Abstract: Embodiments permit searching across different system landscapes, for data associated with master data objects. A map is constructed comprising (explicit, inferred) connections between different pieces of data located in various databases, systems, and landscapes. In certain embodiments the map is constructed utilizing a parameter (e.g., family name) present in a received query, as a boundary condition. The map may be in tabular form, and may conform to a particular notation scheme. Once the map is constructed, the query is executed thereupon to search for relevant data. The corresponding query result is received and stored, ultimately for communication back to the user posing the original query. Embodiments may be particularly suited to returning private data of a unique entity (e.g., natural person, corporation, juristical person) that is stored over a variety of different master data objects (e.g., employee, customer, supplier) and across complex system landscapes.

Abstract: Embodiments permit searching across different system landscapes, for data associated with master data objects. A map is constructed comprising (explicit, inferred) connections between different pieces of data located in various databases, systems, and landscapes. In certain embodiments the map is constructed utilizing a parameter (e.g., family name) present in a received query, as a boundary condition. The map may be in tabular form, and may conform to a particular notation scheme. Once the map is constructed, the query is executed thereupon to search for relevant data. The corresponding query result is received and stored, ultimately for communication back to the user posing the original query. Embodiments may be particularly suited to returning private data of a unique entity (e.g., natural person, corporation, juristical person) that is stored over a variety of different master data objects (e.g., employee, customer, supplier) and across complex system landscapes.

Abstract: An identifying data set is defined including semi-identifying attributes with semi-identifying attribute values corresponding to an entity. Descriptive records corresponding to the semi-identifying attributes are created. Based on the descriptive records, retention records with retention attributes are created. A composed retention attribute is generated based on applying cryptographic hash algorithms to the retention attributes stored in a database. A retention attribute description is generated with a category and payload corresponding to the composed retention attribute. The semi-identifying attributes with corresponding positions are stored in the retention attribute. Hashes corresponding to the retention attributes are computed based on a composing mechanism that includes logic to compute the hashes. The hashes are combined to generate the composed retention attribute. When a request is received to permanently delete the entity from the database, the entity may be permanently deleted from the database.

Abstract: A data information framework collects related data sharing characteristics (e.g., personal information, others) revealed by associated purpose information, and reports on that data. The location of the data is not restricted, and can be collected from various locations (e.g. different databases on different computer systems). An engine implements data creation defining links between different stored data structures (e.g., tables) using specific fields. A plurality of tables may be grouped into a smaller number of table clusters to facilitate constructing the data model. The model may be evaluated, enhanced, and/or corrected (e.g., by a user). The model may include fields reflecting the purpose information for the stored data, said fields accessible by the engine during data handling processes. The data model may include descriptions providing data storage location. Purpose information may be mapped to table fields.

Abstract: An identifying data set is defined including semi-identifying attributes with semi-identifying attribute values corresponding to an entity. Descriptive records corresponding to the semi-identifying attributes are created. Based on the descriptive records, retention records with retention attributes are created. A composed retention attribute is generated based on applying cryptographic hash algorithms to the retention attributes stored in a database. A retention attribute description is generated with a category and payload corresponding to the composed retention attribute. The semi-identifying attributes with corresponding positions are stored in the retention attribute. Hashes corresponding to the retention attributes are computed based on a composing mechanism that includes logic to compute the hashes. The hashes are combined to generate the composed retention attribute. When a request is received to permanently delete the entity from the database, the entity may be permanently deleted from the database.

Abstract: A data information framework collects related data sharing characteristics (e.g., personal information, others) revealed by associated purpose information, and reports on that data. The location of the data is not restricted, and can be collected from various locations (e.g. different databases on different computer systems). An engine implements data creation defining links between different stored data structures (e.g., tables) using specific fields. A plurality of tables may be grouped into a smaller number of table clusters to facilitate constructing the data model. The model may be evaluated, enhanced, and/or corrected (e.g., by a user). The model may include fields reflecting the purpose information for the stored data, said fields accessible by the engine during data handling processes. The data model may include descriptions providing data storage location. Purpose information may be mapped to table fields.