A method includes receiving, in a first query interface, a query composed by the user by typing commands into a query box of the first query interface and based on the receiving of the query, causing events corresponding to query results of the query to be displayed in the first query interface with fields corresponding to the events. Based on the selection by the user of an option, a second query interface is displayed with a table that includes events that correspond to query results of a loaded query. The table includes columns corresponding to event attributes, rows corresponding to events. Cells are populated with the data items of event attributes, where one of the columns corresponds to a field of the fields displayed in the first query interface. The table also includes interactive regions selectable by the user to add one or more commands to the loaded query.

A system is provided that includes a document validation within a non-relational database system that is capable of reviewing documents according to one or more validation rules. The document validator may be incorporated within a non-relational database engine and may perform validation operations when one or more database functions are performed.

This disclosure relates to systems and methods for verifying the presentation of content to a target audience using generated metrics indicative of a likelihood that the content was presented to actual human individuals within the target audience. In some instances, such a metric may be associated with a probability model estimating that a user (e.g., a user of a device) is human and not a bot and/or other automated service. Metrics consistent with aspects of the disclosed embodiments may be generated based, at least in part, on user information received from a user and/or associated devices and/or associated services. Consistent with various disclosed embodiments, metrics indicative of whether a user is human, content distribution decisions and user agency decisions may use such metrics.

The present invention provides a system, method and software for digitizing and automating an auditing process. The system comprises a computer, a regulations database, a storage database, and a software program. The method includes: providing regulations or standards for a user to select from the regulations database as a focus for an audit; providing a user-selected filtered regulation or standard from the regulations database; presenting the user a plurality of audit questions in the form of an audit row, one audit row at a time; receiving user responses from which at least one standard row item is created and/or updated; and sending one or more of a filtered regulation or standard row item to the storage database as an audit. The system may permit user information updates. After an audit is completed, the system may transfer the audit information into a final report, which may be provided to the user.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating automatic suggestions to effectively modify the organization of an ingested data collection without destruction of the underlying raw data. In particular, in one or more embodiments, the disclosed systems utilize multiple machine learning models in sequence to determine likelihoods that the organizational structure of an ingested data collection should be modified in various ways. In response to generating these likelihoods, the disclosed systems generate corresponding automatic suggestions to modify the organization of the ingested data collection. In response to a detected selection of one or more of the automatic suggestions, the disclosed systems read data out of the ingested data collection in accordance with the selected automatic suggestions to effectively modify the organization of the ingested data collection.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating derived dimensions within ingested data collections. In particular, in one or more embodiments, the disclosed systems generate derived dimensions that modify or correct one or more organizational schemas of an ingested data collection when querying the ingested data collection. For example, the disclosed systems receive a user-defined definition, and generates a fallback expression based on the user-defined definition and aligned to the schema of a particular ingested data collection. In at least one embodiment, the disclosed systems generate the derived dimension by identifying and modifying one or more existing dimensions in the schema based on the fallback expression when reading data from the ingested data collection.

An action framework system provides context-based actions in association with a source application. The source application detects selection of a content element presented by the source application. The content element is associated with an application identifier identifying a target application and an action identifier identifying an operation to be executed by the target application on the content element. The target application identified by the application identifier is executed to perform the operation identified by the action identifier on the content element, responsive to detection of the selection of the content element. In one implementation, the source application is a search feature providing different contexts of search results (e.g., from a local file system search, a local application content search, a Web search, a remote application content search).

An engine for resolving a query from a user to provide a dynamic actionable dashboard in a business operating system includes an MLET database, a data interface, a logic configured to process incoming queries, fetch data in relation to those queries, and render an actionable dashboard having data resulting from the queries. The MLET database comprises a plurality of templates (“MLETs”), each MLET being associated with a unique identifier and including a mechanism for accessing data relating to that identifier. The logic processes queries into constructs having a tokens and configurable inputs. If the query includes a unique identifier associated with an MLET in the MLET database, the MLET is used to fetch data responding to the query. If the query includes a unique identifier not associated with an MLET in the MLET database, the logic creates a new MLET using operational intelligence and stores it in the MLET database.

An automated data entry system comprising target database servers for storing target database(s), a processing server configured to execute a client-application and configured to store a plurality of data objects including a document schema and a target database schema corresponding to the target database(s), and a client terminal connected to the processing server, configured to: generate a first graphical interface for connection to a target database, and to execute a second graphical interface for connection to the client-application. The processing server may also receive an electronic data file representing an unprocessed document, to associate the unprocessed document with a document schema and extract a data variable based on the document schema, and to generate a database operation comprising the data variable configured according to a target database schema. The client terminal is configured to receive the database operation and execute the database server database operation against a target database.

A filter request for a data set indicates a query specification and one or more sub-range indicators of a disjoint subset descriptor. Corresponding the individual data records obtained from the data set using the query specification, a respective range mapping value is generated. Using the range mapping values and the sub-range indicators, a result set of the filter request is obtained.