Systems, methods, and non-transitory computer readable media are provided for recursively searching a plurality of workspaces of the system for linked data associated with the seed data, initiating an endpoint process for each the seed data and the linked data, and, upon completion of the search, delete the seed data and the linked data identified based at least in part on the endpoint process. The process may be automatically repeated at a predetermined time interval to identify and remove future data that is stored in the plurality of datasets.

A system including a data center hosting a representational state transfer (REST) server in communication with a client network, wherein the REST server includes a GraphQL schema describing tables and fields of a communicatively coupled database. The REST server is configured to: receive a request that includes a GraphQL query; open a streaming connection to the client network; and output a beginning of a response via the streaming connection. The REST server is also configured to process the GraphQL query based on the GraphQL schema to generate a GraphQL result, and to output the GraphQL result in a body of the response via the streaming connection. The REST server is further configured to output an end of the response via the streaming connection, such that the response is correctly formatted in JavaScript Object Notation (JSON).

A method includes receiving a request to modify a first value of a first field of a first item in a self-describing data system, and obtaining a domain comprising items in the self-describing data system. The first item and a second item are included in items, and the second item comprises a second field having a second value. The method includes calculating, based on a rule of the second field, a dependency of the second value on the first value. The rule specifies how the second value is to be calculated using the first value. The method includes modifying, based on the request, the first value. The method includes receiving an event triggered by the modification to the first value. The method includes, responsive to the event, calculating the second value based on the rule, and storing the second value in the second field.

A computer-implemented method and system for generating partition polygons across a geographic area, wherein a concave hull of the reference network, usually a network of linear features such as roads, and the spatial data objects to be processed is first generated, the resulting concave hull thereby providing the boundary geometry for the final partition scheme. By generating a concave hull from the reference network and the spatial data objects, it is possible to derive a boundary that is based on the spatial position of the reference network and the spatial data objects at the periphery of the geographic area that they cover, that is, the boundary itself connects at least a portion of the end points of the reference network and the spatial data objects.

Embodiments minimize downtime involved in moving a PDB between CDBs by allowing read-write access to the PDB through most of the moving operation, and by transparently forwarding connection requests, for the PDB, from the source CDB to the destination CDB. The files of a source PDB are copied from a source CDB to a destination CDB, during which the source PDB may be in read-write mode. The source PDB is then closed to write operations so that changes to the source PDB cease. Another round of recovery is performed on the PDB clone, which applies all changes that have been performed on the source PDB during the copy operation and the PDB clone is opened for read and write operations. Forwarding information is registered with the source location, which information is used to automatically forward connection requests, received at the source location for the moved PDB, to the destination location.

Systems and methods are configured to determine whether a particular information object is a duplicate of an object found in separate information objects. In various embodiments, the particular information object and each separate information object includes a set of data fields for storing data values that allows identical values to be stored in different fields for the objects. The data values for the particular information object are combined to form a data structure that includes a data element for each value. A determination as to whether the particular information object is an exact or partial match of a separate information object is made by performing a function on the data structure for the particular information object and a data structure for the separate information object to identify an intersection that includes data values for the particular information object that have an identical match with values for the separate information object.

Systems and methods are configured to determine whether a particular information object is a duplicate of an object found in separate information objects. In various embodiments, the particular information object and each separate information object includes a set of data fields for storing data values that allows identical values to be stored in different fields for the objects. The data values for the particular information object are combined to form a data structure that includes a data element for each value. A determination as to whether the particular information object is an exact or partial match of a separate information object is made by performing a function on the data structure for the particular information object and a data structure for the separate information object to identify an intersection that includes data values for the particular information object that have an identical match with values for the separate information object.

A method of realizing a scalable fast query engine randomly shuffles object vectors of a massive array of object vectors to produce a sorted array of object vectors, each object vector containing a respective number of keys of a massive set of predefined keys, and inverts the sorted array, with ordered mapping, onto a set of key-specific arrays of objects. Upon receiving a query, a query-specific array of objects is formed from selected key-specific arrays corresponding to specific keys stated in the query. In response to the query, a target set of objects is formed to include the query-specific set and selected objects of key-specific sets of high intersection levels with the query-specific set. The method identifies candidate key-specific arrays from the entire set of key-specific arrays then determines precise, or exact, intersection levels of the candidate key-specific arrays with the query-specific array.

An aggregated performance reporting system includes a computing system in communication with a database that stores element records associated with the elements of a distributed computing environment. The computing system stores instructions that are executed to receive a search term from a user interface, and identify a count of each element type that matches the search term. The instructions then identify one or more aggregated performance indicators that are associated with each element type, issue a request to the database to retrieve those element records having the identified element types, calculate the aggregated performance indicators using parametric information included in the retrieved element records, and facilitate the display of the calculated aggregated performance indicators on a display.

A method includes generating, by a processor of a computing device, a first plurality of models (including a first number of models) based on a genetic algorithm and corresponding to a first epoch of the genetic algorithm. The method includes determining whether to modify an epoch size for the genetic algorithm during a second epoch of the genetic algorithm based on a convergence metric associated with at least one epoch that is prior to the second epoch. The second epoch is subsequent to the first epoch. The method further includes, based on determining to modify the epoch size, generating a second plurality of models (including a second number of models that is different than the first number) based on the genetic algorithm and corresponding to the second epoch. Each model of the first plurality of models and the second plurality of models includes data representative of neural networks.