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Automated Debugging in Data-Intensive Scalable Computing
Developing Big Data Analytics workloads often involves trial and error debugging, due to the unclean nature of datasets or wrong assumptions made about data. When errors (e.g., program crash, outlier results, etc.) arise, developers are often interested in identifying a subset of the input data that...
Guardat en:
| Publicat a: | Proc ACM Symp Cloud Comput |
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| Autors principals: | , , , , , |
| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
2017
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| Matèries: | |
| Accés en línia: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6473804/ https://ncbi.nlm.nih.gov/pubmed/31008457 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1145/3127479.3131624 |
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