Hcl Technologies Limited

Arvind Kumar Maurya

Yogesh Gupta

S U M Prasad Dhanyamraju

Parveen Kumar Jain

Vidhi Bajaj

Subramanya Uma Maheswara Prasad Dhanyamraju

Dhanyamraju S U M Prasad

Gopi Krishna Durbhaka

Sriganesh Sultanpurkar

Karthik Leburi

Asim Prakash Jena

Devendra Brid

Praveen Alwar Pagadala

Ravi Prasad

Akhilesh Kumar Gupta

Mohammad Feroz Khan

Karuna Sharma

Praveen Kumar Jain

The disclosure relates to method and system for intelligently managing time and attendance of a user in an establishment. The method includes recording, by a custom wearable device, a set of evaluation parameters associated with the user; receiving, by an intelligent Access Point Network (IAPN), the set of evaluation parameters from the custom wearable device; computing, by a distance calculator, a distance of the custom wearable device from the IAPN; determining, by an intelligent monitoring subsystem, a valid usage of the custom wearable device based on the set of evaluation parameters using a first trained machine learning model; determining one of a valid presence or a valid movement of the custom wearable device based on the set of evaluation parameters and the distance using a second trained machine learning model; and generating an alert in response to determination of at least one the valid usage or the valid presence.

Intelligent time and attendance management system and method thereof

Disclosed is a system for translating data, extracted from disparate data sources, into a homogeneous dataset to provide meaningful information. The database schema definition module defines a database schema in order to extract meaningful information pertaining to a specific use-case. The data source determination module determines one or more disparate data sources pertinent to extract the meaningful information. The data extraction module extracts heterogeneous dataset from the one or more disparate data sources. The data extraction module further passes the heterogeneous dataset to a Data-Translate Markup Language (DTML) executer to translate the heterogeneous dataset into a homogeneous dataset. The data translation module translates the heterogeneous dataset into the homogeneous dataset by using at least one data adapter. In one aspect, the heterogeneous dataset may be translated to perform data analytics on the homogeneous dataset in order to provide the meaningful information pertaining to the specific use-case.

System for translating data, extracted from disparate datasources, based on a defined metadata and schema

The present disclosure relates to system(s) and method(s) for generating an alert based on change in traffic pattern. The system receives historic traffic data and current traffic data, associated with each road segment, from a set of road segments. Further, the system identifies a change traffic pattern based on analysing the historic traffic pattern and the current traffic pattern, using data analytics and a machine learning algorithm. Furthermore, the system identifies a sub-set of road segments, from the set of road segments, based on comparison of the change in traffic pattern and a pre-defined threshold. The system further determines root cause of change in traffic pattern by analysing the sub-set of road segments. Further, the system generates an alert for updating one or more road segments, from the sub-set of road segments, based on the root cause of change in traffic pattern.

System and method for generating an alert based on change in traffic pattern

The present subject matter discloses a system and method to enable a machine learning based analytics platform. The method may comprise generating a graphical user interface to enable one or more stakeholders to generate and manage a model for predictive analysis. The method may further comprise enabling a business user to define the business problem, and generate models to perform predictive analysis. The method may further comprise deploying the model, in a distributed environment, over a target platform. The method may further comprise monitoring the model to identify at least one error in the model and re-training the model for performing predictive analysis based on the at least one error, thereby enabling the machine learning based analytics platform.

Machine learning based analytics platform

The embodiments herein relate to operational data analysis (ODA) and, more particularly to automate operational data analysis and generate the analysis report for various products using a web-based multi-tenant product intelligence framework. The system allows the user to configure a data collection process, define schema structure, select a data storage for storing the collected data, select or create a data formatting algorithm, and generate a data report to perform the ODA process. Based on the ODA report, appropriate decisions can be taken by an organization.

System and method for automatically analyzing operational data using web-based multi-tenant product intelligence framework

A system and method for providing a unified solution to transmit data from one or more devices/OEMs to one or more storage providers. The system analyzes the metadata of a device, which is sending the request, and identifies an appropriate storage provider for storing the data. Further, the system can convert device specific data format and an API set used for transmitting the data to a storage specific data format and a generic API set. The generic API set can be used to transmit data to one or more storage providers. Additionally, the system supports an extendable interface to initiate a request from the device.

System and method for managing content using generic content management interface

The present disclosure relates to system(s) and method(s) for deploying a data analytics model in a target environment. The system records a set of data pre-processing stages, associated with the data analytics model. The set of data pre-processing stages may comprise receiving raw data, executing a set of ETL functions on the raw data, and executing a set of algorithms on the raw data. Further, the system generates the data analytics model based on the set of algorithms. Furthermore, the system generates a scoring engine workflow, associated with the data analytics model, based on the set of data pre-processing stages. The scoring engine workflow comprises one or more ETL functions and one or more algorithms. Further, the system deploys the data analytics model and the scoring engine workflow in the target environment. The scoring engine workflow enables pre-processing of production data in the target environment.

System and method for deploying a data analytics model in a target environment

The present disclosure relates to system(s) and method(s) for guiding a user in software development lifecycle using machine learning. The system is configured to receive real-time contextual information from a user device and user profile data from a profile database. Further, the system is configured to determine a current issue faced by the current user of the user device. In the next step, the system is configured to enforce a historical solution, from the set of historical solutions, associated with the current issue. If the historical solution is not applicable to address the current issue, the system is configured to extract one or more new solutions, from one or more secondary data sources. Finally, the system is configured to suggest the one or more new solutions to the current user, thereby guiding the current user to address the current issue.

System and method for guiding a user in a software development lifecycle using machine learning

Disclosed are systems and methods for optimizing power consumption of devices. The system includes monitoring module, generating module, matching module, determining module, and identifying module. The monitoring module monitors a device including program code which further includes power consuming functions. The generating module generates plurality of power patterns corresponding to the power consuming functions. The matching module matches the plurality of power patterns with pre-stored plurality of power patterns to identify one or more power patterns having maximum peak value. The determining module determines occurrence of the one or more power patterns for predefined time interval. The identifying module identifies a power consuming function corresponding to a power pattern based on the occurrence. The generating module generates recommendation for the power consuming function by suggesting changes in a code section of the power consuming function.

System and method for optimizing power consumption of one or more devices

A system and method to monitor and control the valves in industrial process control and automation using Near-Field Communication (NFC) is disclosed. The system comprises a NFC-Equipment Interface Unit (NFC-EIU) to acquire parameters from the valves through a port and send the parameters to any handheld device by NFC for analysis. The NFC-EIU on receiving the analysis results from the handheld device can control the valves too.

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Noida, India

Arvind Kumar Maurya