CORER： collaborative multi-knowledge large language model prompt framework for IT application innovation database migration

doi:10.11772/j.issn.1001-9081.2025060745

Journal of Computer Applications ›› 2026, Vol. 46 ›› Issue (6): 1811-1817.DOI: 10.11772/j.issn.1001-9081.2025060745

• Data science and technology • Previous Articles

CORER： collaborative multi-knowledge large language model prompt framework for IT application innovation database migration

Yusheng YI, Zhaohao HUANG, Zihao DENG, Leilei KONG(), Haoliang QI

School of Computer Science and Artificial Intelligence，Foshan University，Foshan Guangdong 528000，China

Received:2025-07-08 Revised:2025-09-18 Accepted:2025-09-22 Online:2025-10-16 Published:2026-06-10
Contact: Leilei KONG
About author:YI Yusheng， born in 1989， Ph. D.， lecturer. His research interests include natural language processing， IT application innovation.
HUANG Zhaohao， born in 1999， M. S. His research interests include natural language processing， IT application innovation.
DENG Zihao， born in 2001， M. S. candidate. His research interests include natural language processing， IT application innovation.
QI Haoliang， born in 1972， Ph. D.， professor. His research interests include natural language processing， machine learning.
First author contact:KONG Leilei， born in 1979， Ph. D.， professor. Her research interests include natural language processing， machine learning.
Supported by:
General Program of National Natural Science Foundation of China(62276064)

面向信创数据库迁移的多知识库协同大语言模型提示框架CORER

易宇声, 黄兆豪, 邓梓昊, 孔蕾蕾(), 齐浩亮

佛山大学计算机与人工智能学院，广东佛山 528000

通讯作者: 孔蕾蕾
作者简介:易宇声（1989—），男，湖南益阳人，讲师，博士，CCF会员，主要研究方向：自然语言处理、信息技术应用创新
黄兆豪（1999—），男，广东茂名人，硕士，主要研究方向：自然语言处理、信息技术应用创新
邓梓昊（2001—），男，广东江门人，硕士研究生，主要研究方向：自然语言处理、信息技术应用创新
齐浩亮（1972—），男，黑龙江哈尔滨人，教授，博士，CCF会员，主要研究方向：自然语言处理、机器学习。
第一联系人：孔蕾蕾（1979—），女，黑龙江哈尔滨人，教授，博士，CCF会员，主要研究方向：自然语言处理、机器学习
基金资助:
国家自然科学基金面上项目(62276064)

Abstract

Abstract:

The main task of Information Technology (IT) application innovation database migration is to migrate the data structure and data from non-domestic databases to domestic databases smoothly. In view of the challenges of syntax differences and complex business logic adaptation between heterogeneous databases in the current IT application innovation database migration， a collaborative multi-knowledge Large Language Model （LLM） prompt framework for IT application innovation-oriented databases migration， CORER （Context-Objective-Rules-Examples-Response）， was proposed， the openGauss Structured Query Language （SQL） syntax rule knowledge base covering 199 SQL syntax rule types and containing 4 162 syntax rules was constructed， and the migration sample knowledge base covering 20.6% of the syntax rule types was constructed by integrating official templates and real cases. Then， the syntax rule knowledge and migration sample knowledge were injected into the LLM context based on the prompt elements， thereby matching the syntax， logic and architecture characteristics of heterogeneous databases adaptively， and guiding the LLM to complete the SQL statement refactoring accurately. Experimental results show that the accuracy of CORER in the MySQL to openGauss migration task is 93.44%， which is 1.31 percentage points higher than that of the rule-based method， and is increased by 7.02% in advanced feature scenarios such as storage procedures and triggers， verifying the comprehensive advantages of CORER in IT innovation-oriented database migration scenarios.

Key words: Information Technology (IT) application innovation, database migration, Structured Query Language (SQL) refactoring, prompt framework, Large Language Model (LLM)

摘要：

信息技术（IT）应用创新（简称“信创”）数据库迁移的主要任务是将数据结构与数据从非国产数据库平稳迁移至国产数据库。针对当前信创数据库迁移中存在的异构数据库间语法差异和业务逻辑适配复杂等挑战，提出一种面向信创数据库迁移的多知识库协同的大语言模型（LLM）提示框架CORER（Context-Objective-Rules-Examples-Response），构建覆盖199种结构化查询语言（SQL）语法规则类型且包含4 162条语法规则的openGauss SQL语法规则知识库，并融合官方模板与真实案例构建覆盖20.6%语法规则类型的迁移样例知识库。基于提示要素，在LLM上下文中注入语法规则知识与迁移样例知识，以自适应地完成对异构数据库语法、逻辑和架构特性的匹配，引导LLM精准地完成SQL语句重构。实验结果表明，CORER在MySQL到openGauss的迁移任务中的准确率达93.44%，相较于基于规则的方法提升了1.31个百分点，且在存储过程和触发器等高级特性场景中提升了7.02%，验证了CORER在信创数据库迁移场景下的综合优势。

关键词: 信息技术（IT）应用创新, 数据库迁移, 结构化查询语言重构, 提示框架, 大语言模型

CLC Number:

TP182

Yusheng YI, Zhaohao HUANG, Zihao DENG, Leilei KONG, Haoliang QI. CORER： collaborative multi-knowledge large language model prompt framework for IT application innovation database migration[J]. Journal of Computer Applications, 2026, 46(6): 1811-1817.

易宇声, 黄兆豪, 邓梓昊, 孔蕾蕾, 齐浩亮. 面向信创数据库迁移的多知识库协同大语言模型提示框架CORER[J]. 《计算机应用》唯一官方网站, 2026, 46(6): 1811-1817.

Figures/Tables 8

References 27

[1]	民政部信息中心. 信创产业发展的重要意义［EB/OL］. ［2025-03-11］..
	Information Center of Ministry of Civil Affairs. The importance of the development of the information technology application innovation industry［EB/OL］. ［2025-03-11］..
[2]	苏莹，杨文波，张景云. 国产数据库替换中的常见挑战与应用策略［J］. 信息系统工程， 2025（1）： 99-102.
	SU Y， YANG W B， ZHANG J Y. Common challenges and application strategies in domestic database replacement［J］. China CIO News， 2025（1）： 99-102.
[3]	THALHEIM B， WANG Q. Data migration： a theoretical perspective［J］. Data and Knowledge Engineering， 2013， 87： 260-278.
[4]	LASZEWSKI T， NAUDURI P. Identifying the level of effort and cost［M］// Migrating to the cloud： oracle client/server modernization. Rockland， MA： Syngress， 2011： 21-43.
[5]	SALUNKE S V， OUDA A. A performance benchmark for the PostgreSQL and MySQL databases［J］. Future Internet， 2024， 16（10）： No.382.
[6]	阿里云. 数据传输服务DTS［EB/OL］. ［2025-05-19］..
	Aliyun. Data transmission service DTS［EB/OL］. ［2025-05-19］..
[7]	HAGOS D H， BATTLE R， RAWAT D B. Recent advances in generative AI and large language models： current status， challenges， and perspectives［J］. IEEE Transactions on Artificial Intelligence， 2024， 5（12）： 5873-5893.
[8]	CHEN M， TWOREK J， JUN H， et al. Evaluating large language models trained on code［EB/OL］. ［2025-05-19］..
[9]	ZHU X， LI Q， CUI L， et al. Large language model enhanced text-to-SQL generation： a survey［EB/OL］. ［2025-03-12］..
[10]	ZHOU X， SUN Z， LI G. DB-GPT： large language model meets database［J］. Data Science and Engineering， 2024， 9（1）： 102-111.
[11]	GOYAL M K， CHATURVEDI R， SUNDARAMOORTHY P， et al. Leveraging generative AI for database migration： a comprehensive approach for heterogeneous migrations［EB/OL］. ［2025-05-22］..
[12]	NGOM A L， KRASKA T. Mallet： SQL dialect translation with LLM rule generation［C］// Proceedings of the 7th International Workshop on Exploiting Artificial Intelligence Techniques for Data Management. New York： ACM， 2024： No.3.
[13]	MySQL. MySQL homepage［EB/OL］. ［2025-05-19］..
[14]	李国良，王江，陈国. openGauss：企业级开源数据库系统［J］. 计算机科学技术学报， 2024， 39（5）： 1007-1028.
	LI G L， WANG J， CHEN G. openGauss： enterprise-level open source database system［J］. Journal of Computer Science and Technology， 2024， 39（5）： 1007-1028.
[15]	openGauss. SQL语法［EB/OL］. ［2025-05-19］.
	语法.html. （openGauss. SQL syntax［EB/OL］. ［2025-05-19］. 语法.html.）
[16]	SQLGlot. SQLGlot homepage［EB/OL］. ［2025-05-28］..
[17]	jOOQ. jOOQ homepage［EB/OL］. ［2025-05-28］..
[18]	SQLines. SQLines homepage［EB/OL］. ［2025-05-28］..
[19]	ELAMPARITHI M， ANURATHA V. A review on database migration strategies， techniques and tools［J］. World Journal of Computer Application and Technology， 2015， 3（3）： 41-48.
[20]	华为云. 数据库和应用迁移UGO［EB/OL］. ［2025-05-19］..
	Cloud Huawei. Database and application migration UGO ［EB/OL］. ［2025-05-19］..
[21]	Cloud Google. Gemini helps migrate Oracle to PostgreSQL on Google Cloud［EB/OL］. ［2025-03-22］..
[22]	ZHOU W， GAO Y， ZHOU X， et al. Cracking SQL barriers： an LLM-based dialect translation system［J］. Proceedings of the ACM on Management of Data， 2025， 3（3）： No.141.
[23]	BROWN T B， MANN B， RYDER N， et al. Language models are few-shot learners［C］// Proceedings of the 34th International Conference on Neural Information Processing Systems. Red Hook： Curran Associates Inc.， 2020： 1877-1901.
[24]	KATHARE N， REDDY O V， PRABHU V. A comprehensive study of Elasticsearch［J］. International Journal of Science and Research， 2021， 10（6）： 716-720.
[25]	REIMERS N， GUREVYCH I. Sentence-BERT： sentence embeddings using Siamese BERT-networks［C］// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Stroudsburg： ACL， 2019： 3982-3992.
[26]	ROBERTSON S， ZARAGOZA H. The probabilistic relevance framework： BM25 and beyond［J］. Foundations and Trends^® in Information Retrieval， 2009， 3（4）： 333-389.
[27]	TOUVRON H， LAVRIL T， IZACARD G， et al. LLaMA： open and efficient foundation language models［EB/OL］. ［2025-01-02］..

功能特性	语法数	示例
DDL	126	CREATE、ALTER
DML	15	INSERT、UPDATE、DELETE
DQL	5	UNION、INTERSECT
TCL	11	BEGIN、COMMIT/END
DCL	25	REVOKE、SET ROLE
性能与维护	28	EXPLAIN、EXPLAIN PLAN
安全管理	17	ALTER AUDIT POLICY
游标与预处理语句	9	DECLARE、CURSOR
高级特性	21	CREATE PACKAGE与 DROP PACKAGE
资源与扩展	14	CREATE RESOURCE与 DROP RESOURCE
企业版特性	19	CREATE、ALTER、DROP

功能特性	语法数	示例
DDL	126	CREATE、ALTER
DML	15	INSERT、UPDATE、DELETE
DQL	5	UNION、INTERSECT
TCL	11	BEGIN、COMMIT/END
DCL	25	REVOKE、SET ROLE
性能与维护	28	EXPLAIN、EXPLAIN PLAN
安全管理	17	ALTER AUDIT POLICY
游标与预处理语句	9	DECLARE、CURSOR
高级特性	21	CREATE PACKAGE与 DROP PACKAGE
资源与扩展	14	CREATE RESOURCE与 DROP RESOURCE
企业版特性	19	CREATE、ALTER、DROP

二级分类	语法内容
功能描述	BEGIN可以用于开始一个匿名块，也可以用于开始一个事务……
注意事项	在gsql中使用BEGIN开启事务执行SQL语句时，勿将BEGIN与需要执行的SQL语句写在一行……
语法格式	1）开启匿名块：［DECLARE ［declare_statements］］ BEGIN execution_statements END； 2）开启事务：……
参数说明	1）declare_statements：声明变量，包括变量名和变量类型，如“sales_cnt int”。 2）execution_statements：……

二级分类	语法内容
功能描述	BEGIN可以用于开始一个匿名块，也可以用于开始一个事务……
注意事项	在gsql中使用BEGIN开启事务执行SQL语句时，勿将BEGIN与需要执行的SQL语句写在一行……
语法格式	1）开启匿名块：［DECLARE ［declare_statements］］ BEGIN execution_statements END； 2）开启事务：……
参数说明	1）declare_statements：声明变量，包括变量名和变量类型，如“sales_cnt int”。 2）execution_statements：……

类型	组元	内容
语句模板	Template	SELECT ［DISTINCT］ <column1>， <column2>， …， <columnN> FROM <table1> ［INNER JOIN <table2> ON <condition>］［LEFT JOIN <table3> ON <condition>］［RIGHT JOIN <table4> ON <condition>］［JOIN <table5> ON <condition>］ WHERE <condition> …
迁移样本	SourceSQL	SELECT * FROM users WHERE name LIKE CONCAT（'%'，？， '%'） AND age BETWEEN？ AND？ ORDER BY created_at DESC LIMIT 10
	TargetSQL	SELECT * FROM users WHERE name LIKE '%' \|\| ？ \|\| '%' AND age BETWEEN？ AND？ ORDER BY created_at DESC LIMIT 10
	ErrorContext	MySQL使用CONCAT函数进行字符串拼接，而openGauss使用'\|\|'作为字符串连接符，因此无法自动转换

CORER： collaborative multi-knowledge large language model prompt framework for IT application innovation database migration

面向信创数据库迁移的多知识库协同大语言模型提示框架CORER

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 27

Related Articles 15

Recommended Articles

Metrics

语句功能特性	语句示例	SQL语句数	占比/%
数据定义语言（DDL）	CREATE TABLE， ALTER INDEX	2 101	20.60
数据操作语言（DML）	INSERT， UPDATE， DELETE	3 826	37.40
数据查询语言（DQL）	SELECT（含JOIN/子查询）	1 848	18.10
事务控制语言（TCL）	COMMIT， SAVEPOINT	614	6.00
数据控制语言（DCL）	GRANT， REVOKE	307	3.00
性能与维护	ANALYZE TABLE， OPTIMIZE	512	5.00
高级特性	存储过程/触发器/窗口函数	759	7.50
资源与扩展	CREATE TABLESPACE	250	2.40

方法	准确率								平均准确率
方法	DDL	DML	DQL	TCL	DCL	性能与维护	高级特性	资源与扩展	平均准确率
UGO	0.947 1	0.935 3	0.915 2	0.961 4	0.970 5	0.921 6	0.813 8	0.905 7	0.921 3
CORER	0.950 2	0.932 1	0.935 4	0.955 7	0.965 3	0.945 2	0.870 9	0.920 4	0.934 4

方法	准确率								平均准确率
方法	DDL	DML	DQL	TCL	DCL	性能/维护	高级特性	资源/扩展	平均准确率
CORER	0.950 2	0.932 1	0.935 4	0.955 7	0.965 3	0.945 2	0.870 9	0.920 4	0.934 4
CORER-Rules⁻	0.913 7	0.896 5	0.882 3	0.921 4	0.940 2	0.874 5	0.723 1	0.856 2	0.762 9
CORER-Examples⁻	0.931 5	0.918 2	0.901 7	0.932 5	0.951 1	0.897 3	0.755 6	0.889 5	0.781 9
CORER-Base	0.854 2	0.863 9	0.837 1	0.880 3	0.902 4	0.801 5	0.690 8	0.783 3	0.720 9

[1]	Longyu XIONG, Shengdong DU, Haochen SHI, Jie HU, Yan YANG, Tianrui LI. Government affairs hotline question answering system based on knowledge-enhanced large language model architecture [J]. Journal of Computer Applications, 2026, 46(6): 1721-1727.
[2]	Jintao WANG, Zhilin GAO, Qixiang MENG, Fanliang BU. Legal case retrieval method via case information reformulation using large language model [J]. Journal of Computer Applications, 2026, 46(6): 1785-1792.
[3]	Taixin CAI, Fengfeng WEI. Large language model-enhanced ant colony optimization for multi-solution traveling salesman problems [J]. Journal of Computer Applications, 2026, 46(6): 1712-1720.
[4]	Jiali ZHENG, Gang ZHOU, Jing CHEN, Shunhang LI. Adaptive multi-feature fusion detection method for AI-generated text [J]. Journal of Computer Applications, 2026, 46(5): 1433-1440.
[5]	Xing SHENG, Sunxian WENG, Kuosong CHEN, Zhongping WANG, Ruifeng REN, Yong LIU. Deep learning-based patent value evaluation for power grid enterprises [J]. Journal of Computer Applications, 2026, 46(5): 1468-1474.
[6]	Qianfei WANG, Yang LI, Deyu LI, Suge WANG. Dual-channel feature fusion representation method for short-text clustering based on large language model [J]. Journal of Computer Applications, 2026, 46(5): 1441-1449.
[7]	Xiaoyu WANG, Xin LI, Di XUE, Zhangtao JIANG, Wei WANG, Yanjun XIAO. Vulnerability classification framework for video surveillance network security based on large language models [J]. Journal of Computer Applications, 2026, 46(4): 1158-1170.
[8]	Kaizhou SHI, Xuan HE, Guoyi HOU, Gen LI, Shuanggao LI, Xiang HUANG. Airborne product metrological traceability knowledge graph construction method based on large language models [J]. Journal of Computer Applications, 2026, 46(4): 1086-1095.
[9]	Haoyang ZHANG, Liping ZHANG, Sheng YAN, Na LI, Xuefei ZHANG. Review of large language model methods for knowledge graph completion [J]. Journal of Computer Applications, 2026, 46(3): 683-695.
[10]	Bin SHEN, Xiaoning CHEN, Hua CHENG, Yiquan FANG, Huifeng WANG. Intelligent undergraduate teaching evaluation system based on large language models [J]. Journal of Computer Applications, 2026, 46(3): 993-1003.
[11]	Enkang XI, Jing FAN, Yadong JIN, Hua DONG, Hao YU, Yihang SUN. Review of threats faced by federated learning in privacy and security field [J]. Journal of Computer Applications, 2026, 46(3): 798-808.
[12]	Yiming HUANG, Xihua ZOU, Guo DENG, Di ZHENG. Pre-answering and retrieval filtering： dual-stage optimization method for RAG-based question-answering systems [J]. Journal of Computer Applications, 2026, 46(3): 696-707.
[13]	Dingjia WU, Zhe CUI. MG-SQL： SQL generation framework with enhanced schema linking and multi-generator collaboration [J]. Journal of Computer Applications, 2026, 46(3): 723-731.
[14]	Rilong WANG, Zhenping LI, Xiaosong LI, Qiang GAO, Ya HE, Yong ZHONG, Yingxiao ZHAO. Multi-Agent collaborative knowledge reasoning framework [J]. Journal of Computer Applications, 2026, 46(3): 708-714.
[15]	Fei GAO, Dong CHEN, Dixing BIAN, Wenqiang FAN, Qidong LIU, Pei LYU, Chaoyang ZHANG, Mingliang XU. Multistage coupled decision-making framework for researcher redeployment after discipline revocation [J]. Journal of Computer Applications, 2026, 46(2): 416-426.