DW2RDF4SDG – Modelado de ontologías desde cubos multidimensionales en función de los Objetivos de Desarrollo Sostenible
DOI:
https://doi.org/10.18046/syt.v16i44.2724Palabras clave:
Objetivos de Desarrollo Sostenible, modelo multidimensional, data warehouse, ontología, agua.Resumen
Los modelos multidimensionales y sus medidas en relación con diferentes dimensiones son poderosos instrumentos para la toma de decisiones. Las ontologías, en su expresión básica como RDF [Resource Description Framework] representan la realidad a partir de relaciones entre clases, y constituyen la base de los datos enlazados de la Web semántica. Este trabajo provee una metodología sencilla para, partiendo de un modelo multidimensional de Data Warehouse obtener una ontología RDF que sea fácilmente enlazable a otras ontologías de los Objetivos de Desarrollo Sostenible al 2030, más específicamente a la Ontología de Interface de los Objetivos de Desarrollo Sostenible [SDGIO] que está emergiendo por un esfuerzo global impulsado por el Programa de Naciones Unidas para el Medioambiente [United Nations Environment Programme, UNEP]. La metodología ofrecida, etiquetada como DW2RDF4SDG, se instrumenta para el Objetivo de Desarrollo Sostenible 6 [ODS6], el cual está dirigido a asegurar la disponibilidad y gestión sostenible del agua y saneamiento para todos.
Referencias
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Prat, N., Akoka, J., & Comyn-Wattiau, I. (2012). Transforming multidimensional models into OWL-DL ontologies. In: Research Challenges in Information Science (RCIS), 2012 Sixth International Conference on. doi:10.1109/RCIS.2012.6240451. IEEE.
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Wiegleb, V. (2017). Hydro-social arrangements and paradigmatic change in water governance: an analysis of the sustainable development goals (SDGs). Sustainability Science, doi:10.1007/s11625-017-0518-1.
Wiseli, D., Tanusetiawan, R., & Purnomo, F. (2017). Simulation game as a reference to smart city management. Procedia Computer Science, 116, 468-475. doi:10.1016/j.procs.2017.10.053.
Zimányi, E., & Abelló, A. (Eds.). (2016). Lecture Notes in Business Information Processing, 253: Business intelligence: 5th European Summer School, eBISS 2015, Barcelona, Spain, July 5-10, 2015. Cham, Switzerland: Springer.
Albarrak, K. M. & Sibley, E. H. (2011). A survey of methods that transform data models into Ontology models. In: IEEE International Conference on Information Reuse & Integration, Las Vegas, NV, 2011, (pp. 58-65). doi: 10.1109/IRI.2011.6009521.
Ashraf, J., Chang, E., Hussain, O.K., & Hussain, F.K. (2015). Ontology usage analysis in the ontology lifecycle: A state-of-the-art review. Knowledge-Based Systems, 80, 34-47. doi:10.1016/j.knosys.2015.02.026
Berson, A. & Smith, S. J. (1997). Data warehousing, data mining, and OLAP. New York, NY: McGraw-Hill.
Biermann, F., Kanie, N., & Kim, R.E. (2017). Global governance by goal-setting: the novel approach of the UN Sustainable Development Goals. Current Opinion in Environmental Sustainability, 26-27, 26-31. doi:10.1016/j.cosust.2017.01.010
Carpani, F. (2000). CMDM: un modelo conceptual para la especificación de bases multidimensionales [thesis]. Universidad de la República: Montevideo, Uruguay.
Cope, M. A., & Pincetl, S. (2014). Confronting standards and nomenclature in spatial data infrastructures: A case study of urban Los Angeles county geospatial water management data. IJSDIR, 9, 36-58.
Cumming, T.L, Shackleton, R., Förster, J., Dini, J., Khan, A., Gumula, M., & Kubiszewski, I. (2017). Achieving the national development agenda and the Sustainable Development Goals (SDGs) through investment in ecological infrastructure: A case study of South Africa. Ecosystem Services, 27(B), 253-260. doi:10.1016/j.ecoser.2017.05.005.
Giupponi, C. & Gain, A.K. (2017). Integrated spatial assessment of the water, energy and food dimensions of the Sustainable Development Goals. Regional Environmental Change, 17(7), 1881-1893. doi:10.1007/s10113-016-0998-z.
Golfarelli, M. & Rizzi, S. (2009). Data warehouse design: Modern principles and methodologies. New Delhi, India: McGraw-Hill.
Hoekstra, A.J, Chapagain, A.K., & van Oel, P.R. (2017). Advancing water footprint assessment research: Challenges in monitoring progress towards Sustainable Development Goal 6. Water, 9(4), 438. doi:10.3390/w9060438.
Inmon, W.H. (2005). Building the data warehouse. Indianapolis, IN: John Wiley & Sons.
Jensen,M. (2016). Sustainable Development Goals Interface Ontology: Semantics for sustainability [ppt]. Retrieved from: http://ncgia.buffalo.edu/OntologyConference/PPT/Jensen.pdf
Jha, M. K., & Chowdary, V. M. (2007). Challenges of using remote sensing and GIS in developing nations. Hydrogeology Journal, 15(1), 197-200.
Khan, S. M., Bain, R. S., Lunze, K., Unalan, T., Beshanski-Pedersen, B., Slaymaker, T., & ... Hancioglu, A. (2017). Optimizing household survey methods to monitor the Sustainable Development Goals targets 6.1 and 6.2 on drinking water, sanitation and hygiene: A mixed-methods field-test in Belize. Plos One, 12(12), 1-18. doi:10.1371/journal.pone.0189089.
Kimball, R. & Ross, M. (2002). The data warehouse toolkit: The complete guide to dimensional modelling. New York, NY: Wiley.
Kurze, C., Gluchowski, P., & Bohringer, M. (2010). Towards an ontology of multidimensional data structures for analytical purposes. In: 43rd Hawaii International Conference on System Sciences, Honolulu, HI, (pp. 1-10). doi: 10.1109/HICSS.2010.485.
Malinowski, E. & Zimnyi, E. (2008). Advanced data warehouse design: From conventional to spatial and temporal applications (data-centric systems and applications). Berlin-Heidelberg: Springer.
Moreira, J., Cordeiro, K., Campos, M. L., & Borges, M. (2014). Ontowarehousing - Multidimensional design supported by a foundational ontology: A temporal perspective, data warehousing and knowledge discovery. In:
Lecture Notes in Computer Science, 8646: Proceedings of the 16th International Conference, DaWaK 2014, (pp. 35-44). Berlin-Heidelberg: Springer.
Mugagga, F. & Nabaasa, B.B. (2016). The centrality of water resources to the realization of Sustainable Development Goals (SDG): A review of potentials and constraints on the African continent, International Soil and Water Conservation Research, 4(3), pp. 215-223. doi:10.1016/j.iswcr.2016.05.004.
Nebot,V., Berlanga, R., Pérez, J.M, Aramburu, M. J., & Pedersen, T. B. (2009). Multidimensional integrated ontologies: A framework for designing semantic data warehouses. Lecture Notes un Computer Science, 5530: Journal on Data Semantics, 13, 1-36. Berlin-Heidelberg: Springer. doi:10.1007/978-3-642-03098-7_1.
Pinilla G. & Barón, J. (2015). Inference model for dynamic classification of monographs at university level. Sistemas & Telemática, 13(35), 23-38. doi: 10.18046/syt.v13i35.2150
Prat, N., Akoka, J., & Comyn-Wattiau, I. (2012). Transforming multidimensional models into OWL-DL ontologies. In: Research Challenges in Information Science (RCIS), 2012 Sixth International Conference on. doi:10.1109/RCIS.2012.6240451. IEEE.
Spijkers, O. (2016). The Cross-fertilization between the Sustainable Development Goals and international water law. Review of European Comparative & International Environmental Law, 25(1), 39-49. doi:10.1111/reel.12152.
United Nation [UN]. (2015). Transforming our world: The 2030 Agenda of Sustainable Development [A/RES/70/1]. Retrieved from: https://sustainabledevelopment.un.org/post15/transformingourworld/publication
United Nations Environment Programme [UNEP]. (2016). Post 2015 Note #1: Integrating the three dimensions of sustainable development. Retrieved from: http://www.unep.org/unea1/docs/UNEP%20Post%202015%20Note%201%20final.pdf
United States Geologic Survey [USGS]. (2017). Maps and GIS data. Retrieved from: https://water.usgs.gov/maps.html
Wiegleb, V. (2017). Hydro-social arrangements and paradigmatic change in water governance: an analysis of the sustainable development goals (SDGs). Sustainability Science, doi:10.1007/s11625-017-0518-1.
Wiseli, D., Tanusetiawan, R., & Purnomo, F. (2017). Simulation game as a reference to smart city management. Procedia Computer Science, 116, 468-475. doi:10.1016/j.procs.2017.10.053.
Zimányi, E., & Abelló, A. (Eds.). (2016). Lecture Notes in Business Information Processing, 253: Business intelligence: 5th European Summer School, eBISS 2015, Barcelona, Spain, July 5-10, 2015. Cham, Switzerland: Springer.
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2018-02-23
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Investigación científica y tecnológica
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Esta publicación está licenciada bajo los términos de la licencia CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/deed.es)
