Multidimensional machine learning data and user interface segment tagging engine apparatuses, methods and systems

Multidimensional machine learning data and user interface segment tagging engine apparatuses, methods and systems

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...

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Bibliographische Detailangaben
Hauptverfasser: Neto, Manuel De Araujo Pedreira, Snyder, Jason Alan, Gorman, Stephen Michael, Klau Silverman, Elena, Clark, Michael
Format: Patent
Sprache:eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR
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Zusammenfassung: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. An update to a survey data file is detected. The updated survey data is stored in a SQL database configured to utilize a composite index of the updated survey data that optimizes database query time. A set of affected entity segment identifiers is determined. A set of affected category identifiers is determined. A set of affected cognitive intelligence (CI) datapoint identifiers is determined as CI datapoint identifiers associated with each combination of an affected entity segment identifier and an affected category identifier. Metrics for each allowable response question identifier are calculated and stored in a NoSQL database configured to act as cache for generating visualizations.