{'fraction_non_alphanumeric': 0.02514792899408284, 'fraction_numerical': 0.0, 'mean_word_length': 4.504065040650406, '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}}
Web Mining in Search Engines

Given the rate of growth of the Web, scalability of search engines is a key issue, as the amount of hardware and network resources needed is large, and expensive. In addition, search engines are popular tools, so they have heavy constraints on query answer time. So, the efficient use of resources can improve both scalability and answer time. One tool to achieve these goals is Web mining. Web mining has three branches: link mining, usage mining, and content mining. One important analysis in all these cases is the dynamic behavior. Here we give examples of link and usage mining related to search engines, as well as the related Web dynamics.