10 results about "Metadata schema" patented technology

A machine-readable medium stores instructions that, when executed by a processor, cause the processor to execute operations. The operations include receiving information identifying a database table and preprocessing the table. The preprocessing includes extracting primary and foreign key relationships and generating sample data by combining frequency-based sampling of common values with random sampling. The operations also include generating, using an LLM, a unified metadata schema. The generating includes processing the table and the sample data using domain-specific prompts and outputting the unified metadata schema. The operations include embedding and indexing the unified metadata schema and converting a natural language question output from an end-user device into a SQL query. The converting includes identifying relevant metadata in the unified metadata schema matching query intent and generating the SQL query using the filtered metadata. The operations also include executing the SQL query and providing query results to the end-user device.

The application provides a review recommendation method based on a meta-learning heterogeneous graph attention neural network. The method constructs original data into required metadata modes through a meta-learning process and a graph neural network model. A designed local graph extraction algorithm constructs the relationship among three types of nodes of users, commodities and reviews, expands a graph attention network structure taking each user as a task, and is used for capturing the preference information of the corresponding reviews of the user.

The disclosed process validation platform enables generation of metadata associated with data validation tests. For example, the process validation platform can retrieve a dataset associated with a data transformation pipeline and validate the quality of the dataset. Based on such validation, the process validation platform can generate a metadata schema and associated metadata structure (e.g., using a metadata generation model). The process validation platform can store the metadata structure in a cloud storage database and generate the metadata structure on a graphical user interface associated with a user to enable user interaction and modification of the generated metadata structure prior to the generation of associated code samples for data validation processes.

This application discloses a cross-data space data synchronization method based on connector construction, belonging to the field of data integration. The method includes: determining a source connector and a target connector based on a user-submitted task definition file; the source connector is the connector corresponding to the source data space, and the target connector is the connector corresponding to the target data space; each connector includes a connection configuration unit, a data pattern descriptor, semantic mapping rules, a data reading interface, and a data writing interface; determining a data synchronization task graph based on the semantic mapping rules of the source connector and the target connector; reading the original data from the source data space through the source connector; mapping the original data pattern and original semantics corresponding to the original data to a general data pattern and general semantics; mapping the general data of the general data pattern and general semantics to the target data of the target data pattern and target semantics through the target connector; and writing the target data into the target data space.

A system, method, and computer-program product include receiving, via an API server, an upload request that includes a logical table reference parameter and a structured data file. A table metadata schema corresponding to the logical table reference parameter is retrieved from a metadata repository. A plurality of data entries of the structured data file are converted into schema-conforming data records using column metadata definitions extracted from the schema. The conversion includes identifying source column headers matching source column identifiers, parsing associated cell value sets, validating the cell value sets based on criteria defined in the metadata definitions, and associating validated values with database columns. The schema-conforming data records are persisted to a database table designated in the table metadata schema.

Many service sectors deal with complex unstructured documents for making the customers understand and to make informed decisions or resolve issues. However, providing accurate and timely responses to customer queries on unstructured documents can be challenging. The present disclosure created a metadata schema that defines the structure and hierarchy of the unstructured document elements, such as headings and subheadings. Further, the present disclosure implemented parsing rules to ensure that all applicable documents metadata are captured despite inconsistencies in formatting. Furthermore, semantic preamble solution is used to understand the context and relationships between terms, which helps in accurately categorizing and segregating related content.

Systems and methods for extracting information from documents using language models are disclosed. In an embodiment, a method includes receiving an input document containing unstructured or semi-structured data, performing document classification by providing a first prompt to a first language model, the first prompt including a section defining document classification parameters, example documents, and the input document, performing format pattern detection by providing a second prompt to a second language model, the second prompt including a section defining format analysis parameters, a list of pre-configured document patterns, and the input document, performing information extraction by providing a third prompt to a third language model, the third prompt including a section defining information extraction parameters, pattern extraction metadata associated with the matched document pattern, a schema specification, and the input document, wherein the third language model outputs a normalized data record according to the schema with field values and confidence scores.

Methods of troubleshooting non-event malfunctions include providing a database including pre-populated non-event issues and associated corrective actions, receiving search criteria regarding a particular non-event issue via entry of a search string at a user interface, parsing and normalizing the search string into a meta-data schema to produce a normalized search string, searching the database with the normalized search string to generate a listing of one or more particular corrective actions, and receiving the listing of one or more particular corrective actions that are associated with the normalized search string. Apparatus configured to carry out the methods are provided, as are other aspects.

Methods of troubleshooting non-event malfunctions. The methods include providing a database including pre-populated non-event issues and associated corrective actions, inputting search criteria regarding a particular non-event issue via entry of a search string at the user interface, parsing and normalizing the search string into a meta-data schema to produce a normalized search string, searching the database with the normalized search string to generate a listing of one or more particular corrective actions, and receiving the listing of one or more particular corrective actions that are associated with the normalized search string. Apparatus configured to carry out the methods are provided, as are other aspects.

A computing system that includes one or more server computing devices including one or more processors configured to execute instructions for a domain extensibility module that provides software development tools for building domain extensions for a database platform, and a data ingestion module that provides software development tools for defining a metadata schema for extracting metadata from data files. The one or more processors are configured to receive a set of data from a user computing device, define a target metadata schema that includes one or more metadata fields that will be populated during a data ingestion process, define a target domain extension that defines one or more data types for storing the received set of data after performing the data ingestion process, and ingest the received set of data using a metadata extraction pipeline to generate metadata files based on the target metadata schema.