Abstract: In some examples, a system receives a user input to create a classification to use for a first data set. In addition, the system may receive a user input to associate the classification with the first data set as reference data. The system may determine a classification association with the first data set based in part on comparing a first classification model corresponding to the reference data with a second classification model of a second data set, the second classification model being determined based at least in part on a plurality of data properties of the second data set. Further, the system may determine a user curation result with respect to the classification association with the first data set, and may update the classification model for the first data set based at least in part on the user curation result.

Abstract: According to a present invention embodiment, a system determines a relationship between source and target database tables, and includes a computer system including at least one processor. Potential pivot keys of the target database table are determined, and maps are created for each potential pivot key between the database tables based on distinct values. Transformations for each map are generated that enable target data to be produced from source data. The transformations for each potential pivot key are analyzed and the potential pivot key with the transformations that generate the greatest amount of matching data is selected as the resulting pivot key. The database table columns corresponding to the resulting pivot key are determined to be associated by the relationship. Embodiments of the present invention further include a method and computer program product for determining a relationship between source and target database tables in substantially the same manner described above.

Abstract: According to a present invention embodiment, a system determines a relationship between source and target database tables, and includes a computer system including at least one processor. Potential pivot keys of the target database table are determined, and maps are created for each potential pivot key between the database tables based on distinct values. Transformations for each map are generated that enable target data to be produced from source data. The transformations for each potential pivot key are analyzed and the potential pivot key with the transformations that generate the greatest amount of matching data is selected as the resulting pivot key. The database table columns corresponding to the resulting pivot key are determined to be associated by the relationship. Embodiments of the present invention further include a method and computer program product for determining a relationship between source and target database tables in substantially the same manner described above.

Abstract: In a computing system, wherein applications access databases to obtain data and the databases are updated from time to time and the applications require consistent data from the databases even while an update is occurring, a first database; a second database, wherein the first database and second database a substantive copies of each other outside of an update period; a database indicator that indicates one of the first and second databases as a live database and the other one of the first and second databases as a load database; a query router for routing queries from application to the live database; and a router switcher for switching the database indicator such that the live database becomes the load database and the load database becomes the live database.

Abstract: In a data processing system, hierarchical documents or hierarchical messages are mapped to a Nested Relational Data Model to allow for transformation and manipulation using declarative statements. The resulting nested data can be converted to a relational format and mapped to multiple relational tables, and/or converted from a nested relational format to an external hierarchical format, such as XML. The system can specify and execute declarative rules to extract, transform, integrate, load and update hierarchical and relational data. The system can also be used for extending documents with relational and non-relational data and applying updates based on these documents to relational database targets. The system can also be used for mapping Nested Relational Data to function calls that accept tables as parameters and return multiple scalar and table parameters as output.