An enterprise ontology, an application data usage model, and/or cross-application data dependencies may be developed using artificial intelligence. Using pattern recognition and/or information extraction techniques, the artificial intelligence may analyze application source code to identify common DDL or SQL statements to formulate an ontology and/or a usage model for the application. A plurality of application ontologies and/or data usage models may be used to build a semantic hub. The semantic hub may be analyzed to identify data redundancies, data use frequency, potential data quality challenges, and/or data dependencies between applications to produce a data abstraction model that allows legacy applications to communicate with one or more data stores.

Syntax parsing on a SQL statement is performed to determine whether an extended syntax identifier exists in the SQL statement, where the extended syntax identifier indicates a target computing service for the SQL statement. It is determined that the extended syntax identifier exists in the SQL statement. A computing service description statement in a first statement format is generated based on the SQL statement, where the first statement format is a statement format that can be recognized by a target computing framework. The computing service description statement is submitted to the target computing framework. Data queried by the SQL statement is invoked, in the target computing framework based on the computing service description statement, to perform target computation, where the SQL statement includes a computing element needed by the target computing service.

A system for processing database data includes an interface and a processor. The interface is configured to receive a query for the database data comprising a date range and a data selection criterion. The processor is configured to determine a set of fields of the database data corresponding to a most recent date of the date range; determine a subset of the set of fields of the database data specified by the data selection criterion; determine a set of transformations, where each transformation of the set of transformations corresponds to a field of the subset and a sub-range of the date range; transform the database data to determine transformed database data using the set of transformations; and select data from the transformed database data using the data selection criterion to determine a query response.

A system and method for processing of queries including receiving a query including a set operation and a sort operation, wherein the set operation includes a first data structure and a second data structure and the sort operation requests a result set that is sorted based on a column or attribute of the first data structure and a column or attribute of the second data structure; generating a query plan in which a sort operation occurs prior to the set operation; determining a first, partial set of one or more resultant rows responsive to the query; sending the first, partial set of one or more resultant rows responsive to the query to a client; determining a second, partial set of one or more resultant rows responsive to the query; and sending the second, partial set of one or more resultant rows to the client.

Embodiments of the invention are directed to a system, method, or computer program product for providing an intelligent quality accelerator with root mapping system. The system provides a business language model and process automation that stratifies sampling of resource exchanges from products using a machine learning loop and provides an end to end simulation for root cause analysis. In this way, the system provides two layers, a robust sampling and root cause analysis.

In some implementations, the method includes receiving data characterizing a user request indicative of retrieval of a portion of a hierarchical dataset associated with a hierarchical industrial asset and stored in a partition of a database. The user request includes pagination criteria and a search parameter. The method also includes selecting the portion of the hierarchical dataset based on the search parameter. The method further includes generating a plurality of data subsets from at least the portion of the hierarchical dataset. The searching is based on a threshold data subset size included in the pagination criteria. The generating includes dividing the portion of the hierarchical subset into the plurality of data subsets. The size of each data subset of the plurality of data subsets is less than the threshold data subset size. The method further includes providing the plurality of data subsets.

Solutions for enforcing both syntactic and semantic correctness of domain-specific data queries include: receiving a data query; constraining an operation of the data query to enforce semantic correctness, wherein enforcing semantic correctness comprises: determining semantic information associated with each of a plurality of data entities; based on at least the semantic information, determining whether combining the two data entities of the plurality of data entities is allowed or is not allowed; based on at least determining that combining the two data entities is allowed, permitting combining the two data entities in the data query; and based on at least determining that combining the two data entities is not allowed, preventing combining the two data entities in the data query; and executing the data query. Some examples further include basing the permission on (at least) an ontology between the semantic information of the two data entities and a set of axioms.

Systems and methods for multi-layered key-value storage are described. For example, methods may include receiving two or more put requests that each include a respective primary key and a corresponding respective value; storing the two or more put requests in a buffer in a first datastore; determining whether the buffer is storing put requests that collectively exceed a threshold; responsive to the determination that the threshold has been exceeded, transmitting a write request to a second datastore, including a subsidiary key and a corresponding data file that includes the respective values of the two or more put requests at respective offsets in the data file; for the two or more put requests, storing respective entries in an index in the first datastore that associate the respective primary keys with the subsidiary key and the respective offsets; and deleting the two or more put requests from the buffer.

Systems and methods are provided for receiving a request for data associated with a particular functionality of an application, identifying a first attribute for which data is to be generated to fulfill the request, and determining that the first attribute corresponds to data to be generated by a first machine learning model. The systems and methods further providing for executing a view or procedure to generate data for input to the first machine learning model, inputting the generated data into the first machine learning model, and receiving output from the first machine learning model. The output is provided in response to the request for data associated with the particular functionality of the application.

A computer system, which is coupled to a plurality of databases configured to store data having different data types, being configured to: manage a catalog database configured to store a catalog including information on an SQL which has been used; execute, in a case of receiving a retrieval request including a first SQL from a user terminal, the first SQL and output an execution result of the first SQL to the user terminal; refer to the catalog database, and select at least one recommended SQL in which a retrieval condition, which is a retrieval condition for retrieving data by joining the plurality of databases, and is similar to the retrieval condition of the first SQL, is defined; and present the at least one recommended SQL to the user terminal.