结构化数据库查询语言智能合成技术研究进展

Advances in SQL Intelligent Synthesis Technology

近年来,随着大数据、云计算等技术的飞速发展,大规模数据的产生使得各类应用对于数据库技术的依赖日益加深。然而,传统的数据库一般采用形式化的数据库查询语言SQL进行操作,对无编程经验或数据库使用经验的用户来说,复杂SQL语法难度较高,降低了各个领域数据库应用者的便捷程度。近年来,机器学习、深度神经网络等人工智能技术的飞速发展,尤其是ChatGPT横空出世引发的大语言模型技术热潮,驱动了数据库与人工智能的深度结合与技术变革。通过智能方法将用户输入语言自动化合成SQL语言,以满足不同程度数据库使用者的操作需求,提升数据库的智能性、环境适应性及用户友好性。为全面聚焦数据库查询语言智能合成技术的最新研究进展,从范例输入、文本输入及语音输入这3类用户输入切入,详细阐述各类智能合成模型的研究脉络、代表性工作及最新进展,同时对各类方法的技术框架进行归纳与对比,最后对全文进行全面性的总结,并针对现有方法存在的问题和挑战展望未来发展方向。

In recent years,with the rapid development of technologies such as big data and cloud computing,large-scale data ge-neration has deepened the dependence of various applications on database technology.However,traditional databases typically operate through the formalized database query language SQL,which poses a significant difficulty for users without programming or database usage experience,reducing the accessibility of databases across various fields.With the rapid advancement of artificial intelligence technologies like machine learning and deep neural networks,especially the surge of large language model technology sparked by the emergence of ChatGPT,there has been a profound synthesis and technological transformation of databases and intelligent technology.Intelligent methods are employed to automatically translate user input language into SQL,meeting the operational needs of database users of varying levels of expertise and enhancing databases’intelligence,environmental adaptability,and user-friendliness.To comprehensively focus on the latest research developments in intelligent SQL generation technology,this paper delves into three types of user inputs-example-based,text-based,and voice-based-and provides a detailed exposition of the research trajectory,representative works,and the latest advancements of various intelligent synthesis models.Additionally,this paper categorizes and compares the technical frameworks of these methods and provides an overall summary.Finally,it paper looks forward to future development directions in light of existing problems and challenges with current methods.