Обзор методов интеграции интеллектуального анализа данных в СУБД

Михаил Леонидович Цымблер

Аннотация


Интеллектуальный анализ данных направлен на извлечение доступных для понимания знаний, необходимых для принятия решений в различных сферах человеческой деятельности. Феномен Больших данных является характерным признаком современного информационного общества. Процессы очистки и структурирования Больших данных приводят к образованию сверхбольших баз и хранилищ данных. Несмотря на появление большого количества NoSQL СУБД, основным инструментом управления базами данных по-прежнему остаются реляционные СУБД. Одним из перспективных направлений развития реляционных СУБД является внедрение в них средств интеллектуального анализа данных. Интеграция позволяет как избежать накладных расходов по экспорту анализируемых данных из хранилища и импорту результатов анализа обратно в хранилище, так и использовать при анализе данных системные сервисы, заложенные в архитектуре СУБД. В статье представлен обзор методов и подходов к решению задачи интеграции интеллектуального анализа данных в СУБД. Приводится классификация подходов к решению задачи интеграции интеллектуального анализа данных в СУБД. Представлены расширения языка баз данных SQL, обеспечивающие синтаксическую поддержку интеллектуального анализа данных в СУБД. Рассмотрены примеры реализации алгоритмов интеллектуального анализа данных на SQL и систем анализа данных в реляционных СУБД.

Ключевые слова


интеллектуальный анализ данных; реляционная СУБД; классификация; кластеризация; поиск шаблонов

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Литература


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DOI: http://dx.doi.org/10.14529/cmse190203