A system and method for flight chart element searching is disclosed. A host computing device is configured to convert PDF flight chart files to SVG flight chart files defined in XML, detect flight chart elements in the SVG flight chart files, convert the SVG flight chart files to flight charts defined in aircraft display hardware directives, and combine the flight charts and flight chart element data into a flight chart database. An aircraft computing device is configured to display a flight chart and a GUI using an aircraft display, and highlight a flight chart element in response to a user searching for and selecting a flight chart element name.

A method for performing recursive searching of items of a data structure having a data mode includes creating an instance of a query definition, the instance of the query definition comprising a unique identifier, specifying one or more elements of the query definition, providing the query definition as an input to a query engine. The method further includes the operations of determining, by the query engine, query execution instructions based on the query definition, the query instructions specifying a recursive level-by-level search until a terminal node of the data structure is reached, obtaining results of a query executed based on the query execution instructions; and outputting query results.

Computer implemented methods for storing ad hoc relations between previously unrelated database objects assigned to different database structures using an electronic computing device are presented, the methods including: causing the electronic computing device to define at least three database structures: structure A, structure B and structure C, where each of the at least three database structures each includes a number of objects, where there exists at least one relation between objects of structure A and objects of structure B, and where there exists at least one relation between objects of structure B and objects of structure C; filtering data of structure A; accessing structure B using a first selected relation between structure A and structure B; storing information about filtering of structure A and information on a first selected path between structure A and structure B; filtering results obtained from structure B.

A method and/or system includes: adding a new source of data to be stored in the data storage system; obtaining the schema for the new source of data to be stored in the data storage system; storing the data to be stored in the data storage system in a Not Only Structured Query Language (NOSQL) database in the data storage system; and storing schema for the data to be stored in the data storage system in a metadata store in the electronic data storage system wherein the metadata store is separate from the NOSQL database.

A method and/or system includes: adding a new source of data to be stored in the data storage system; obtaining the schema for the new source of data to be stored in the data storage system; storing the data to be stored in the data storage system in a Not Only Structured Query Language (NOSQL) database in the data storage system; and storing schema for the data to be stored in the data storage system in a metadata store in the electronic data storage system wherein the metadata store is separate from the NOSQL database.

The Multidimensional Machine Learning Data and User Interface Segment Tagging Engine Apparatuses, Methods and Systems (“MLUI”) transforms ambient condition data, sales data, user interface selections, cognitive intelligence question input inputs via MLUI components into project projections, campaigns, user interface visualizations, cognitive intelligence question output outputs. A cognitive intelligence (CI) datapoint identifier cache datastructure is generated, the CI datapoint identifier cache datastructure configured to comprise a category identifier and an entity segment identifier. A CI datapoint value cache datastructure is generated, the CI datapoint value cache datastructure configured to comprise a set of module datastructures, each module datastructure corresponding to a module identifier associated with the category identifier, each module datastructure comprising a set of metric datastructures, each metric datastructure corresponding to a set of calculated metrics. The generated CI datapoint identifier cache datastructure and the generated CI datapoint value cache datastructure are stored as a key-value pair.

The Multidimensional Machine Learning Data and User Interface Segment Tagging Engine Apparatuses, Methods and Systems (“MLUI”) transforms ambient condition data, sales data, user interface selections, cognitive intelligence question input inputs via MLUI components into project projections, campaigns, user interface visualizations, cognitive intelligence question output outputs. A cognitive intelligence (CI) datapoint identifier cache datastructure is generated, the CI datapoint identifier cache datastructure configured to comprise a category identifier and an entity segment identifier. A CI datapoint value cache datastructure is generated, the CI datapoint value cache datastructure configured to comprise a set of module datastructures, each module datastructure corresponding to a module identifier associated with the category identifier, each module datastructure comprising a set of metric datastructures, each metric datastructure corresponding to a set of calculated metrics. The generated CI datapoint identifier cache datastructure and the generated CI datapoint value cache datastructure are stored as a key-value pair.

Systems and methods for embodiments of artificial intelligence systems for identity management are disclosed. Embodiments of the identity management systems disclosed herein may support the creation, association, searching, or visualization of any relevant context to identity management assets for a variety of purposes, including the creation of nested identity management artifacts in a search index and search syntaxes for querying such nested artifacts.

Systems and methods for embodiments of artificial intelligence systems for identity management are disclosed. Embodiments of the identity management systems disclosed herein may support the creation, association, searching, or visualization of any relevant context to identity management assets for a variety of purposes, including the creation of nested identity management artifacts in a search index and search syntaxes for querying such nested artifacts.

Embodiments are directed to a computer implemented method of processing multiple versions of documents. The method includes importing a new version of a document. The method further includes determining that a section of the document can be imported from a previous version of the document. The method further includes creating a table of contents entry for the section and indexing the new version of the document. Another embodiment can be directed to a computer implemented method of implementing searches across multiple versions of a document. After receiving a search query, the search query can be performed across multiple versions of the document. Duplicates can be analyzed and removed. If there are no results from a target version, search results from a prior version of a document are analyzed to find a result from the target version.