{'fraction_non_alphanumeric': 0.03277153558052434, 'fraction_numerical': 0.006554307116104869, 'mean_word_length': 5.073863636363637, '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}}
A spatio-temporal access method based on snapshots and events

This paper describes a new spatio-temporal access method (SEST-Index) that combines two approaches for modeling spatio-temporal information: snapshots and events. This method makes it possible to not only process time slice and interval queries, but also queries about events. The SEST Index implementation uses an R-tree structure for storing snapshots and a log data structure for storing events that occur between consecutive snapshots. Experimental results that compare SEST-Index and HR-tree show that, for a change frequency between 1% and 13%, SEST-Index requires less storage space than HR-tree, and for a change frequency between 1% and 7%, SEST-Index outperforms HR-tree for interval queries. In addition, as SEST-Index is an event-oriented structure, event queries are efficiently answered. In order to decrease the storage space for frequencies of change above 20%, this work explores alternatives that optimize the space of the log structure without affecting the efficiency of query answers.