Abstract: Methods, systems, and computer programs are described for tracking evaluation of workload stability through performance indexing. A plurality of metric source data is received by at least one hardware processor. Based on this data, a workload is identified as a stable workload candidate. A performance index is then generated, reflecting the characteristics of the identified stable workload candidate. The performance index is continuously tracked over a period of time, enabling the detection and analysis of any modifications to the workload and the subsequent impact on system performance.

Abstract: A method includes updating, by at least one hardware processor, a table with a detected regression associated with database code of a database. A first stored procedure is performed to determine a root cause of the detected regression. A second stored procedure is performed to determine an impact of the detected regression based at least on the root cause. A determination is made on whether to perform mitigation of the detected regression based on the impact.

Abstract: Methods, systems, and computer programs are presented for providing performance metrics in an online performance analysis system employing customer production workloads. A plurality of metric source data is received from a cloud data platform. A workload is identified as a stable workload candidate based at least in part on the plurality of metric source data. The cloud data platform generates a performance index based on the workload being identified as a stable workload candidate. The performance index is tracked over a period of time to identify changes in workload.

Abstract: A method and system for genomic selection of a non-family and low-heritability variety are provided. The method includes: evaluating a single nucleotide polymorphisms (SNPs) effect size based on a non-equivalent condition of SNPs by correcting a correlation relationship between the SNPs; and breeding the non-family and low-heritability variety based on the SNPs effect size. In the present disclosure, the method for evaluating the SNPs effect size can be used for breeding the non-family and low-heritability variety.

Abstract: A method includes decoding, by at least one hardware processor, a notification of a changed database code of a database. A query is executed responsive to the notification. The query indicates a data processing command and a data object in the database. A regression in the changed database code is detected based on multiple regression testing operations applied to the data processing command and the data object. Analysis of the regression is performed to detect a rollout parameter of a plurality of rollout parameters as a root cause of the regression. The plurality of rollout parameters are associated with the changed database code. A determination is made on whether to perform a mitigation action for the regression based on the rollout parameter.

Abstract: Provided herein are systems and methods for automating the rollout of database changes. For example, a method includes detecting a change in database code of a database resulting in a changed database code. The change in the database code includes a parameter rollout for at least one parameter setting of the database. Execution of a query associated with the change in the database code is monitored. The query uses the at least one parameter setting. A regression in the changed database code is detected based on the monitoring. Impact analysis is performed to determine a scope of impact of the regression on at least another query using the at least one parameter setting. A determination is made on whether to perform a rollback of the change in the database code or perform mitigation based on the scope of impact.