公开(公告)号：US12235795B2

公开(公告)日：2025-02-25

申请号：US17816276

申请日：2022-07-29

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Sagar Venkappa Nyamagouda ,  Smitha Jayaram ,  Hiro Rameshlal Lalwani ,  Rachit Gupta ,  Sherine Jacob ,  Anand Andaneppa Ganjihal

IPC: G06F16/13 ,  G06F11/34

Abstract: In some examples, a system receives workload information of a workload collection, and applies a machine learning model on the workload information, the machine learning model trained using training information including features of different types of workloads. The system produces, by the machine learning model, an identification of a first file system from among different types of file systems, the machine learning model producing an output value corresponding to the first file system that is a candidate for use in storing files of the workload collection.

公开(公告)号：US20240037067A1

公开(公告)日：2024-02-01

申请号：US17816276

申请日：2022-07-29

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Sagar Venkappa Nyamagouda ,  Smitha Jayaram ,  Hiro Rameshlal Lalwani ,  Rachit Gupta ,  Sherine Jacob ,  Anand Andaneppa Ganjihal

IPC: G06F16/13 ,  G06F11/34

CPC classification number: G06F16/13 ,  G06F11/3414

Abstract: In some examples, a system receives workload information of a workload collection, and applies a machine learning model on the workload information, the machine learning model trained using training information including features of different types of workloads. The system produces, by the machine learning model, an identification of a first file system from among different types of file systems, the machine learning model producing an output value corresponding to the first file system that is a candidate for use in storing files of the workload collection.

公开(公告)号：US12086153B1

公开(公告)日：2024-09-10

申请号：US18308093

申请日：2023-04-27

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Kalapriya Kannan ,  Chirag Talreja ,  Chinmay Chaturvedi ,  Sagar Venkappa Nyamagouda ,  Jayasankar Nallasamy ,  Prasad Pimplaskar

IPC: G06F16/25 ,  G06F16/21

CPC classification number: G06F16/254 ,  G06F16/213

Abstract: Systems and methods are provided for generating extract-transform-load (“ETL”) machine learning (“ML”) pipeline validation rules based on user-input, wherein the ETL ML pipeline validation rules may be applicable to validate an ETL ML pipeline against multiple test datasets. The ETL ML pipeline validation rules may comprise compute-type validation rules for computing expected values of data structures within a dataset output by the ETL ML pipeline. The ETL ML pipeline validation rules may comprise check-type validation rules for checking whether data structures within a dataset output by the ETL ML pipeline have intended characteristics. Where the ETL ML pipeline validation rules are applicable to validate an ETL ML pipeline against a test dataset which was not referenced to describe the ETL ML pipeline validation rules, then the ETL ML pipeline may reuse these ETL ML pipeline validation rules to validate the ETL ML pipeline without further user-input.