Sap Se

Sandra Bracholdt

Jan Portisch

Volker Saggau

Joachim Gross

Michael Hladik

Christoph Meinel

Stephan Schub

Roland Kolata

Oliver Huth

Bjoern Pantel

Benjamin Merkle

Ronald Boehle

Björn Pantel

Ulrich Block

Various examples are directed to systems and methods for utilizing relationship data. A computing system may receive a time-dependent query against a knowledge graph data structure. The computing system may access confirmation data from the knowledge graph data structure, the confirmation data describing a first plurality of confirmation points-in-time at which the first test relationship is true. The computing system may determine that at least one of a beginning or an end of a first time period associated with a test relationship of the time-dependent query is not defined by the knowledge graph data structure. The computing system may determine a response to the first time-dependent query indicating a veracity of the test relationship at a test point-in-time using the first plurality of confirmation points-and-time.

Systems and methods for manipulating time-dependent relationships in knowledge graph data structures

A computer-implemented method includes representing a plurality of database tables as respective vectors in a multi-dimensional vector space, receiving an indication that a first database table represented by a first vector and a second database table represented by a second vector are related to each other, moving positions of the respective vectors representing the plurality of database tables in the multi-dimensional vector space in response to the indication, and grouping the plurality of database tables into one or more table clusters based on positions of the respective vectors representing the plurality of database tables in the multi-dimensional vector space.

Relationship analysis using vector representations of database tables

An intelligent labeling method is disclosed herein. The method can receive a plurality of input instances and access a knowledge graph comprising a plurality of nodes and a plurality of edges connecting the plurality of nodes. The plurality of nodes represent objects and the edges represent relationship between the objects. For at least some selected input instances, the method can identify respective matching objects represented by corresponding nodes of the knowledge graph. A selected input instance shares a common label with the corresponding matching object. For identified matching objects, the method can identify a common class object represented by a corresponding node of the knowledge graph. A percentage of identified matching objects are descendants of the common class object, and the percentage is greater than a predefined threshold. The method can associate a label of the common class object with the plurality of input instances.

Systems and methods for intelligent labeling of instance data clusters based on knowledge graph

A computer-implemented method includes receiving a request of data records for a data schema, obtaining a schema alignment which maps the data schema to a subset of an ontology of a knowledge graph, generating at least one query based on the subset of the ontology of the knowledge graph, obtaining a list of data records by executing the at least one query on the knowledge graph to obtain a list of data records, and persisting the list of data records to the data schema.

Schema-based data retrieval from knowledge graphs

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.

Automatic conversion of data models using data model annotations

Various examples are directed to systems and methods for administering data model metadata for a plurality of data models. A metadata service may receive a first retrieval request from a requesting system. The first retrieval request may comprise an indication of a first local object identifier referencing a definition of a first local object from a first data model and an indication of a target data model. The metadata service may retrieve a first record from a metadata identifier registry, where the first record comprises an indication of the first local object identifier and an indication of a first global object identifier corresponding to the first local object identifier. The metadata service may determine a second local object identifier referencing a definition of a second local object identifier referencing a definition of a second local object in the target data model and return the second local object identifier.

Metadata object identifier registry

A metastructure for representing and manipulating or analyzing a data model is provided herein. A source representation of the first data model may be received. The source representation may include a representation of one or more structural components of the first data model, respectively having identifiers and types. One or more instances of a metastructure schema object datatype may be generated based on the one or more structural components of the source representation. Generating a given instance of the metastructure schema object datatype for a given structural component may include assigning a first identifier of the given structural component to the first data member of the given instance, determining a first type associated with the given structural component, and assigning the first type to the second data member of the given instance. The one or more instances of the metastructure schema object datatype may be stored in association.

Matching metastructure for data modeling

A computer-implemented method includes representing a plurality of database tables as respective vectors in a multi-dimensional vector space, receiving an indication that a first database table represented by a first vector and a second database table represented by a second vector are related to each other, moving the respective vectors representing the plurality of database tables in the multi-dimensional vector space in response to the indication, and grouping the plurality of database tables into one or more table clusters based on positions of the respective vectors representing the plurality of database tables in the multi-dimensional vector space.

A variety of scenarios for generating mock data based on original data are described. Characteristics of the original data can be reflected in the mock data without having access to the actual original data. Instead, statistical artifacts representing the original data can be used to instantiate reconstructed database tables. In relational database scenarios, foreign key relationships can be preserved even if the actual values are not known. The reconstructed database tables can be used as mock data in a variety of circumstances, including testing, and is especially useful to recreate characteristics of the original data when the original data cannot be made available for size, confidentiality, or privacy reasons.

Generation of realistic mock data

Transformation rule generation and validation functionality is provided herein. Transformation rules can be generated for one or more mappings in an alignment between a source database and a target database. The transformation rules can transform instance data from the source data model to a form matching the target data model. One or more transformation rules can be generated for a mapping between fields in a source database and a field in a target database. The transformation rules can be generated based on one or more source fields and a target field of a mapping, and one or more identified functions. Evaluating the transformation rules can include generating test data based on the transformation rules applied to instance data from the source database. The test data can be evaluated against instance data from the target database. The transformation rules and the evaluation results can be provided in a user interface.

Transformation rule generation and validation

Techniques and solutions are described for defining transformation specifications in a programming-language independent language and converting such specifications to one or more executable formats. The language can provide for defining rules and actions. Rules can refer to (e.g., be based at least in part on) data targets, such as attributes of a schema, whose identifiers are to be read or updated, or to other rules. Rules can be reused, and can recursively refer to one another, such that a large number of complex schema transformations can be accomplished using a series of first order logic statements. Actions can define what, and how, values will be changed when a predicate rule is satisfied. A transformation specification in the language can be parsed and selectively complied to one or more executable formats, including in programming languages such as the structured query language. Disclosed technologies can facilitate data transformations by non-technical users.

Logical, recursive definition of data transformations

Techniques and solutions are described for determining a set of elements of a second set that may correspond to a given element of a first set of elements. The elements can be, in specific examples, components of a database system, such as tables (or entities), attributes, or records. Significance metric values are calculated for elements in the first and second sets. The significance metric values can be a number of records in an entity or a number of read or write access operations for an entity or for a record of an entity. A significance metric value for the given element can be used at least in part to select elements of the second set as potential match candidates, based at least in part on significance metric values for elements of the second set. Selecting elements can include selecting elements based on a window of elements of the second set or a range of significance metric values.

Candidate element selection using significance metric values

Techniques and solutions are described for determining, evaluating, and selecting data retrieval pathways. A plurality of database objects, such as tables, can be retrieved, and relationships between them determined. The relationships are between various attributes of the database objects. A plurality of pathways are determined between at least two target database objects, where the path determination does not consider attribute-level relationship information. After a path is determined, the relationship-level information can be added. A summary of the path, and relationship information, can be displayed to a user. A user can select paths to remove from the display, and can indicate one or more database objects that are required for a valid path, one or more database objects that are prohibited from valid paths, or combinations thereof. A user can select a path, and a query language statement to implement the path can be generated and displayed to the user.

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Dielheim, Germany

Sandra Bracholdt