Abstract: Techniques and solutions are described for converting data models between formats, such as between a conceptual data model and a physical data model for a database system, or between a conceptual data model and artefacts to be implemented in the database system. The conceptual data model is annotated with annotations that allow the physical data model or database artefacts to be automatically generated from the conceptual data model. The annotations can reflect relationships between entity types in the physical data model, such as inheritance relationships, header/item relationships, or one-to-one cardinality relationships. Annotations can also indicate attributes that should be added to entity types in the conceptual data model, such as attributes for versioning or data governance, that may not be used in the conceptual data model. Annotations can be used to determine how entity types in the conceptual data model will be denormalized in the physical data model.

Abstract: Techniques and solutions are described for converting data models between formats, such as between a conceptual data model and a physical data model for a database system, or between a conceptual data model and artefacts to be implemented in the database system. The conceptual data model is annotated with annotations that allow the physical data model or database artefacts to be automatically generated from the conceptual data model. The annotations can reflect relationships between entity types in the physical data model, such as inheritance relationships, header/item relationships, or one-to-one cardinality relationships. Annotations can also indicate attributes that should be added to entity types in the conceptual data model, such as attributes for versioning or data governance, that may not be used in the conceptual data model. Annotations can be used to determine how entity types in the conceptual data model will be denormalized in the physical data model.

Abstract: Techniques and solutions are described for converting data models between formats, such as between a conceptual data model and a physical data model for a database system, or between a conceptual data model and artefacts to be implemented in the database system. The conceptual data model is annotated with annotations that allow the physical data model or database artefacts to be automatically generated from the conceptual data model. The annotations can reflect relationships between entity types in the physical data model, such as inheritance relationships, header/item relationships, or one-to-one cardinality relationships. Annotations can also indicate attributes that should be added to entity types in the conceptual data model, such as attributes for versioning or data governance, that may not be used in the conceptual data model. Annotations can be used to determine how entity types in the conceptual data model will be denormalized in the physical data model.

Abstract: Techniques and solutions are described for converting data models between formats, such as between a conceptual data model and a physical data model for a database system, or between a conceptual data model and artefacts to be implemented in the database system. The conceptual data model is annotated with annotations that allow the physical data model or database artefacts to be automatically generated from the conceptual data model. The annotations can reflect relationships between entity types in the physical data model, such as inheritance relationships, header/item relationships, or one-to-one cardinality relationships. Annotations can also indicate attributes that should be added to entity types in the conceptual data model, such as attributes for versioning or data governance, that may not be used in the conceptual data model. Annotations can be used to determine how entity types in the conceptual data model will be denormalized in the physical data model.

Abstract: According to an embodiment of the present disclosures, systems, methods, and non-transitory computer-readable mediums having program instructions thereon, provide for a framework for automatically transferring metadata from different sources and different components of a computing platform. Further, the framework also provides for the seamless inclusion of additional metadata sources from the computing platform. The framework also provides for visualizing (e.g., modeling) the extracted metadata with a graphical user interface software application.

Abstract: Embodiments relate to methods and apparatuses implementing database-level consistency checking in a declarative manner. A consistency engine within the database layer may access one or more consistency rules in the form of a table or executable program code. Based upon application of these consistency rules to records comprising combinations of data characteristics, the consistency engine may determine the validity or invalidity those records. Consistency rules may implement a ‘check’ method, and also a ‘derive’ method allowing derivation of data characteristics (targets) in a record from other characteristics (sources) in the record. Filters may be used to split data records to sets of records having all fields assigned, and those having ‘not assigned’ fields. Consistency rules used for derivation methods can be nested. Also, in certain embodiments a consistency engine may use filtering techniques for constraint checking including multi-level derivations in a declarative way.

Abstract: In a banking application system including numerous banking applications, a data management framework allows for the generalized storage and subsequent access of result data. Through the application of the framework to the raw processing data generated by one of the banking applications, the result data is stored in a general storage location. Through this framework, the data is then readily accessible by all other banking applications. This framework includes a header field, characteristics data and key figure data. This removes proprietary restrictions on the data itself and thus makes it available to the other applications, increasing cross-system efficiency by allowing other applications to access and subsequently use the result data.

Abstract: In a banking application system including numerous banking applications, a data management framework allows for the generalized storage and subsequent access of result data. Through the application of the framework to the raw processing data generated by one of the banking applications, the result data is stored in a general storage location. Through this framework, the data is then readily accessible by all other banking applications. This framework includes a header field, characteristics data and key figure data. This removes proprietary restrictions on the data itself and thus makes it available to the other applications, increasing cross-system efficiency by allowing other applications to access and subsequently use the result data.