On the Provenance Extraction Techniques from Large Scale Log Files: A Case Study for the Numerical Weather Prediction Models-Reference-Cited by-同舟云学术

On the Provenance Extraction Techniques from Large Scale Log Files: A Case Study for the Numerical Weather Prediction Models

Published:2021 Issue: Volume: Page:249-260
ISSN:0302-9743
Container-title:Euro-Par 2020: Parallel Processing Workshops
language:
Short-container-title:

Author:

Tufek Alper^ORCID,Aktas Mehmet S.^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-71593-9_20

Reference23 articles.

1. Simmhan, Y.L., Plale, B., Gannon, D.: A survey of data provenance in e-science. ACM SIGMOD Rec. 34(3), 31–36 (2005). https://doi.org/10.1145/1084805.1084812

2. Missier, P., Belhajjame, K., Cheney, J.: The W3C PROV family of specifications for modelling provenance metadata. In: Proceedings of the 16th International Conference on Extending Database Technology, pp. 773–776 (2013). https://doi.org/10.1145/2452376.2452478

3. Tufek, A., Gurbuz, A., Ekuklu, O.F., Aktas, M. S.: Provenance collection platform for the Weather Research and Forecasting Model. In: 2018 14th International Conference on Semantics, Knowledge and Grids (SKG), pp. 17–24 (2018). https://doi.org/10.1109/skg.2018.00009

4. Simmhan, Y.L., Plale, B., Gannon, D.: A framework for collecting provenance in data-centric scientific workflows. In: 2006 IEEE International Conference on Web Services (ICWS06), pp. 427–436 (2006). https://doi.org/10.1109/icws.2006.5

5. Indiana University, Pervasive Technology Institute. (n.d.). Karma. Pervasive Technology Institute website: https://pti.iu.edu/impact/open-source/karma.html. 12 Apr 2020

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Efficient Text Classification with Deep Learning on Imbalanced Data Improved with Better Distribution;Turkish Journal of Science and Technology;2022-02-22

2. Text classification using improved bidirectional transformer;Concurrency and Computation: Practice and Experience;2021-07-18

3. Price Forecasting with Deep Learning in Business to Consumer Markets;Computational Science and Its Applications – ICCSA 2021;2021