dc.creator | Valencia Parra, Álvaro | es |
dc.creator | Parody Núñez, María Luisa | es |
dc.creator | Varela Vaca, Ángel Jesús | es |
dc.creator | Caballero, Ismael | es |
dc.creator | Gómez López, María Teresa | es |
dc.date.accessioned | 2020-06-08T08:59:02Z | |
dc.date.available | 2020-06-08T08:59:02Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Valencia Parra, Á., Parody Núñez, M.L., Varela Vaca, Á.J., Caballero, I. y Gómez López, M.T. (2019). DMN for Data Quality Measurement and Assessment. En BPM 2019: International Conference on Business Process Management (362-374), Vienna, Austria: Springer. | |
dc.identifier.isbn | 978-3-030-37452-5 | es |
dc.identifier.uri | https://hdl.handle.net/11441/97520 | |
dc.description.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. | es |
dc.description.sponsorship | Ministerio de Ciencia y Tecnología RTI2018-094283-B-C33 | es |
dc.description.sponsorship | Ministerio de Ciencia y Tecnología RTI2018-094283-B-C31 | es |
dc.description.sponsorship | European Regional Development Fund SBPLY/17/180501/000293 | es |
dc.format | application/pdf | es |
dc.format.extent | 13 | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | BPM 2019: International Conference on Business Process Management (2019), p 362-374 | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Data quality | es |
dc.subject | Decision Model and Notation | es |
dc.subject | Data quality measurement | es |
dc.subject | Data quality assessment | es |
dc.title | DMN for Data Quality Measurement and Assessment | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | RTI2018-094283-B-C33 | es |
dc.relation.projectID | RTI2018-094283-B-C31 | es |
dc.relation.projectID | SBPLY/17/180501/000293 | es |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-030-37453-2_30 | es |
dc.identifier.doi | 10.1007/978-3-030-37453-2_30 | es |
dc.publication.initialPage | 362 | es |
dc.publication.endPage | 374 | es |
dc.eventtitle | BPM 2019: International Conference on Business Process Management | es |
dc.eventinstitution | Vienna, Austria | es |
dc.relation.publicationplace | Cham, Switzerland | es |
dc.contributor.funder | Ministerio de Ciencia Y Tecnología (MCYT). España | es |
dc.contributor.funder | Ministerio de Ciencia Y Tecnología (MCYT). España | es |
dc.contributor.funder | European Union (UE) | es |