A generic query having a dynamic query parameter may be executed for a specified process. One or more expected parameters for the query are determined based on the dynamic query parameter and the process, where the one or more expected parameters are tagged to the dynamic query parameter for the process. One or more measured parameters are accessed based on the one or more expected parameters from one or more databases accessible over a network. The one or more measured parameters are returned responsive to the query.

Technologies are described herein for executing queries expressed with reference to a structured query language against unstructured data. A user issues a structured query through a traditional structured data management (“SDM”) application. Upon receiving the structured query, an SDM driver analyzes the structured query and extracts a data structure from the unstructured data, if necessary. The structured query is then converted to an unstructured query based on the extracted data structure. The converted unstructured query may then be executed against the unstructured data. Results from the query are reorganized into structured data utilizing the extracted data structure and are then presented to the user through the SDM application.

Systems, computer-implemented methods, applications, user interfaces, and tangible non-transitory computer readable media for providing triggers in a system of record are disclosed. For example, a computer-implemented method may include maintaining a trigger associated with an application where the trigger comprises a set of conditions and a set of operations associated with a custom computer language that is supported by the application, evaluating the conditions associated with the trigger based on an occurrence of an event associated with the application, determining that the conditions associated with the trigger are satisfied based on the evaluating of the conditions, and executing the operations associated with the custom computer language based on determining that the conditions of the trigger are satisfied. For example, execution of such operations may include performing one or more actions in association with the application and/or one or more third-party applications that are integrated with the application.

The present disclosure relates to systems, methods, and non-transitory computer-readable media for generating pairs of natural language queries and corresponding query-language representations. For example, the disclosed systems can generate a contextual representation of a prior-generated dialogue sequence to compare with logical-form rules. In some implementations, the logical-form rules comprise trigger conditions and corresponding logical-form actions for constructing a logical-form representation of a subsequent dialogue sequence. Based on the comparison to logical-form rules indicating satisfaction of one or more trigger conditions, the disclosed systems can perform logical-form actions to generate a logical-form representation of a subsequent dialogue sequence. In turn, the disclosed systems can apply a natural-language-to-query-language (NL2QL) template to the logical-form representation to generate a natural language query and a corresponding query-language representation for the subsequent dialogue sequence.

Embodiments of the present disclosure describe systems, methods, and computer program products for executing and tuning a machine learning operation within a database. An example method can include receiving a data query referencing an input data set of a database, executing a plurality of machine learning operations to generate, in view of the input data set, a plurality of output data sets each having a respective accuracy value, wherein each of the plurality of machine learning operations is executed by a processing device according to one of a plurality of unique sets of hyperparameters, selecting a first output data set of the plurality of output data sets in view of the accuracy values, and returning the first output data set in response to the data query.

Systems and methods for personally identifiable information metadata governance are disclosed. In one embodiment, a method for personally identifiable information (PII) metadata governance may include: (1) receiving, by a PII metadata identification program executed by an electronic device, a data processing flow for a project; (2) retrieving, by the PII metadata identification program, code for the data processing flow from a code repository; (3) identifying, by the PII metadata identification program, potential PII access points in the code; (4) determining, by the PII metadata identification program, that the potential PII access points match PII access points in a PII reference data database; (5) confirming, by the PII metadata identification program, that an individual assigned to the project is entitled access the PII data; and (6) granting, by the PII metadata identification program, access to the PII data to the individual.

Systems and methods are described for recommending a content item. A search query for a content item is received. The availability of the content item from more than one source is determined. In response to determining that the content item is available from more than one source, the quality of each of the available content items from respective sources is determined. A recommendation factor is determined. The recommendation factor is based on at least one of the bandwidth available to a user device, the resolution capability of the user device, and the quality of experience of each of the sources from which the content item is available. A list of search results for the available content items is generated. The list is ordered based on the quality of each of the available content items from respective sources and the recommendation factor.

A method includes receiving, by a computing device, a first transaction from a user device for a first version of a database record; generating, by the computing device, a bit for the database record; receiving, by the computing device, a second transaction from a second user device for a second version of the database record; locking, by the computing device, the database record; determining, by the computing device, a modification between the first version of the database record and the second version of the database record; and updating, by the computing device, the bit in response to the modification.

Embodiments predict errors using database validation rules. Validation rules can be defined that include business logic for validating transactions performed on a database with a data model. Transactions can be performed using the database, where the database is in a post-transaction state after performance of the transactions. The database can be validated in the post-transaction state by performing the defined business logic for a subset of validation rules, where at least one validation rule fails to validate. Using a trained machine learning model, one or more errors for one or more future transactions can be predicted, the predicted errors being based on the at least one failed validation rule.

Disclosed is a method for estimating database management system performance, in which a performance change ratio of a DBMS can be determined once a first knob group, a second knob group, and a data volume of active data in data managed by the DBMS are obtained, without actually configuring the second knob group in the DBMS, executing a job by the DBMS, and then observing the execution. In other words, the performance change ratio of the DBMS can be estimated without interacting with the DBMS. DBMS security can be ensured, performance measurement approaches are provided for self-tuning and self-management of the DBMS, and reliable and stable running of the DBMS is ensured.