{'fraction_non_alphanumeric': 0.030035335689045935, 'fraction_numerical': 0.0017667844522968198, 'mean_word_length': 5.517241379310345, '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}}
Spatio-Temporal Data Mining: From Big Data to Patterns

Technological advances in terms of data acquisition enable to better monitor dynamic phenomena in various domains (areas, fields) including environment. The collected data is more and more complex - spatial, temporal, heterogeneous and multi-scale. Exploiting this data requires new data analysis and knowledge discovery methods. In that context, approaches aimed at discovering spatio-Temporal patterns are particularly relevant. This paper1 focuses on spatio-Temporal data and associated data mining methods.