Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A computer-implemented method can receive a source table containing data tuples and a source schema defining attributes of the data tuples, and match the source schema to an ontology of a knowledge graph. The knowledge graph can include a plurality of instances and the ontology defines properties of the plurality of instances. The computer-implemented method can link the data tuples to respective instances in the knowledge graph, and identifying non-matching properties of the respective instances, wherein the non-matching properties are defined in the ontology and not matched to the source schema. The computer-implemented method can obtain property values associated with the non-matching properties from the knowledge graph, and add one or more of the non-matching properties and the associated property values to respective data tuples of the source table.

Abstract: A method for data model clustering is provided herein. A first representation of a data model may be received. Edge betweenness values may be determined for respective nodes in the first representation. At least one node in the first representation may be identified as a linking node based on the respective edge betweenness values. One or more linking nodes may be removed from the first representation, thereby forming at least a first cluster and a second cluster. Degrees for the respective remaining nodes may be calculated. Respective hub nodes may be identified for the respective clusters based on the respective degrees in the clusters. Respective descriptions may be generated for the respective clusters based on the respective hub nodes. A clustered representation of the first representation may be stored with the clusters and their respective descriptions.

Abstract: A computer-implemented method can receive a source table containing data tuples and a source schema defining attributes of the data tuples, and match the source schema to an ontology of a knowledge graph. The knowledge graph can include a plurality of instances and the ontology defines properties of the plurality of instances. The computer-implemented method can link the data tuples to respective instances in the knowledge graph, and identifying non-matching properties of the respective instances, wherein the non-matching properties are defined in the ontology and not matched to the source schema. The computer-implemented method can obtain property values associated with the non-matching properties from the knowledge graph, and add one or more of the non-matching properties and the associated property values to respective data tuples of the source table.

Abstract: A method for aligning data model schemas is provided herein. A first schema and a second schema may be received. The schemas may include sets of nodes and links between the nodes. An anchor point between the first schema and the second schema may be received. A source node in the first schema may be identified to be mapped to the second schema. A source distance may be calculated between the source node and the anchor point in the first schema. Option distances may be calculated between the anchor point and the other nodes in the second schema. Penalty scores may be calculated for the option distances. A mapping node may be selected from the nodes in the second schema based on their penalty scores. A new anchor point identifying a correspondence between the source node and the mapping node may be stored.

Abstract: 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.

Abstract: 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.

Abstract: A process for mining existing views to generate join statements is provided herein. A request to generate a join statement may be received, which may include a database object identifier to be part of the join statement. One or more views in the database may be identified, which may include the database object identifier. One or more edges in the one or more views may be identified, which may include the database object identifier. Scores for the one or more edges may be calculated. The edges may be filtered based on their scores according to one or more filtering rules. The filtered edges may be provided. A selected edge may be received, which may be selected from the provided filtered edges. The join statement may be generated in a query language. The join statement may be based on the selected edge. The generated join statement may be provided.

Abstract: 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.

Abstract: Smart rule development and rule mining functionality is provided herein. Rule mining for use in rule development can include generating logic statement proposals, rule deduplication, and rule template generation. Rule mining can include accessing a rule set to analyze the rule set against an input logic statement to identify existing rules which match at least in part the input logic statement. Rule deduplication can include returning exact rule matches to replace the input logic statement. Proposing logic statements can include returning logically related rules from rules found that include the input logic statement. Generating rule templates can include returning a template based on the entire rule(s) which includes the input logic statement. Ranking scores can be calculated for returned rules, whether for deduplication, proposals, or template generation. The scores can be based on statistical information for the rules, such as usage of the rule or coverage of the rule.