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: Intelligent annotation of data models can be implemented. In one embodiment, the method can receive a data model including entities and relationships between the entities. An entity can include a set of attributes. The method can annotate the data model by defining a logical entity including one or more of the entities, validate the logical entity, and automatically generate a message structure corresponding to the logical entity. The message structure can include properties mapped to at least some of the attributes of entities contained in the logical entity. The properties can be defined in one or more database tables represented by the logical entity.

Abstract: Techniques and solutions are described for identifying data, such as records in a relational database. The data can have a first plurality of attributes, a second plurality of which are used to create the identifier. The identifier can be included as a column in a data structure in which the data is stored, such as a column in a table storing a record. The disclosed data identifiers can provide semantically meaningful information. The disclosed identifiers can also improve data store performance, such as by facilitating data retrieval, and helping to guard against inserting duplicate entries in the data store.

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 identifying data, such as records in a relational database. The data can have a first plurality of attributes, a second plurality of which are used to create the identifier. The identifier can be included as a column in a data structure in which the data is stored, such as a column in a table storing a record. The disclosed data identifiers can provide semantically meaningful information. The disclosed identifiers can also improve data store performance, such as by facilitating data retrieval, and helping to guard against inserting duplicate entries in the data store.