公开(公告)号：US20200349112A1

公开(公告)日：2020-11-05

申请号：US16856706

申请日：2020-04-23

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Anubhav Apurva ,  Jayasankar Nallasamy

IPC: G06F16/16 ,  G06N20/00

Abstract: Examples include generating log line signatures for log lines of a plurality of log files, converting log line signatures to dimensional vectors, generating log file vectors and identifying one or more subsystems associated with each log file, based on a log file vector associated with the corresponding log file and a classification model.

公开(公告)号：US20230412449A1

公开(公告)日：2023-12-21

申请号：US17836551

申请日：2022-06-09

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Kalapriya Kannan ,  Jayasankar Nallasamy ,  Chirag Talreja ,  Pruthvi Raju ,  Rohini Raghuwanshi

IPC: H04L41/0604 ,  H04L41/16 ,  H04L41/069 ,  H04L41/0893

CPC classification number: H04L41/0618 ,  H04L41/16 ,  H04L41/0893 ,  H04L41/069 ,  H04L41/0613

Abstract: Network alert detection utilizing trained edge classification models is described. An example of a computing system includes a processor and a memory storing instructions that cause the processor to train one or more classification models at a core for detection of signatures based on training data derived from a set of error codes; deploy the one or more trained classification models at an edge of a network; receive alerts from one or more nodes in one or more clusters of nodes in the network; detect one or more signatures by processing the received alerts at the one or more trained classification models; and perform one or more actions to address a signature that is detected by the one or more trained classification models.

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.