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The history of artificial intelligence (AI) can be dated back to as early as the 1950s, even longer than the history of the database development. However, the AI technology has not been applied on a large scale for a long time due to various objective factors, and even experienced several obvious troughs. With the further development of information technologies in recent years, factors that restrict the AI development have been gradually weakened, and the AI, big data, and cloud computing (ABC) technologies are born. The combination of AI and databases has been a trending research topic in the industry in recent years. Our database team has participated in the exploration of this domain earlier and achieved phased achievements. The AI feature submodule dbmind is more independent than other database components. It can be divided into AI4DB and DB4AI.

  • AI4DB uses AI technologies to optimize database execution performance as well as achieve autonomy and O&M free. It includes self-tuning, self-diagnosis, self-security, self-O&M, and self-healing.
  • DB4AI streamlines the E2E process from databases to AI applications and unifies the AI technology stack to achieve out-of-the-box, high performance, and cost saving. For example, you can use SQL-like statements to use functions such as recommendation system, image retrieval, and time series forecasting to maximize the advantages of MogDB, such as high parallelism and column store.