Systems, methods, and non-transitory computer readable media may be configured to access first database and a second database. The first database may be structured for a first data type, and may include a first data of the first data type. The second database may be structured for a second data type different from the first datatype, and may include a second data of the second data type. The second data may be associated with the first data. A first information request relating to the first data and a second information request relating to the second data may be received. A first response to the first information request may be determined based on the second data. A second response to the second information request may be determined based on the first data. An interface, through which the first response or the second response is accessible, may be provided.

Systems, computer program products, and methods are described herein for intelligent database modelling. The present invention is configured to capture, using the augmented reality application, a drawing made by the user on a medium; initiate a first machine learning model on the drawing; determine that the user is attempting to design a database model; extract at least a first entity from the drawing; determine, using the first machine learning model, one or more attributes for the first entity; initiate, via the augmented reality application, a first push notification for display on the computing device of the user; electronically receive, via the augmented reality application, a user selection of a subset of attributes from the one or more attributes; generate, using the augmented reality application, a conceptual database model based on at least the first entity and the subset of attributes.

A computer-based system may be configured to collect metadata for each source and target defined for a data pipeline and formatting information (e.g., schemas, transformations, etc.) associated with each entity and field. During the definition of the pipeline, how the data will end up in the target may be defined, for example, by a user of the computer-based system via a GUI/interface and/or the like. Information (e.g., modification information, etc.) describing how the data will end up in the target may be defined, stored, and accessed to determine and/or track over which fields and entities are affected by the user-defined mutations and over which schemas. Lineage information (e.g., a genealogical tree, data lineage tracing, etc.) describing a data, version, and transformation may be generated and used to determine a source for a data record, how changes to the data record are related, how lineage evolved, and/or the like.

A data model for managing custom fields for tasks in projects. The custom fields can be defined and shared across an organization, and are always unique. Each type of custom field is an object that is subclass of a generic object. Other objects define attributes of the custom fields and assign values to the attributes. The data model allows the custom fields to be preserved and managed across multiple projects and users.

A building system including one or more memory devices having instructions stored thereon, that, when executed by one or more processors, cause the one or more processors to generate an event subscription for a consuming system, the event subscription defining events to be sent to the consuming system. The building system operates to receive an event from an event source, the event comprising data and a timestamp and identify contextual data describing the event in a digital twin, the digital twin comprising a virtual representation of a building. The building system operates to enrich the event with the contextual data and provide, based on the event subscription and the contextual data of the enriched event, the enriched event to the consuming system.

Automatically mapping unlabeled input data onto a target schema via semantic type detection is described. The input data includes data elements that are structured as 2D table rows and columns forming cells. Each data element is included in a cell. The target schema includes a set of fields. Schema mapping includes mapping each column to one or more fields. More particularly, the fields are clustered into field clusters, where each field cluster includes one or more of the fields. Each column is automatically mapped to one of the field clusters of the set of field clusters. The mapping between schema fields and data columns is automatically performed based on appropriate pairings of the detected semantic types, where the semantic types are encoded in vector representations of the fields, the field clusters, and the data elements.

A method of populating a data set includes generating a plurality of models that model the behavior of an agent process, where the plurality of models includes a first model, a second model, and a third model. The method also includes using the plurality of models to generate one or more requests for one or more external data sources, and using the plurality of models to select a plurality of queries from a data store of predefined queries. The plurality of queries are selected by the plurality of models to request information that is missing from the data set. The method also includes populating at least a portion of the data set using information received in response to the one or more requests for the one or more external data sources and the plurality of queries.

The present disclosure relates to generating a filtered data set. Data from a plurality of systems of record of a plurality of data source providers may be accessed. A master data set generated using the data accessed from the plurality of systems of record may be maintained. Restriction policies including one or more rules for restricting sharing of data may be maintained. A filtered data set may be generated for a data source provider responsive to an application of restriction policies of other data source providers to the master data set. The filtered data set may be provisioned.

The present disclosure relates to generating performance profiles of member nodes. A plurality of electronic activities can be accessed. A subset of electronic activities from the plurality of electronic activities can be identified. The subset of electronic activities can be parsed to identify participants of the electronic activities. A second node profile can be accessed for each participant. Participant types can be identified from each second node profiles. A distribution of the subset of electronic activities can be determined. A performance profile can be generated.

An information processing apparatus includes a mapper that determines a mapping candidate corresponding to a description included in the input data from among hierarchically structured classes or properties included in a standard data model. The mapper detects a class or a property that exactly matches or partially matches, with respect to a schema included in the input data and a content thereof, the content from a class hierarchical structure of the standard data model and a set of properties defined for a class, and determines a first mapping candidate based on the detected class or property.