Systems, methods, and instrumentalities are disclosed for switching a control scheme to control a set of system modules and/or modular devices of a surgical hub. A surgical hub may determine a first control scheme that is configured to control a set of system modules and/or modular devices. The surgical hub may receive an input from one of the set of modules or a device located in an OR. The surgical hub may make a determination that at least one of a safety status level or an overload status level of the surgical hub is higher than its threshold value. Based on at least the received input and the determination, the surgical hub may determine a second control scheme to be used to control the set of system modules. The surgical hub may send a control program indicating the second control scheme to one or more system modules and/or modular devices.

Techniques are disclosed for generating a database schema using trained machine learning models that, in some embodiments, may include graph neural networks (GNN). A GNN may identify source to target database schema mappings using, among other features of the graph, context data associated with each node in a graph. Context data describes relationships between a particular node and some (or all) of the other nodes in the graph. The system may use this context data (and other graph data) in combination with a trained GNN model to identify a mapping between one or more source database entities to corresponding target database entities.

Embodiments of the invention disclosed relate to systems and methods for facilitating the development of an athlete in a given sport. In one embodiment, a system can include one or more computing devices communicating via a computer network to receive input associated with Skill Scores and to compute and store an Athlete Development Rating (ADR) based at least on said input. In one embodiment, the ADR is based on a composite score of one or more Skills Scores and/or one or more Skill Group Scores. In certain embodiments, Skill Scores are associated with date data to provide a historical view of the athlete's development. In some embodiments, Skill Scores can be based on quantitative, objective assessments and/or qualitative, subjective expert assessments. Program modules can be configured to display historical ADR data, Skill Scores, and/or Skill Group Scores.

Data is processed iteratively by a database system with a first cache storing key-value data which resulted from previous iterations of processing input data and a second cache storing aggregated data which resulted from previous iterations of processing key-value data stored in the first cache. In a current iteration, the database system receives further input data related to the input data of the previous iterations, transforms the further input data into further key-value data and stores the further key-value data in the first cache in addition to the stored key-value data which resulted from previous iterations. The database system further processes the further key-value data and the aggregated data stored in the second cache to form updated aggregated data, and stores the updated aggregated data in the second cache for usage in further iterations. The database system also provides the updated aggregated data to at least one client.

Various embodiments provide methods, apparatus, systems, computing entities, and/or the like, for recommending and/or defining cross-schema mappings, or a mapping from a data element type of a first schema to one of a second schema. In one example embodiment, a method is provided. The method includes identifying a plurality of canonical data element types belonging to a canonical schema and an external data element type belonging to an external schema. The method includes obtaining a multi-dimensional representation for each data element type and determining a predicted similarity score for each canonical data element type using comparison results from a mapper machine learning model. The method further includes generating a recommendation data object indicating one or more mappings between the external data element type and a selected subset of the plurality of canonical data element types, the subset selected according to predicted similarity score.

A collaborative data schema management system for federated learning (i.e., federated data manager (FDM)) is provided. Among other things, FDM enables the members of a federated learning alliance to (1) propose data schemas for use by the alliance, (2) identify and bind local datasets to proposed schemas, (3) create, based on the proposed schemas, training datasets for addressing various ML tasks, and (4) control, for each training dataset, which of the local datasets bound to that training dataset (and thus, which alliance members) will actually participate in the training of a particular ML model. FDM enables these features while ensuring that the contents of the members' local datasets remain hidden from each other, thereby preserving the privacy of that data.

In accordance with embodiments, there are provided mechanisms and methods for creating, exporting, viewing and testing, and importing custom applications in a multitenant database environment. These mechanisms and methods can enable embodiments to provide a vehicle for sharing applications across organizational boundaries. The ability to share applications across organizational boundaries can enable tenants in a multi-tenant database system, for example, to easily and efficiently import and export, and thus share, applications with other tenants in the multi-tenant environment.

Techniques and solutions are described for storing and processing metadata, including to instantiate database artefacts at a target system based on metadata for database artefacts maintained at a source system. The target system can query the source system for metadata associated with database artefacts of the source system. The target system can instantiate database artefacts based on such metadata. The database artefacts of the target system are linked to corresponding database artefacts of the source system, such as by associating a database artefact of the target system with an API useable to obtain data or metadata from the source system for a corresponding database artefact of the source system. The target system obtains additional data or metadata for a database artefact of the target system using a corresponding API.

A data processing system for discovering a semantic meaning of a field included in one or more data sets is configured to identify a field included in one or more data sets, with the field having an identifier. For that field, the system profiles data values of the field to generate a data profile, accesses a plurality of label proposal tests, and generates a set of label proposals by applying the plurality of label proposal tests to the data profile. The system determines a similarity among the label proposals and selects a classification. The system identifies one of the label proposals as identifying the semantic meaning. The system stores the identifier of the field with the identified one of the label proposals that identifies the semantic meaning.

A method, an apparatus, and a system for configuring, designing, and/or implementing database tables are detailed that provides a framework into which a remainder of database tables is developed. Also detailed is a method to develop this framework of database tables. This so developed framework provides a platform for converting multiple independent heterogeneous databases into standardized homogeneous databases.