Patent ID: 11928124
Assignee: ACCENTURE GLOBAL SOLUTIONS LIMITED
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 9:
10. A method of processing data comprising:
partitioning current data to be uploaded to a data consumer wherein the current data includes current facts data and current dimensions data which includes reference data for the current facts data;
identifying anomalies in each of the partitions of the current data;
identifying multiple model input parameters by aggregating the current data at multiple levels;
obtaining predictions for corresponding variables in the current data from one or more Machine Learning (ML) models for one day forecast from the multiple model input parameters;
comparing the predictions from the one or more ML models with historical data;
determining that at least one of the one or more ML models requires to be retrained based on the comparison;
automatically retraining the at least one ML model;
aggregating the current data into one or more clusters based on corresponding one or more aggregation criteria;
obtaining one or more real-time patterns as outputs from processing the aggregated data by the automatically-retrained at least one ML model;
determining, based on comparisons of the real-time patterns with one or more dynamic thresholds, that quality of the aggregated data permits uploading of the aggregated data to the data consumer,
wherein an upload of the aggregated data includes,
determining that differences exist in the current facts data in comparison with the historical data,
identifying that incremental data including the differences in the current facts data is to be uploaded to the data consumer,
wherein the identification is based on comparisons of the real-time patterns generated by the automatically re-trained ML model with one or more dynamic thresholds;

calculating a number of processing units required for the upload based at least on the incremental data by determining different configurations for different data volumes, wherein each configuration is selected based on a number of processing units determined from a delta factor that represents a percentage of the current data to be processed vs total amount of data in a database including the current data, a load factor ⊖ which is a comparison of all the processing units and base processing units, μfull that forms the number of processing units required for a full load and μbase that represents the number of processing units required if there are no interactions between the current facts data and the current dimensions data; and
uploading the incremental data to the data consumer with the processing units as assigned from the calculation.