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Data provenance allows scientists to validate their model as well as to investigate the origin of an unexpected value. Furthermore, it can be used as a replication recipe for output data products. However, capturing provenance requires enormous effort by scientists in terms of time and training. First, they need to design the workflow of the scientific model, i.e., workflow provenance, which requires...
Scientists require provenance information either to validate their model or to investigate the origin of an unexpected value. However, they do not maintain any provenance information and even designing the processing workflow is rare in practice. Therefore, in this paper, we propose a solution that can build the workflow provenance graph by interpreting the scripts used for actual processing. Further,...
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