Abstract: A method is presented to facilitate the training of a very large number of machine-learning performance models used to detect anomalies in computing operations. The models are grouped together according to model type, and are allocated to different pods of a computing environment that is used to carry out the operations being monitored. Initial training of models in a group is carried out while monitoring resource usage, and a particular pod is selected for further training based on the resource usage. The pod selected for training preferably has a minimum change in resource usage before and after the initial training. A different pod can be selected for scoring the trained models. The pod selected for scoring preferably has a maximum resource usage during an initial scoring among all pods.

Abstract: A virtual change database system that supports iterative and parallel database application development is disclosed. The system stores a common set of base physical data and a plurality of sets of virtual changes. Each set of virtual changes is associated with a database object. A database application may access a database object in the database by using the virtual version of the object to extract the object's data content from the common base physical data. The database system present a first query response to (i) a first application based on the set of base physical data and (ii) a first set of virtual changes for a particular database object, while also presenting a second query response to a second application based on the set of base physical data and a second, different set of virtual changes for the particular database object.

Abstract: A method and system for generating a job alert. According to embodiments of the present invention, before a target job is processed, a characteristic of input and output of the target job in at least one stage is determined through analyzing a historical job, and a resource overhead associated with the processing of the target job is calculated based on the characteristic of input and output. Then, an alert for the target job is generated in response to the resource overhead exceeding a predetermined threshold. In such manner, an alert for the target job can be proactively generated before the resource overhead problem occurs, so as to enable an administrator or developer to discover a fault in advance and adopt measures actively to avoid loss and damage to the intermediate results or output data when the target job is processed.

Abstract: A virtual change database system that supports iterative and parallel database application development is disclosed. The system stores a common set of base physical data and a plurality of sets of virtual changes. Each set of virtual changes is associated with a database object. A database application may access a database object in the database by using the virtual version of the object to extract the object's data content from the common base physical data. The database system present a first query response to (i) a first application based on the set of base physical data and (ii) a first set of virtual changes for a particular database object, while also presenting a second query response to a second application based on the set of base physical data and a second, different set of virtual changes for the particular database object.

Abstract: A method and system for generating a job alert. According to embodiments of the present invention, before a target job is processed, a characteristic of input and output of the target job in at least one stage is determined through analyzing a historical job, and a resource overhead associated with the processing of the target job is calculated based on the characteristic of input and output. Then, an alert for the target job is generated in response to the resource overhead exceeding a predetermined threshold. In such manner, an alert for the target job can be proactively generated before the resource overhead problem occurs, so as to enable an administrator or developer to discover a fault in advance and adopt measures actively to avoid loss and damage to the intermediate results or output data when the target job is processed.

Abstract: Provided is a database schema migration difference detecting technique for detecting differences of a customized database schema before and after database migration by using XML technology, including: extracting a first database schema from a fresh database of a release and saving the first database schema in a first XML file; extracting a customized database schema from a customized database of the release and saving the customized database schema in a second XML file; comparing the first and second XML files, filtering the identical content in the first and second XML files and saving differences in a delta XML file; and comparing the delta XML file with a new database to detect a state of the customized database schema after migration. The detecting state indicates whether there is any loss or change in the customized database schema after migration, as well as, conflicts with the database schema in a database of a new release.

Abstract: Provided is a database schema migration difference detecting technique for detecting differences of a customized database schema before and after database migration by using XML technology, including: extracting a first database schema from a fresh database of a release and saving the first database schema in a first XML file; extracting a customized database schema from a customized database of the release and saving the customized database schema in a second XML file; comparing the first and second XML files, filtering the identical content in the first and second XML files and saving differences in a delta XML file; and comparing the delta XML file with a new database to detect a state of the customized database schema after migration. The detecting state indicates whether there is any loss or change in the customized database schema after migration, as well as, conflicts with the database schema in a database of a new release.

Abstract: Provided is a database schema migration difference detecting technique for detecting differences of a customized database schema before and after database migration by using XML technology, including: extracting a first database schema from a fresh database of a release and saving the first database schema in a first XML file; extracting a customized database schema from a customized database of the release and saving the customized database schema in a second XML file; comparing the first and second XML files, filtering the identical content in the first and second XML files and saving differences in a delta XML file; and comparing the delta XML file with a new database to detect a state of the customized database schema after migration. The detecting state indicates whether there is any loss or change in the customized database schema after migration, as well as, conflicts with the database schema in a database of a new release.

Abstract: The present invention provides a method and an apparatus for aggregating database runtime information and analyzing application performance. According to one aspect of the present invention, there is provided a method for aggregating database runtime information, comprising: aggregating said database runtime information based on queries; and aggregating, based on objects, said database runtime information that is aggregated based on queries.

Abstract: The application relates to a tool and a method for customizing hint. According to the invention, provided is at least one hint service adapter configured to collect relevant information of different database platforms, validate customized hint on real database platforms and provide feedback, and deploy the customized hint on database platform. Also provided is hint defining means configured to customize hint independently of database platforms, and providing customized hint to the at least one hint service adapter.