Título DMN for Data Quality Measurement and Assessment
Autores Valencia-Parra Á. , PARODY NÚÑEZ, MARÍA LUISA, Varela-Vaca Á.J. , Caballero I. , Gómez-López M.T.
Publicación externa No
Medio Lecture Notes in Business Information Processing
Alcance Conference Paper
Naturaleza Científica
Cuartil SJR 3
Impacto SJR 0.26
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078521375&doi=10.1007%2f978-3-030-37453-2_30&partnerID=40&md5=1e2345a4af99de454805a06de346cb43
Fecha de publicacion 01/01/2019
ISI 000723926800040
Scopus Id 2-s2.0-85078521375
DOI 10.1007/978-3-030-37453-2_30
Abstract Data Quality assessment is aimed at evaluating the suitability of a dataset for an intended task. The extensive literature on data quality describes the various methodologies for assessing data quality by means of data profiling techniques of the whole datasets. Our investigations are aimed to provide solutions to the need of automatically assessing the level of quality of the records of a dataset, where data profiling tools do not provide an adequate level of information. As most of the times, it is easier to describe when a record has quality enough than calculating a qualitative indicator, we propose a semi-automatically business rule-guided data quality assessment methodology for every record. This involves first listing the business rules that describe the data (data requirements), then those describing how to produce measures (business rules for data quality measurements), and finally, those defining how to assess the level of data quality of a data set (business rules for data quality assessment). The main contribution of this paper is the adoption of the OMG standard DMN (Decision Model and Notation) to support the data quality requirement description and their automatic assessment by using the existing DMN engines. © 2019, Springer Nature Switzerland AG.
Palabras clave Automatic guided vehicles; Enterprise resource management; Automatic assessment; Business rules; Data profiling; Data quality; Data quality assessment; Data requirements; Data set; Decision modeling; Data reduction
Miembros de la Universidad Loyola

Change your preferences Gestionar cookies