determination of the threshold parameters that identify potential outliers, inconsistencies, and errors in the data. Findings: We show that the proposed cross-sectional checks and multiannual checks are helpful to identify outliers, extreme observations and to detect ontological inconsistencies not described in the available meta-data. For this reason, they may be a useful complement to integrate the processing of the available information.Research limitations: The coverage of the study is limited to European Higher Education Institutions. The cross-sectional and multiannual checks are not yet completely integrated.Practical implications: The consideration of the quality of the available data and information is important to enhance data quality-aware empirical investigations, highlighting problems, and areas where to invest for improving the coverage and interoperability of data in future data collection initiatives.Originality/value: The data-driven quality checks proposed in this paper may be useful as a reference for building and monitoring the data quality of new databases or of existing databases available for other countries or systems characterized by high heterogeneity and complexity of the units of analysis without relying on pre-specified theoretical d展开更多
This paper presents a case study of reverse engineering closely-spaced free-form shapes. The raw point cloud data captured from a body scanner was processed to filter most noise and redundancy. They were used to gener...This paper presents a case study of reverse engineering closely-spaced free-form shapes. The raw point cloud data captured from a body scanner was processed to filter most noise and redundancy. They were used to generate meshes through triangulation of points. Upon removal of inconsistencies resulted from residual noise, the clean-up meshes were then used to reconstruct the free-form surfaces that represented a fabric layer and a human body surface. The solid produced between these two surfaces is the fabric-over-body model. It helped generate a FEA (finite-element analysis) mesh with quality checks, such as distortion and stretch, were performed for all the meshed tetrahedral elements. The purpose is to prepare a FEA-ready model for future CFD (computational fluid dynamics) analysis.展开更多
基金support of the European Commission ETER Project (No. 934533-2017-AO8-CH)H2020 RISIS 2 project (No. 824091)。
文摘Purpose: This paper relates the definition of data quality procedures for knowledge organizations such as Higher Education Institutions. The main purpose is to present the flexible approach developed for monitoring the data quality of the European Tertiary Education Register(ETER) database, illustrating its functioning and highlighting the main challenges that still have to be faced in this domain.Design/methodology/approach: The proposed data quality methodology is based on two kinds of checks, one to assess the consistency of cross-sectional data and the other to evaluate the stability of multiannual data. This methodology has an operational and empirical orientation. This means that the proposed checks do not assume any theoretical distribution for the determination of the threshold parameters that identify potential outliers, inconsistencies, and errors in the data. Findings: We show that the proposed cross-sectional checks and multiannual checks are helpful to identify outliers, extreme observations and to detect ontological inconsistencies not described in the available meta-data. For this reason, they may be a useful complement to integrate the processing of the available information.Research limitations: The coverage of the study is limited to European Higher Education Institutions. The cross-sectional and multiannual checks are not yet completely integrated.Practical implications: The consideration of the quality of the available data and information is important to enhance data quality-aware empirical investigations, highlighting problems, and areas where to invest for improving the coverage and interoperability of data in future data collection initiatives.Originality/value: The data-driven quality checks proposed in this paper may be useful as a reference for building and monitoring the data quality of new databases or of existing databases available for other countries or systems characterized by high heterogeneity and complexity of the units of analysis without relying on pre-specified theoretical d
文摘This paper presents a case study of reverse engineering closely-spaced free-form shapes. The raw point cloud data captured from a body scanner was processed to filter most noise and redundancy. They were used to generate meshes through triangulation of points. Upon removal of inconsistencies resulted from residual noise, the clean-up meshes were then used to reconstruct the free-form surfaces that represented a fabric layer and a human body surface. The solid produced between these two surfaces is the fabric-over-body model. It helped generate a FEA (finite-element analysis) mesh with quality checks, such as distortion and stretch, were performed for all the meshed tetrahedral elements. The purpose is to prepare a FEA-ready model for future CFD (computational fluid dynamics) analysis.
