A mapping and query service for mapping between object-oriented programming objects and deep key-value data stores. The service to implement a store operation for a mapping and query service that supports the storage of a set of one or more objects having classes and fields written in source code of an object-oriented programming language in a deep key-value data store.

The subject technology receives, by a database system, raw input data from a source table provided by a machine learning development environment, the source table comprising multiple rows where each row includes multiple columns, the raw input data comprising values in a first format, the values comprising input features corresponding to datasets included in the raw input data for machine learning models, the machine learning development environment comprising an external system from the database system and is accessed by a plurality of different users that are external to the database system. The subject technology generates cell data for a feature store table based at least in part on the values from the source table. The subject technology performs at least one database operation to generate the feature store table including at least table metadata, column metadata, and the generated cell data.

A bidirectional mapping is established between network content and application programs, based on declarations at both the network content and at the application. Additionally, bidirectional mapping can provide for deep links, which can associate specific network content with a specific presentation of data in an application program. The identification format for such deep links can conform to a predetermined standard or it can be custom implemented according to a format declared either as part of the network content or the application program. The bidirectional mapping is then utilized by a lookup service to provide functionality to a third-party entity. The lookup service can identify, to the entity, application programs associated with network content specified by that entity and network content associated with application programs specified by that entity.

A bidirectional mapping is established between network content and application programs, based on declarations at both the network content and at the application. Additionally, bidirectional mapping can provide for deep links, which can associate specific network content with a specific presentation of data in an application program. The identification format for such deep links can conform to a predetermined standard or it can be custom implemented according to a format declared either as part of the network content or the application program. The bidirectional mapping is then utilized by a lookup service to provide functionality to a third-party entity. The lookup service can identify, to the entity, application programs associated with network content specified by that entity and network content associated with application programs specified by that entity.

Systems and methods for generating a unified metadata model, that includes selecting a first source metadata model, copying a first class, from the first source metadata model, to a first modified metadata model using a unified metadata mapping, and after copying the first class, selecting a second source metadata model, copying a second class, from the second source metadata model, to a second modified metadata model using the unified metadata mapping, and creating the unified metadata model using the first modified metadata model and the second modified metadata model.

Systems and methods for generating a unified metadata model, that includes selecting a first source metadata model, copying a first class, from the first source metadata model, to a first modified metadata model using a unified metadata mapping, and after copying the first class, selecting a second source metadata model, copying a second class, from the second source metadata model, to a second modified metadata model using the unified metadata mapping, and creating the unified metadata model using the first modified metadata model and the second modified metadata model.

A computer-implemented method, system and computer program product for generating visualizations for semi-structured data. Visualization data is extracted from infographics depicting semi-structured data. The visualization data that is extracted includes the traits or characteristics of the semi-structured data depicted in the infographics (e.g., dimension), the characteristics of the infographics (e.g., location of the depicted data), and the constraints or display requirements (e.g., display target value in a particular axis). A trait and constraint rule set is then generated based on the extracted visualization data. The trait and constraint rule set includes a set of rules that maps the display requirements to the particular set of traits or characteristics exhibited by the semi-structured data displayed in the infographics. A model is then trained to map the semi-structured data to elements of the infographics using the trait and constraint rule set and the characteristics of the infographics using association rule learning.

A computer-implemented method, system and computer program product for generating visualizations for semi-structured data. Visualization data is extracted from infographics depicting semi-structured data. The visualization data that is extracted includes the traits or characteristics of the semi-structured data depicted in the infographics (e.g., dimension), the characteristics of the infographics (e.g., location of the depicted data), and the constraints or display requirements (e.g., display target value in a particular axis). A trait and constraint rule set is then generated based on the extracted visualization data. The trait and constraint rule set includes a set of rules that maps the display requirements to the particular set of traits or characteristics exhibited by the semi-structured data displayed in the infographics. A model is then trained to map the semi-structured data to elements of the infographics using the trait and constraint rule set and the characteristics of the infographics using association rule learning.

The present disclosure involves systems, software, and computer implemented methods for providing default values for fields of data objects in an offline mode. One example method includes receiving, at a client device, a default group mapping that includes a default group identifier and a default value to be used as an initial value for a field. A field mapping can be received that includes a default group identifier and a field identifier. A request can be received while the client device is offline to create an instance of an object. A determination can be made that the field mapping includes a field identifier for a field of the object. A default value can be retrieved, from a local repository on the client device. A field value of the field in a created instance of the data object can be set to be the default value.

Techniques are described for diagnosing, characterizing, and addressing problems across a variety of industry sectors. In some embodiments, a system receives information about a company or other entity and maps the information to different areas of operation that are relevant to the entity. The system may identify potential problems and root causes that degrade operations relevant to the entity. The system may further use a model to gauge how significant various sector-specific and/or sector-generic problems are for the entity. Additionally or alternatively, the system may compare the scores to benchmark models to determine how an entity is performing and progressing relative to other entities in the same sector and/or across different sectors. The techniques allow users to quickly assess the performance of an entity across several different areas of operation, isolate underperforming areas, identify the root causes, and deploy technical solutions to address underlying problems.