一句话：通过访谈11名DBMS专家，揭示DBMS管理自动化的实践挑战与采用机遇。
该研究关注数据库管理系统（DBMS）管理日益复杂、自动化工具虽涌现但业界采用有限的矛盾。作者在企业环境中开展定性研究，深度访谈11名DBMS专家，分析其感知、日常流程与经验，以识别DBMS管理中的困难及自动化挑战与前景。研究得出两方面贡献：一是从从业者视角揭示自动化工具在真实DBMS管理实践中的障碍与机会；二是提炼出可转化为有用启示的发现，以促进DBMS管理自动化的开发与采用。作者承认结论受特定公司情境限制，但认为可为后续研究提供有价值的知识基础。
Background. In recent years, the volume and complexity of data handled by Database Management Systems (DBMS) have surged, necessitating greater efforts and resources for efficient administration. In response, numerous automation tools for DBMS administration have emerged, particularly with the progression of AI and machine learning technologies. However, despite these advancements, the industry-wide adoption of such tools remains limited.Aims. This qualitative research aims to delve into the practices of DBMS users, identifying their difficulties around DBMS administration. By doing so, we intend to uncover key challenges and prospects for DBMS administration automation, thereby promoting its development and adoption.Method. This paper presents the findings of a qualitative study we conducted in an industrial setting to explore this particular issue. The study involved conducting in-depth interviews with 11 DBMS experts, and we analyzed the data to derive a set of implications.Results. We argue that our study offers two important contributions: firstly, it provides valuable insights into the challenges and opportunities of DBMS administration automation through interviewees’ perceptions, routines, and experiences. Secondly, it presents a set of findings that can be derived to useful implications and promote DBMS administration automation.Conclusions. This paper presents an empirical study conducted in an industrial context that examines the challenges and opportunities of DBMS administration automation within a particular company. Although the study’s findings may not apply to all companies, we believe the results provide a valuable body of knowledge with implications that can be useful for future research endeavors.CCS CONCEPTS• General and reference → Empirical studies; • Social and professional topics → Automation.