Accenture Global Solutions Limited

Oracle International Corporation

Vmware, Inc.

Ravi Ranjan

Rohit Jain

Vijay H Gurdasani

Sumit Kumar Ghosh

Neelam Ved

Rajnee Maheshwari

Ravi Kumar Reddy Kottapalli

Sameer Arun Joshi

Sudarshana Kandachar Sridhara Rao

Samarjeet Singh Tomar

Harendra Kumar Mishra

Shrihari Amarendra Bhat

Venkata Swamy Babu Budumuru

Methods, apparatus, systems, and articles of manufacture are disclosed to dynamically monitor and control compute device identities during operations. Disclosed is an apparatus comprising interface circuitry, machine readable instructions, and processor circuitry to at least one of instantiate or execute the machine readable instructions to generate a unique label for a node from a data plane, the unique label to identify the node, perform an operation on the node, the operation to be performed on the node by identifying the node associated with the unique label, and maintain the unique label until the operation on the node is successful.

Methods and apparatus to dynamically monitor and control compute device identities during operations

'Semi-supervised' machine learning relies on less human input than a supervised algorithm to train a machine learning algorithm to perform entity recognition (NER). Starting with a known entity value or known pattern value for a specific entity type, phrases in a training data corpus are identified that include the known entity value. Context-value patterns are generated to match selected phrases that include the known entity value. One or more context-value patterns may be validated based on human input. The validated patterns identify additional entity values. A subset of the additional entity values may also be validated based on human input. Occurrences of validated entity values may be labeled in the training corpus. Sample phrases from the labeled training dataset may be extracted to form a reduced-size training set for a supervised machine learning model which may be further used in production to label data for any named entity recognition application.

System and method to generate a labeled dataset for training an entity detection system

A device may select an enterprise content management (ECM) system for monitoring. The ECM system may include a set of subsystems. The device may monitor the set of subsystems of the ECM system based on selecting the ECM system for monitoring. The device may perform a set of health checks on the set of subsystems based on monitoring the set of subsystems. The set of health checks may include a validation of a set of network addresses associated with the set of subsystems, an analysis of a set of log files associated with the set of subsystems, and a determination of a set of metrics associated with the set of subsystems. The device may generate a health check report based on the set of health checks. The device may provide the health check report.

System monitoring device

Growing community of inventors

Bengaluru, India

Ravi Ranjan