Patent ID: 11954129
Assignee: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 15:
16. A cloud server comprising:
one or more processors; and
one or more memories coupled with the one or more processors, the one or more memories storing programmed instructions, which when executed by the one or more processors, causes the one or more processors to:
receive information corresponding to an outlier cluster and data drift;
generate a machine learning algorithm using the information corresponding to the outlier cluster and the data drift;
apply the machine learning algorithm to train a data model; and
update baseline reference data based on clustering of updated prediction data, using at least one data clustering technique, wherein the updated prediction data is obtained from the trained data model,
wherein the information corresponding to the outlier cluster and the data drift is received from an edge system configured to:
process input data, based on baseline reference data, to obtain a plurality of representative points, wherein the input data comprises at least one of data received from a data source and prediction data obtained from a data model, and wherein the plurality of representative points correspond to segments of the input data derived using a predefined segment size;
cluster the plurality of representative points to generate a plurality of clusters using a first data clustering technique, wherein each cluster among the plurality of clusters comprises at least one representative point from the plurality of representative points;
modify the predefined segment size when deviations are identified between multiple sets of clusters of the plurality of representative points prepared using different data clustering techniques;
detect the outlier cluster from the plurality of clusters based on at least one of a maximum distance of the plurality of clusters from a highest density cluster and comparison of quantity and values of the plurality of representative points with predefined rules, wherein a histogram algorithm is used to determine densities corresponding to the plurality of clusters; and
identify the data drift based on changes in densities of the plurality of clusters over a predefined period of time.