Dell Products Inc.

Saheli Saha

Ramakanth Kanagovi

Guhesh Swaminathan

Prerit Jain

Sankunny Jayaprasad

Jason Fay

Erik Reyes

Araiz Abdul Baqi

Meenakshi Parameswaran

Srivats Athindran

Prakash Sridharan

Sunil A Vyas

Kevin P Olalde

Geoffrey S Meyer

Virender Sharma

A method for managing an anomaly in a client includes: obtaining, by an analyzer, historical metadata (HM); obtaining, by the analyzer, an error description that is associated with the HM; analyzing, by the analyzer, the HM to generate a first data frame (DF); generating, by the analyzer, a second DF and a third DF based on the first DF, in which the second DF and the third DF are sent to an engine; generating, by the analyzer, a fourth DF based on the first DF and error description, in which the fourth DF is sent to the engine; tuning, by the engine, an anomaly detection model (ADM) to obtain a tuned ADM using: a first target parameter (TP) and the second DF; a second TP and the third DF; a third TP and the fourth DF; and initiating, by the engine, notification of an administrator about the tuned ADM.

Method and system for detecting anomalous sub-sequences in metadata

Method and system for detecting anomalous sub- sequences in metadata using rolling windows

An apparatus comprises a processing device configured to receive a request to generate a synthesized document comprising one or more search terms, and to extract, utilizing a first machine learning model, keywords from a set of documents. The processing device is also configured to select first content for inclusion in a first section of the synthesized document based on a similarity of the search terms and the extracted keywords from corresponding first sections of the set of documents, and to determine, utilizing a second machine learning model that takes as input the selected first content, a set of terms for a second section of the synthesized document. The processing device is further configured to select second content for inclusion in the second section of the synthesized document based on a similarity of the determined set of terms and the extracted keywords from corresponding sections of the set of documents.

Machine learning-based generation of synthesized documents

One example method includes acquiring data from a knowledge base that includes message codes indicating conditions that occurred during performance of one or more test runs of a computing operation, and the message codes are included in message code sequences, processing the data by mapping the message codes, and message codes included in a customer issue log, to codes that are readable by a machine learning process, transforming the data to generate an output that comprises, for each message code sequence, relationships between each of the message codes in that message code sequence, extracting features from the transformed data, and the extracting generates multiple datasets that include the features, performing a similarity comparison by comparing a customer field issue with the datasets, and based on the similarity comparison, identifying and recommending a solution to the customer field issue.

Machine learning method to rediscover failure scenario by comparing customer's server incident logs with internal test case logs

An information handling system instantiates a hardware configuration generator to generate platform configurations. The hardware configuration generator provides a ranked list of the platform configurations, receives platform customer service request information, provides a ranked list of part numbers associated with the platform configurations based upon the customer service request information, and modifies the ranked list of platform configurations based upon the ranked list of part numbers.

Utilizing customer service incidents to rank server system under test configurations based on component priority

Techniques described herein relate to a method for performing testing operations for information handling systems. The method includes obtaining a test case from an information handling system; in response to obtaining the test case: obtaining log information associated with the test case from the information handling system; performing data preparation to generate processed subsequences using the log information; applying a plurality of prediction models to the processed subsequences and training data to generate anomalous subsequence predictions; generating ensemble anomaly scores and severity indexes associated with the processed subsequences using the anomalous subsequence predictions and the processed subsequences; making a determination that the ensemble anomaly scores and severity indexes associated with the processed subsequences result in detection of an anomalous subsequence; and in response to the determination: determining a next best test case associated with the anomalous subsequence; and initiating performance of the next best test case.

Method and system for performing testing operations for information handling systems

A system, method, and computer-readable medium are provided that engages in a data-driven and machine learning-based approach to arrive at high-value, system under test configurations for validation. Embodiments determine all the possible configurations for a computer platform, considering the variety of processors, boards, adapters, and the like, and then utilize a pseudo-ensemble clustering methodology that combines a k-means clustering technique with a neural-network based Kohenon self-organizing map competitive clustering technique to associate like configurations, and then utilizes a data-driven scoring methodology on the clustered configurations to prioritize those configurations to be validation tested.

Growing community of inventors

Bengaluru, India

Saheli Saha