OntoExpLine: Towards an Ontology for Algebraic Experiment Lines

Abstract


Scientific workflows are used as an abstraction to implement complex simulations of Computational Science and Engineering (CSE). However, managing CSE experiments is not a simple task because the same experiment can involve the execution and correlated analysis of several workflows. The algebraic experiment lines (LinExps) involve techniques to model CSE experiments at different levels of abstraction and, despite representing a step forward, they still lack support about domain data and automatic checking that assist workflow derivation process. This paper proposes the OntoExpLine, a LinExps ontology that aims at providing semantics and flexibility to the scientific experimentation process.

Keywords: e-science, scientific workflows, ontologies, provenance, experiment lines

References

Carvalho, L. A. M. C., Garijo, D., Medeiros, C. B., and Gil, Y. (2018). Semantic software metadata for workflow exploration and evolution. In IEEE eScience, pages 431–441. IEEE Computer Society.

de Almeida Falbo, R. (2014). Sabio: Systematic approach for building ontologies. In Joint Workshop ONTO.COM / ODISE on Ontologies in Conceptual Modeling and Information Systems Engineering, CEUR Workshop Proceedings. CEUR-WS.org.

de Oliveira, D., Ogasawara, E. S., Dias, J., Baião, F. A., and Mattoso, M. (2012). Ontologybased semi-automatic workflow composition. JIDM, 3(1):61–72.

Dias, L. G., Lopes, B., and de Oliveira, D. (2019). Aplicação de ontologias de proveniência em workflows científicos: um mapeamento sistemático. In XIII BreSci. SBC.

Freire, J., Koop, D., Santos, E., and Silva, C. T. (2008). Provenance for computational tasks: A survey. Comput. Sci. Eng., 10(3):11–21.

Gil, Y., Ratnakar, V., Kim, J., González-Calero, P. A., Groth, P. T., Moody, J., and Deelman, E. (2011). Wings: Intelligent workflow-based design of computational experiments. IEEE Intell. Syst., 26(1):62–72.

Gruber, T. R. et al. (1993). A translation approach to portable ontology specifications. Knowledge acquisition, 5(2):199–221.

Guarino, N. (1997). Understanding, building and using ontologies. International Journal of Human-Computer Studies, 46(2-3):293–310.

Ison, J., Kalaš, M., Jonassen, I., Bolser, D., Uludag, M., McWilliam, H., Malone, J., Lopez, R., Pettifer, S., and Rice, P. (2013). Edam: an ontology of bioinformatics operations, types of data and identifiers, topics and formats. Bioinformatics, 29(10):1325–1332.

Marinho, A., de Oliveira, D., Ogasawara, E. S., Sousa, V. S., Ocaña, K. A. C. S., Murta, L., Braganholo, V., and Mattoso, M. (2017). Deriving scientific workflows from algebraic experiment lines: A practical approach. FGCS, 68:111–127.

Mcguinness, D. L., Fikes, R., Hendler, J., and Stein, L. A. (2002). Daml+oil: an ontology language for the semantic web. IEEE Intelligent Systems, 17(5):72–80.

Ocaña, K. A., de Oliveira, D., Ogasawara, E., Dávila, A. M., Lima, A. A., and Mattoso, M. (2011). Sciphy: a cloud-based workflow for phylogenetic analysis of drug targets in protozoan genomes. In BSB, pages 66–70. Springer.

Ogasawara, E. S., de Oliveira, D., Valduriez, P., Dias, J., Porto, F., and Mattoso, M. (2011). An algebraic approach for data-centric scientific workflows. PVLDB, 4(12):1328–1339.

Sameh, A., Cybenko, G., Kalos, M., Neves, K., Rice, J., Sorensen, D., and Sullivan, F. (1996). Computational science and engineering. ACM Comput. Surv., 28(4):810–817.

Weibel, S. L. and Koch, T. (2000). The dublin core metadata initiative. D-lib magazine, 6(12):1082–9873.
Published
2020-06-30
DIAS, Luiz Gustavo; LOPES, Bruno; DE OLIVEIRA, Daniel. OntoExpLine: Towards an Ontology for Algebraic Experiment Lines. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 14. , 2020, Cuiabá. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 33-40. ISSN 2763-8774. DOI: https://doi.org/10.5753/bresci.2020.11179.