{'fraction_non_alphanumeric': 0.02050473186119874, 'fraction_numerical': 0.0, 'mean_word_length': 4.990566037735849, '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}}
Subgraph Matching on Multiple Graph Streams

Nowadays, graph data can be collected via more than one resource and in parallel. This involves not only changing the information but also immediately updating the outcomes of the data mining task. In this case, it is undesirable to recompute all the tasks once the changes occur to the original data. Therefore, we propose a streaming graph table (SGTable) to capture inserted/removed edges and an incremental graph matching algorithm to retrieve new results on only new data. Besides, we do the multi-streaming environment experiments using Spark that gather graphs from various sources.