Building a Linked Data Mashup for Public Health Data Integration

  • Gabriel Lopes Federal Institute of Ceará
  • Vânia Vidal Federal University of Ceará
  • Mauro Oliveira Federal Institute of Ceará

Abstract


Linked Data promotes the publication of structured data, easing the development of an homogeneized-view over heterogeneous sources, called Linked Data Mashup view (LDM view). This article describes the processes of specification and materialization of a LDM view of two heterogeneous bases from Brazilian Public Health System (SUS): Information System on Live Births (SINASC) and SUS electronic (e-SUS). From this process, it was possible to obtain an integrated-view of the bases previously isolated. This integrated-view will be useful for analyzing the correlation between mother’s information during pregnancy with deaths and anomalies in newborns.
Keywords: Linked Data, View, Linked Data Mashup, Public Health System

References

Bizer, C., Heath, T., and Berners-Lee, T. (2009a). Linked data - the story so far. Int. J. Semantic Web Inf. Syst., 5(3):1–22.

Bizer, C., Volz, J., Kobilarov, G., and Gaedke, M. (2009b). Silk - a link discovery framework for the web of data. In 18th International World Wide Web Conference.

Heath, T. and Bizer, C. (2011). Linked Data: Evolving the Web into a Global Data Space. Morgan & Claypool, 1st edition.

Hoang, H. H., Cung, T. N., Truong, D. K., Hwang, D., and Jung, J. J. (2014). Semantic information integration with linked data mashups approaches. IJDSN, 2014.

Jarrar, M. and Dikaiakos, M. D. (2008). Mashql: A query-by-diagram topping sparql. In Proceedings of the 2Nd International Workshop on Ontologies and Information Systems for the Semantic Web, ONISW ’08, pages 89–96, New York, NY, USA. ACM.

Mendes, P. N., Mühleisen, H., and Bizer, C. (2012). Sieve: Linked Data Quality Assessment and Fusion. In 2nd International Workshop on Linked Web Data Management (LWDM 2012) at the 15th International Conference on Extending Database Technology, EDBT 2012, page to appear.

Pipino, L. L., Lee, Y. W., and Wang, R. Y. (2002). Data quality assessment. Commun. ACM, 45(4):211–218.

RDF (2014). Resource description framework.

Schultz, A., Matteini, A., Isele, R., Bizer, C., and Becker, C. (2011). Ldif : Linked data integration framework.

Vidal, V. M. P., Casanova, M. A., Arruda, N., Roberval, M., Leme, L. P., Lopes, G. R., and Renso, C. (2015). Advanced Information Systems Engineering: 27th International Conference, CAiSE 2015, Stockholm, Sweden, June 8-12, 2015, Proceedings, chapter Specification and Incremental Maintenance of Linked Data Mashup Views, pages 214–229. Springer International Publishing, Cham.

W3C (2016). R2RML RDB to RDF Mapping Language. available at https://www.w3.org/TR/r2rml/.

Ziegler, P. and Dittrich, K. R. (2007). Data integration – problems, approaches, and perspectives.
Published
2016-10-04
LOPES, Gabriel; VIDAL, Vânia; OLIVEIRA, Mauro. Building a Linked Data Mashup for Public Health Data Integration. In: BRAZILIAN SYMPOSIUM ON DATABASES (SBBD), 31. , 2016, Salvador/BA. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2016 . p. 145-150. ISSN 2763-8979. DOI: https://doi.org/10.5753/sbbd.2016.24319.