Volume 38 Number 7
  • ISSN: 0263-5046
  • E-ISSN: 1365-2397
Preview this article:

There is no abstract available.


Article metrics loading...

Loading full text...

Full text loading...


  1. Azevedo, L., Souza, R., Thiago, R., Soares, E., Moreno, M.
    [2020]. Experiencing ProvLake to manage the data lineage of AI workflows.Meeting in Innovation in Information Systems (EISI), Abstracts.
    [Google Scholar]
  2. Chevitarese, D.S., Szwarcman, D., Brazil, E.V., Zadrozny, B.
    [2018]. Efficient classification of seismic textures.2018 International Joint Conference on Neural Networks (IJCNN), 1–8.
    [Google Scholar]
  3. Davidson, S.B., Freire, J.
    [2008]. Provenance and scientific workflows: Challenges and opportunities.SIGMOD, 1345–1350.
    [Google Scholar]
  4. Fielding, R.T. and Taylor, R.N.
    [2000]. Architectural styles and the design of network-based software architectures. 7, University of California, Irvine.
    [Google Scholar]
  5. Gil, Y., Pierce, S.A., Babaie, H., Banerjee, A., Borne, K., Bust, G., Cheatham, M., Ebert-Upho, I., Gomes, C., Hill, M., Horel, J., Hsu, L., Kinter, J., Knoblock, C., Krum, D., Kumar, V., Lermusiaux, P., Liu, Y. , North, C., Pankratius, V. , Peters, S., Plale, B., Pope, A., Ravela, S., Re-strepo, J., Ridley, A., Samet, H., Shekhar, S.
    [2018]. Intelligent systems for geosciences: an essential research agenda. Communications of the ACM.
    [Google Scholar]
  6. Liu, J., Pacitti, E., Valduriez, P., Mattoso, M.
    [2015]. A survey of data-intensive scientific workflow management.Journal of Grid Computing, 13(4), 457–493.
    [Google Scholar]
  7. Mattoso, M., Werner, C., Travassos, G., Braganholo, V., Ogasawara, E., de Oliveira, D., Cruz, S., Martinho, W., Murta, L.
    [2010]. Towards supporting the life cycle of large-scale scientific experiments.International Journal of Business Process Integration and Management, 5, 79–92.
    [Google Scholar]
  8. Moreno, M.F., Brandão, R., Cerqueira, R.
    [2017]. Extending hypermedia conceptual models to support hyperknowledge specifications.International Journal of Semantic Computing, 11(01), 43–64.
    [Google Scholar]
  9. Moreno, M.F., Brandão, R., Ferreira, J.J., Fucs, A., Cerqueira, R.
    [2016]. Towards a conceptual model for cognitive-intensive practices.IEEE International Symposium on Multimedia (ISM), 148–151.
    [Google Scholar]
  10. Moreno, M.F., Lourenco, V., Fiorini, S., Costa, P., Brandão, R., Civitarese, D., Cerqueira, R.
    [2020]. Managing machine learning workflow components.14th International Conference on Semantic Computing (ICSC), 25–30.
    [Google Scholar]
  11. Moreno, M.F., Santos, R., Santos, W., R. Cerqueira, R.
    [2018]. Kes: The knowledge explorer system.International Semantic Web Conference (P&D/Industry/BlueSky), Abstracts.
    [Google Scholar]
  12. Randen, T., Monsen, E., Signer, C., Abrahamsen, A., Hansen, J.O., S ter, T., Schlaf, J.
    [2000]. Three-dimensional texture attributes for seismic data analysis.SEG Technical Program, Expanded Abstracts, 668–671.
    [Google Scholar]
  13. Souza, R., Azevedo, L., Lourenco, V., Soares, E., Thiago, R., Brandão, R., Civitarese, D., Brazil, E., Moreno, M., Valduriez, P., Mattoso, M., Cerqueira, R., Netto, M.A.S.
    [2019a]. Provenance data in the machine learning lifecycle in computational science and engineering.IEEE/ACM Workflows in Support of Large-Scale Science (WORKS), 1–10.
    [Google Scholar]
  14. Souza, R., Azevedo, L., Thiago, R., Soares, E., Nery, M., Netto, M., Brazil, E.V., Cerqueira, R., Valduriez, R, Mattoso, M.
    [2019b], Efficient runtime capture of multiworkflow data using provenance.IEEE International Conference on e-Science (eScience), 1–10.
    [Google Scholar]
  15. Souza, R., Brazil, E.V., Azevedo, L., Ferreira, R., Chevitarese, D., Soares, E., Thiago, R., Nery, M., Torres, V, Cerqueira, R.
    [2019c]. Managing data trace-ability in the data lifecycle for deep learning applied to seismic data.American Association of Petroleum Geologists Annual Convention and Exhibition, Abstracts.
    [Google Scholar]
  16. Thiago, R., Souza, R., Azevedo, L., Soares, E., Santos, R., Santos, W., De Bayser, M., Cardoso, M., Moreno, M., Cerqueira, R.
    [2020]. Managing data lineage of O&G machine learning models: The sweet spot for shale use case.EAGE Digitalization Conference and Exhibition, Extended Abstracts.
    [Google Scholar]

Data & Media loading...

  • Article Type: Research Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error