Embodiments use successive refinement to allow a user to systematically explore the result set of an arbitrary query over RDF, such as a SPARQL query. A user inputs an arbitrary base query and modifies this query by replacing selected variables with values to which each selected variable is bound within the result set of the base query. Embodiments present, via a GUI, variable facets that may be substituted for query variables. Embodiments also present, through a GUI, a query history graph that represents query versions that a user has created. A user may navigate this query history graph to return to previously-created query versions. The GUI also provides information about the facets, including a number of results that would be included in the result set of the query version resulting from substitution of the facet for the associated variable.

Embodiments use successive refinement to allow a user to systematically explore the result set of an arbitrary query over RDF, such as a SPARQL query. A user inputs an arbitrary base query and modifies this query by replacing selected variables with values to which each selected variable is bound within the result set of the base query. Embodiments present, via a GUI, variable facets that may be substituted for query variables. Embodiments also present, through a GUI, a query history graph that represents query versions that a user has created. A user may navigate this query history graph to return to previously-created query versions. The GUI also provides information about the facets, including a number of results that would be included in the result set of the query version resulting from substitution of the facet for the associated variable.

An automated command assistance tool is provided for a browser-enabled command line interface of a cloud service. The automated command assistance tool provides examples illustrating the correct syntax for commands used to manage the resources of a cloud service. The command assistance tool learns the syntax of a command from usage patterns found in telemetric data, scripts and user documentation and forms templates containing a command's usage pattern and related information. The templates are used to generate examples that respond to a user query for assistance with usage of a particular command.

An automated command assistance tool is provided for a browser-enabled command line interface of a cloud service. The automated command assistance tool provides examples illustrating the correct syntax for commands used to manage the resources of a cloud service. The command assistance tool learns the syntax of a command from usage patterns found in telemetric data, scripts and user documentation and forms templates containing a command's usage pattern and related information. The templates are used to generate examples that respond to a user query for assistance with usage of a particular command.

Embodiments of the present disclosure provide methods, apparatus, systems, computer program products for transferring a performance of a procedure found in technical documentation for an item via an interactive electronic technical manual system (IETM) configured to provide electronic and credentialed access to the technical documentation. In one embodiment, a method is provided comprising: providing the steps of the procedure in an order in which the steps are to be carried out; and while a user is participating in the performance of the procedure: causing a particular step that is being carried out to be highlighted; receiving input of a selection of a transfer mechanism and in response: causing an indication to be displayed between the particular step and a next step to be carried out identifying where the performance has been suspended; providing a transfer window displaying transfer information; and recording the transfer information and an identifier for the indication.

Embodiments of the present disclosure provide methods, apparatus, systems, computer program products for transferring a performance of a procedure found in technical documentation for an item via an interactive electronic technical manual system (IETM) configured to provide electronic and credentialed access to the technical documentation. In one embodiment, a method is provided comprising: providing the steps of the procedure in an order in which the steps are to be carried out; and while a user is participating in the performance of the procedure: causing a particular step that is being carried out to be highlighted; receiving input of a selection of a transfer mechanism and in response: causing an indication to be displayed between the particular step and a next step to be carried out identifying where the performance has been suspended; providing a transfer window displaying transfer information; and recording the transfer information and an identifier for the indication.

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 category identifier selection is obtained via a category selection interaction interface mechanism. Entity segment identifier selections are obtained via entity segment selection interaction interface mechanisms. A set of visualization cognitive intelligence (CI) datapoint identifiers is determined as CI datapoint identifiers associated with each combination of a selected entity segment identifier and the selected category identifier. CI datapoint values corresponding to the set of visualization CI datapoint identifiers are retrieved from a NoSQL database configured to act as cache for generating visualizations based on metrics calculated using survey data. A visualization is generated using the retrieved CI datapoint values.

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 category identifier selection is obtained via a category selection interaction interface mechanism. Entity segment identifier selections are obtained via entity segment selection interaction interface mechanisms. A set of visualization cognitive intelligence (CI) datapoint identifiers is determined as CI datapoint identifiers associated with each combination of a selected entity segment identifier and the selected category identifier. CI datapoint values corresponding to the set of visualization CI datapoint identifiers are retrieved from a NoSQL database configured to act as cache for generating visualizations based on metrics calculated using survey data. A visualization is generated using the retrieved CI datapoint values.

Systems and methods are disclosed herein for processing one or more document and/or hierarchal project. For example, a system is disclosed having memory storing the document as a data file comprising content data. The system also includes at least one processor coupled to the at least one memory and configured to designate a portion of the content data as micro content; receive user input providing a phrase; and generate a phrase map configured to associate the phrase with the designated micro content and store the phrase map as phrase map data, the phrase map data comprising at least the phrase associated with a reference indicative of the associated micro content.

An example system includes a processor to receive deltas corresponding to concurrently executed operations of a number of other replicas on an element of a delta-based conflict free replicated data type (CRDT) array of a distributed computing system. The processor is to modify, at the first replica, the element of the delta-based CRDT array based on the deltas using a dot store that maps dots to a dot store.