INTEGRACuBe: Exploration of analytical data in RDF
Abstract
The growth of web-available datasets that use the RDF standard enables data analysis that involves multiple dimensions. According to the W3C, one of the resources for analyzing multidimensional data is the use of the RDF Data Cube vocabulary. However, there is still a lack of support tools for applying this vocabulary in datasets. In this sense, this article proposes INTEGRACuBe, an environment that uses a meta-scheme and semi-automated mechanisms to support the mapping of data resources to the RDF Data Cube metamodel. As a result, an exploration of analytical data in RDF will be possible. Additionally, a case study is presented in the Software Development Management scenario.
Keywords:
RDF Data Cube, data resource mapping, metamodel, multidimensional analysis
References
Avelino, J. O., Cordeiro, K. F., and Cavalcanti, M. C. (2020). An RDF based approach for integrating data at different levels of abstraction. In Proceedings of the Brazilian Symposium on Multimedia and the Web, WebMedia ’20, page 81–88.
Cordeiro, Kelli, F., Pereira, et al. (2011). An approach for managing and semantically enriching the publication of linked open governmental data. In SBBD, pages 82–95.
Cyganiak, R., Reynolds, et al. (2014). The RDF data cube vocabulary. World Wide Web Consortium (W3C), 16th Jan, 2:014.
Escobar, P. et al. (2020). Adding value to linked open data using a multidimensional model approach based on the RDF data cube vocabulary. Computer Standards Interfaces.
Etcheverry, L. and Vaisman, A. A. (2017). Efficient analytical queries on semantic web data cubes. Journal on Data Semantics, 6(4):199–219.
Figueiredo, G., Cordeiro, K. F., and Campos, M. L. M. (2020). LigADOS: Interlinking datasets in open data portal platforms on the semantic web. Metadata and Semantic Research, 1355:73-84.
Kimball, R. and Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. Wiley, Indianapolis, IN, 3 edition.
Mountantonakis et al. (2019). Large-scale semantic integration of linked data: A survey. ACM Comput. Surv., 52(5).
Silveira, R. and Cavalcanti, M. (2020). Método para rotular ligações semânticas na web de dados. In Anais do XXXV Simpósio Brasileiro de Bancos de Dados, pages 49–60.
Tadesse, S. et al. (2019). ARDI: Automatic generation of RDFS models from heterogeneous data sources. In 2019 IEEE 23rd International Enterprise Distributed Object Computing Conference (EDOC), pages 190–196.
Cordeiro, Kelli, F., Pereira, et al. (2011). An approach for managing and semantically enriching the publication of linked open governmental data. In SBBD, pages 82–95.
Cyganiak, R., Reynolds, et al. (2014). The RDF data cube vocabulary. World Wide Web Consortium (W3C), 16th Jan, 2:014.
Escobar, P. et al. (2020). Adding value to linked open data using a multidimensional model approach based on the RDF data cube vocabulary. Computer Standards Interfaces.
Etcheverry, L. and Vaisman, A. A. (2017). Efficient analytical queries on semantic web data cubes. Journal on Data Semantics, 6(4):199–219.
Figueiredo, G., Cordeiro, K. F., and Campos, M. L. M. (2020). LigADOS: Interlinking datasets in open data portal platforms on the semantic web. Metadata and Semantic Research, 1355:73-84.
Kimball, R. and Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. Wiley, Indianapolis, IN, 3 edition.
Mountantonakis et al. (2019). Large-scale semantic integration of linked data: A survey. ACM Comput. Surv., 52(5).
Silveira, R. and Cavalcanti, M. (2020). Método para rotular ligações semânticas na web de dados. In Anais do XXXV Simpósio Brasileiro de Bancos de Dados, pages 49–60.
Tadesse, S. et al. (2019). ARDI: Automatic generation of RDFS models from heterogeneous data sources. In 2019 IEEE 23rd International Enterprise Distributed Object Computing Conference (EDOC), pages 190–196.
Published
2021-10-04
How to Cite
AVELINO, Jones O.; CORDEIRO, Kelli F.; C. CAVALCANTI, Maria.
INTEGRACuBe: Exploration of analytical data in RDF. In: BRAZILIAN SYMPOSIUM ON DATABASES (SBBD), 36. , 2021, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 331-336.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2021.17894.
