dc.creator | Roldán García, María del Mar | es |
dc.creator | García Nieto, José Manuel | es |
dc.creator | Maté, Alejandro | es |
dc.creator | Trujillo, Juan | es |
dc.creator | Aldana Montes, José F. | es |
dc.date.accessioned | 2021-05-11T10:01:31Z | |
dc.date.available | 2021-05-11T10:01:31Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Roldán García, M.d.M., García Nieto, J.M., Maté, A., Trujillo, J. y Aldana Montes, J.F. (2021). Ontology-driven approach for KPI meta-modelling, selection and reasoning. International Journal of Information Management, 58 (June 2021-102018) | |
dc.identifier.issn | 0268-4012 | es |
dc.identifier.uri | https://hdl.handle.net/11441/108848 | |
dc.description.abstract | A key challenge in current Business Analytics (BA) is the selection of suitable indicators for business objectives.
This requires the exploration of business data through data-driven approaches, while modelling business strategies
together with domain experts in order to represent domain knowledge. In particular, Key Performance
Indicators (KPIs) allow human experts to properly model ambiguous enterprise goals by means of quantitative
variables with numeric ranges and clear thresholds. Besides business-related domains, the usefulness of KPIs
has been shown in multiple domains, such as: Education, Healthcare and Agriculture. However, finding
accurate KPIs for a given strategic goal still remains a complex task, specially due to the discrepancy
between domain as-sumptions and data facts. In this regard, the semantic web emerges as a powerful
technology for knowledge representation and data modeling through explicit representation formats and
standards such as RDF(S) and OWL. By using this technology, the semantic annotation of indicators of
business objectives would enrich the strategic model obtained. With this motivation, an ontology-driven
approach is proposed to formally concep-tualize essential elements of indicators, covering: performance,
results, measures, goals and relationships of a given business strategy. In this way, all the data involved in the
selection and analysis of KPIs are then integrated and stored in common repositories, hence enabling
sophisticated querying and reasoning for semantic validation. The proposed semantic model is evaluated on a
real-world case study on water management. A series of data analysis and reasoning tasks are conducted to
show how the ontological model is able to detect semantic conflicts in actual correlations of selected indicators. | es |
dc.description.sponsorship | Ministerio de Educación y Ciencia TIN2017-86049-R | es |
dc.description.sponsorship | Ministerio de Educación y Ciencia RTI2018-094283-B-C32 | es |
dc.format | application/pdf | es |
dc.format.extent | 12 | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | International Journal of Information Management, 58 (June 2021-102018) | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Ontology | es |
dc.subject | KPI Modelling | es |
dc.subject | Semantics | es |
dc.subject | Reasoning | es |
dc.subject | Knowledge extraction | es |
dc.subject | Water management | es |
dc.title | Ontology-driven approach for KPI meta-modelling, selection and reasoning | es |
dc.type | info:eu-repo/semantics/article | 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 Ciencias de la Computación e Inteligencia Artificial | es |
dc.relation.projectID | TIN2017-86049-R | es |
dc.relation.projectID | RTI2018-094283-B-C32 | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S026840121930581X | es |
dc.identifier.doi | 10.1016/j.ijinfomgt.2019.10.003 | es |
dc.journaltitle | International Journal of Information Management | es |
dc.publication.volumen | 58 | es |
dc.publication.issue | June 2021-102018 | es |
dc.contributor.funder | Ministerio de Educación y Ciencia (MEC). España | es |