Hewlett-packard Development Company, L.p.

Hewlett Packard Enterprise Development LP

Satish Kumar Mopur

Sridhar Balachandriah

Gunalan Perumal Vijayan

Krishnaprasad Lingadahalli Shastry

Saikat Mukherjee

Ashutosh Agrawal

Gregory S Battas

Vivek Mehrotra

Dinkar Sitaram

Kishore Kumar Muppirala

Ayman Abouelwafa

Karthigeyan Kasthurirengan

Unnikrishnan Ponnan Katangot

Sumanesh Samanta

Krishna Prasad Lingadahalli Shastry

Parthasarathi Ilangovan

Satyaprakash Rao

Mukesh Gupta

Arif Abdulhusein Merchant

Mustafa Uysal

Narayanan Ananthakrishnan Nellayi

Pruthviraj Herur Puttaiah

Aswin Jayaraman

KrishnaPrasad Lingadahalli Shastry

Sakshi Chaitanya Veni

Manasij Nandi

Sijesh T Balakrishnan

Srivatsa Ramaprakash

Sushil Kumar Singh

Duvvuri Rama Kiron

Sathya Prakash Dhanabal

Suman Sathyanarayana Palavalli

Vijetha Vasanth Thanthry

Vijay Kumar

Prasanna Muthukumarasamy

Suresh Ladapuram Soundarajan

Suresh Ladapuram Soundararajan

Systems and methods are provided for implementing a Siamese neural network using improved 'sub' neural networks and loss function. For example, the system can detect a granular change in images using a Siamese Neural Network with Convolutional Autoencoders as the twin sub networks (e.g., Siamese AutoEncoder or “SAE”). In some examples, the loss function may be an adaptive loss function to the SAE network rather than a contrastive loss function, which can help enable smooth control of granularity of change detection across the images. In some examples, an image separation distance value may be calculated to determine the value of change between the image pairs. The image separation distance value may be determined using an Euclidean distance associated with a latent space of an encoder portion of the autoencoder of the neural networks.

Enabling feature importance using Siamese autoencoders for effective image change detection

The present invention relates to a system and a method for updating data models. Input data received from a data source and/or prediction data obtained from a data model is reduced based on baseline reference data to obtain a plurality of representative points. The plurality of representative points are clustered to generate a plurality of clusters. An outlier cluster is detected from the plurality of clusters based on a maximum distance of the plurality of clusters from a highest density cluster and/or comparison of quantity and values of the plurality of representative points with predefined rules. Data drift is identified based on changes in densities of the plurality of clusters. The data model is updated using information corresponding to the outlier cluster and the data drift.

Updating data models to manage data drift and outliers

Bias in Machine Learning (ML) is when an ML algorithm tends to incompletely learn relevant and important patterns from a dataset, or learns the patterns from data incorrectly. Such inaccuracy can cause the algorithm to miss important relationships between patterns and features in data, resulting in inaccurate algorithm predictions. Systems and methods for detecting potential ML bias in input image datasets are described herein. After a target image is received, a subset of images related to the target image is extracted. The target image and subset of images are analyzed under an imbalance assessment and data bias assessment to determine the presence of any potential data bias in a ML training pipeline. If any data bias is determined, one or more messages summarizing the assessments and including explanations to enable more accurate predictions in image assessments are sent to the user.

Image data bias detection with explainability in machine learning

Systems and methods are provided for retraining machine learning (ML) models. Examples may automatically identify skewed, anomalous, and/or drift occurrence data in real-world input data. By automatically identifying such data, examples can reduce subjectivity in ML model retraining as well as reduce time spent determining a need to retrain a ML model. Accordingly, a determination can be made objectively by a computing system or device according to computer-implemented instructions. Additionally, examples may automatically isolate and transfer data relevant to the retraining of a ML model to a training environment for retraining the ML model using real-world input data. Examples also synthesize large samples of data for use in retraining a ML model. The synthesized data may be generated based on the isolated and transferred data and can be used in place of actual real-world input data to reduce a corresponding delay.

Image data synthesis using adversarial autoencoders for continual training of models

The disclosure relates to a framework for dynamic management of analytic functions such as data processors and machine learned ('ML') models for an Internet of Things intelligent edge that addresses management of the lifecycle of the analytic functions from creation to execution, in production. The end user will be seamlessly able to check in an analytic function, version it, deploy it, evaluate model performance and deploy refined versions into the data flows at the edge or core dynamically for existing and new end points. The framework comprises a hypergraph-based model as a foundation, and may use a microservices architecture with the ML infrastructure and models deployed as containerized microservices.

Intelligent lifecycle management of analytic functions for an IOT intelligent edge with a hypergraph-based approach

A system and method for accounting for the impact of concept drift in selecting machine learning training methods to address the identified impact. Pattern recognition is performed on performance metrics of a deployed production model in an Internet-of-Things (IoT) environment to determine the impact that concept drift (data drift) has had on prediction performance. This concurrent analysis is utilized to select one or more approaches for training machine learning models, thereby accounting for the temporal dynamics of concept drift (and its subsequent impact on prediction performance) in a faster and more efficient manner.

Systems and methods for determining machine learning training approaches based on identified impacts of one or more types of concept drift

Intelligent lifecycle management of analytic functions for an IoT intelligent edge with a hypergraph-based approach

The disclosure relates to technology that implements flow control for machine learning on data such as Internet of Things ('IoT') datasets. The system may route outputs of a data splitter function performed on the IoT datasets to a designated target model based on a user specification for routing the outputs. In this manner, the IoT datasets may be dynamically routed to target datasets without reprogramming machine-learning pipelines, which enable rapid training, testing and validation of ML models as well as an ability to concurrently train, validate, and execute ML models.

Intelligent IOT data split management

A method generates input/output (IO) commands by plural different applications that execute on a host. The method prioritizes the applications by inserting different classifiers into the IO commands at a host bus adapter (HBA) located in the host. A storage device receives the IO commands and processes the IO commands according to priorities based on the classifiers for the applications.

Prioritizing input/outputs at a host bus adapter

A method of retrieving data blocks in a storage area network, the method comprising selecting a first one of a plurality of paths to a storage device; retrieving a first number of sequential data blocks from the storage device along the first path; and selecting a second one of the plurality of paths for retrieving subsequent sequential data blocks from the storage device. One or both of the selected paths may be the path with the lowest average turnaround time of all of the available paths.

Data processing method and system

The invention relates to a system and method for prioritizing one or more data processing operations in a computer storage system, the computer storage system including a plurality of modules, the method comprising receiving a command indicating one or more data processing operations to which priority is to be assigned and interfacing with each of the modules so as to prioritize the one or more data processing operations over other data processing operations.

Prioritising data processing operations

Utilization of the processor modules is monitored. A varying load pattern including at least one of a bursty behavior or an oscillatory behavior of the processor modules is identified. Distribution of logical devices between processor modules is performed.

Moving a logical device between processor modules in response to identifying a varying load pattern

A system and method for update of firmware of a storage array controller of a storage device in a storage area network. The I/O workload of the storage device is monitored to obtain workload information regarding I/O traffic of the storage device. The workload information is analyzed to identify patterns in the I/O traffic of the storage device. Based on one or more identified patterns in the I/O traffic, future I/O workload of the storage device is forecasted, and, according to the forecasted future I/O workload, at least one firmware update is scheduled.

System and method for update of firmware of a storage array controller in a storage area network

An embodiment of a method of managing multipathing in a computer system including the steps of establishing a plurality of concurrent multipathing processes on the computer system; disassociating a plurality of operational data paths from a first of the multipathing processes; and associating the operational data paths with a second of the multipathing processes.

Methods and apparatus for managing multipathing software

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Bengaluru, India

Satish Kumar Mopur