Debugging Scientific Workflows with Provenance: Achievements and Lessons Learned

@inproceedings{Oliveira2014DebuggingSW,
  title={Debugging Scientific Workflows with Provenance: Achievements and Lessons Learned},
  author={Daniel de Oliveira and Flavio Costa and V{\'i}tor Silva Sousa and Kary A. C. S. Oca{\~n}a and Marta Mattoso},
  booktitle={SBBD},
  year={2014}
}
1Scientific Workflow Management Systems manage experiments in large-scale and deliver provenance data. Provenance data represents the workflow execution behavior, allowing for tracing the data-flow generation. When provenance is extended with performance execution data, it becomes an important asset to identify and analyze errors that occurred during the workflow execution (i.e. debugging). Debugging is essential for workflows that execute in parallel in large-scale distributed environments… CONTINUE READING

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