Hewlett Packard Enterprise Development LP

Hewlett-Packard Development Company, L.p.

Gregory S Battas

Satish Kumar Mopur

Sridhar Balachandriah

Gunalan Perumal Vijayan

Ashutosh Agrawal

Saikat Mukherjee

Krishnaprasad Lingadahalli Shastry

Bahman Zargham

Awny Kayed Al-Omari

Kashif Abdullah Siddiqui

QiFan Chen

Michael James Hanlon

KrishnaPrasad Lingadahalli Shastry

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 for creating a joined data set from a join input data set is disclosed. The method starts by categorizing the join input data set into a high-skew data set and a low-skew data set. The low-skew data set is distributed to the plurality of CPUs using a first distribution method. The high-skew data set is distributed to the plurality of CPUs using a second distribution method. The plurality of CPUs process the high-skew data set and the low-skew data set to create the joined data set.

Data skew insensitive parallel join scheme

Solutions for reducing latencies in enterprise operations and business processes adaptation particularly as related to enriched publish and subscribe are proposed by the present invention. These solutions are implemented in a zero latency enterprise (ZLE) framework that allows the enterprise to integrate its services, applications and data in real time. Namely, an enterprise equipped to run as a ZLE is capable of integrating, in real time, its enterprise-wide data, applications, business transactions, operations and values. An operational data store operates as an information broker between the applications such that applications publish messages to the central repository and subscribe to messages from the central, rather than exchange request-response messages directly with each other. Thus, enriched publish and subscribe improves information synchronization between applications and reduce the number of request-response messages the applications would otherwise exchange. Consequently, an enterprise conducting its business as a ZLE exhibits superior management of its resources, operations and customer care.

Zero latency enterprise enriched publish/subscribe

A framework, architecture, system and method are provided for reducing business transaction latencies thereby enabling an enterprise to run as a zero latency enterprise (ZLE). The present invention enables the enterprise to integrate its services, applications and data in real time. An enterprise running as a ZLE has a uniform global view of its past and current business transactions, including its customer interactions, internal business operations, supply chain management and more; and this uniform global view can be obtained from anywhere across the enterprise and be used for real-time decision making. Namely, an enterprise equipped to run as a ZLE is capable of integrating, in real time, its enterprise-wide data, applications, business transactions, operations and values. Consequently, an enterprise conducting its business as a ZLE exhibits superior management of its resources, operations and customer care.

Framework, architecture, method and system for reducing latency of business operations of an enterprise

Methods are provided for enabling an enterprise to run as a zero latency enterprise (ZLE) and for running an enterprise as a ZLE. Such methods allow the enterprise to integrate its services, applications and data in real time. Namely, an enterprise equipped to run as a ZLE is capable of integrating, in real time, its enterprise-wide data, applications, business transactions, operations and values. Consequently, an enterprise conducting its business as a ZLE exhibits superior management of its resources, operations and customer care.

Growing community of inventors

Fishers, IN, United States of America

Gregory S Battas