{'fraction_non_alphanumeric': 0.01445466491458607, 'fraction_numerical': 0.0, 'mean_word_length': 5.626086956521739, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
From scripts towards provenance inference

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, scientists can request fine-grained provenance information facilitating the inferred workflow provenance. We also provide a guideline to customize the workflow provenance graph based on user preferences. Our evaluation shows that the proposed approach is relevant and suitable for scientists to manage provenance.