{'fraction_non_alphanumeric': 0.01823486506199854, 'fraction_numerical': 0.0, 'mean_word_length': 5.097777777777778, '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}}
Improvement of database administration by procedure contextualization

Database Administrators (DBAs) relieve on a large set of procedures for incident solving in database. However, in the one hand, they have to work under temporal and financial pressures, and, in the other hand, DBAs are continually readjusting these procedures to manage a multitude of specific situations that differ from the generic situation by some few contextual elements. The exceptions are rather the norms. Thus DBAs developed practices that deal with these contextual elements in order to solve the problem at hand. Capturing and managing practices is far more difficult than procedures. If a procedure-based support system is easy to implement (procedures are well established), a practice-based support system is difficult to design because there are almost as many practices as contextual variants. However, if the context has an infinite dimension, the number of practices is finite. The key elements are the incremental acquisition of knowledge and the learning of new practices, thanks to a software called Contextual Graphs. The goal of our work is to support DBAs by collecting all the practices developed by the DBAs and proposing them to DBAs to benefit of the experience of the other DBAs and to provide a support system acting as a real context-based intelligent assistant system.