Abstract: Disclosed herein is method and system for processing data in serverless computing environment. In an embodiment, information related to functions associated with plurality of serverless computing platforms and the data to be processed are received from data sources. Thereafter, processing operations required for processing the data are determined based on characteristics of data and domain related to data. Further, confidence scores for each function is determined by correlating processing operations and information related to functions. A target function for performing processing operations is identified based on evaluation of confidence scores, and the target function is deployed by forwarding data to serverless computing platform associated with the target function. Finally, the data is processed when an event is triggered for processing the data by target function.

Abstract: A method, corresponding system, and computer program product for edge data processing in an Internet of Things (IoT) environment is disclosed. The method includes receiving, by an IoT Gateway device, a data traffic generated by one or more edge devices in the IoT environment. Thereafter, the IoT Gateway device classifies the received data traffic as one of a normal data and a critical data. In response to classifying the received data traffic, the IoT Gateway device thereafter automatically designates a network location for processing the received data traffic. The network location corresponds to an edge computing resource arranged locally to the IoT Gateway device when the received data is classified as critical data and the network location corresponds to a remote cloud computing resource when the received data is classified as normal data.

Abstract: Disclosed herein is method and system for processing data in serverless computing environment. In an embodiment, information related to functions associated with plurality of serverless computing platforms and the data to be processed are received from data sources. Thereafter, processing operations required for processing the data are determined based on characteristics of data and domain related to data. Further, confidence scores for each function is determined by correlating processing operations and information related to functions. A target function for performing processing operations is identified based on evaluation of confidence scores, and the target function is deployed by forwarding data to serverless computing platform associated with the target function. Finally, the data is processed when an event is triggered for processing the data by target function.

Abstract: This disclosure relates generally to data management, and more particularly to method and system for managing redundant, obsolete, and trivial (ROT) data. In one embodiment, a method for managing ROT documents is disclosed. The method includes receiving a document, and classifying the document into a normal document or a ROT document along with a confidence score using a document classification model. The document classification model may be a domain contextualized machine learning model. The method further includes managing the document according to a document management policy based on the classification and the confidence score.

Abstract: This disclosure relates generally to computer network, and more particularly to a system and method for providing computer network security. In one embodiment, a method is provided for providing computer network security. The method comprises gathering threat information from one or more sources, deriving security intelligence based on the threat information, determining a security measure based on the security intelligence, and dynamically applying the security measure to a computer network using a set of virtual appliances and a set of virtual switches.

Abstract: This disclosure relates to method and system for dynamically handling load on a computer network. In one embodiment, the method includes forecasting recurring load patterns based on a statistical analysis of ongoing network traffic transaction data and historical network traffic transaction data for the computer network, learning a relationship between a peak traffic level and the network traffic transaction data based on an analysis of the historical network traffic transaction data, estimating a current peak traffic level for the ongoing network traffic transaction data based on the relationship, and predicting the upcoming load congestion level by correlating the recurring load patterns and the current peak traffic level. The network traffic transaction data includes at least a network packet parameter, a domain parameter, and a location parameter. The method further includes dynamically handling the upcoming load congestion level by directing an upcoming network traffic to appropriate service instances.

Abstract: A method, corresponding system, and computer program product for edge data processing in an Internet of Things (IoT) environment is disclosed. The method includes receiving, by an IoT Gateway device, a data traffic generated by one or more edge devices in the IoT environment. Thereafter, the IoT Gateway device classifies the received data traffic as one of a normal data and a critical data. In response to classifying the received data traffic, the IoT Gateway device thereafter automatically designates a network location for processing the received data traffic. The network location corresponds to an edge computing resource arranged locally to the IoT Gateway device when the received data is classified as critical data and the network location corresponds to a remote cloud computing resource when the received data is classified as normal data.

Abstract: This disclosure relates generally to computer network, and more particularly to a system and method for providing computer network security. In one embodiment, a method is provided for providing computer network security. The method comprises gathering threat information from one or more sources, deriving security intelligence based on the threat information, determining a security measure based on the security intelligence, and dynamically applying the security measure to a computer network using a set of virtual appliances and a set of virtual switches.