International Business Machines Corporation

Ye Tao

Shuo Li

Peng Hui Jiang

Xiaobo Wang

Sheng Yan Sun

Hong Mei Zhang

ShengYan Sun

Xu Zhao

Xiao Xiao Chen

Xiao Feng Meng

Xiao Hui Xh Wang

Embodiments analyze an incoming query for a database, train a query model using machine learning based on the analyzing of the incoming query, determine a model result based on the trained query model; extract a similar information query based on the model result, generate a recommendation for query tuning and a visual impact of the recommendation for the query tuning based on the extracting of the similar information query, and output the recommendation for the query turning and the visual impact of the recommendation for the query tuning.

Recommendations for changes in database query performance

Functional verification of a new database feature is provided. A set of SQL query seeds are evolved using information contained in a knowledge map that was generated based on a real SQL query workload of an entity corresponding to a database. Real SQL queries are identified in the database that are similar to the evolved set of sample SQL query seeds. A new feature to be implemented in the database is injected into the real SQL queries that are similar to the evolved set of sample SQL query seeds. Multiple runtime access paths are generated for each of the real SQL queries injected with the new feature for output cross validation. Each of the real SQL queries injected with the new feature is run in a test environment using each of the multiple runtime access paths corresponding to a real SQL query injected with the new feature for functional verification.

Structured query language runtime access path generation for deep test in a database

Growing community of inventors

Beijing, China

Ye Tao