Abstract: Disclosed is Extensible Messaging and Presence Protocol (XMPP) server for sharing and controlling electronic devices located at remote locations. XMPP server receives request notification from client device for establishing communication with host device. XMPP server authenticates client device and host device using XMPP credentials and sends request notification to host device and receive an acceptance notification from the host device for establishing communication with the client device. XMPP server establishes bidirectional secured communication channel between the client device and the host device using an XMPP protocol or a network protocol, over a secured layer protocol and facilitates communication between the client device and the host device over the bidirectional secured communication channel. Client device and host device are connected under secured network and restricted to access each other through firewall restricted boundaries.

Abstract: The present disclosure relates to system(s) and method(s) for generating test data for testing an Internet of Things (IOT) network. Initially, the system is configured for receiving sensor ontology data of at least one sensor to be simulated for testing an Internet of things (IOT) network. The sensor ontology data may include a range of operation of the sensor and a frequency of operation of the sensor. Further, the system is configured for accepting a set of test scenarios for testing the IOT network. Furthermore, the system is configured for generating master test data for testing the IOT network, wherein the master test data comprises a set of test packages corresponding to the set of test scenario and the sensor ontology data.

Abstract: Systems and methods for allocating optimal supply chain resources based on order data are disclosed. In some embodiments, a disclosed method includes: receiving, from a computing device, an allocation request associated with an order; determining a customer node associated with the order, wherein the customer node is in a supply chain network; determining, based on the order and the customer node, a sub-network of the supply chain network; determining, based on cost information associated with the sub-network, at least one path from at least one source node to the customer node in the sub-network; generating supply chain allocation data for the order based on the at least one path; and transmitting the supply chain allocation data to the computing device.

Abstract: Disclosed is a method and system for provisioning a device over an Internet of Things (IoT) platform for establishing a bidirectional communication between the device and a system (IoT server). The system may identify a device based on device identification (ID) of the device. The system may further determine whether the device is communicating with the system for the first time or has previously communicated with the system. The system may automatically generate a username and a password for the device for authenticating the user. Post generating the username and the password, the system may transmit the username and the password to the device and an external device. Further, the external device may provide a connection between the device and other devices connected over the IoT based on the username and the password. The device may also create a buddy list by storing the usernames of the other devices.

Abstract: 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.

Abstract: 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.

Abstract: The present subject matter discloses system 102 and method to enable machine learning based analytics platform. The method may comprise generating a graphical user interface to enable one or more stake holders 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 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 enable machine learning based analytics platform.

Abstract: 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.