Abstract: Data analysis plays a crucial role to get significant information out of the data. A real time system and method for analyzing data streams have been provided. The system can utilize many different types of data formats such as numeric, text, video, audio, image, or combination thereof. The analysis takes place as per the requirement using an analytical engine and an intermediate output is generated. The intermediate output is further processed using a distributed real time business rule processing engine to determine required conditions in the data. The business rules comprise one or more set of meta data. On match of the business rule, the system triggers an alert or propagates the required information to integrating solution for required actions. The system and method are technology and communication protocol agnostic, and designed with highly efficient load balanced technique, thereby facilitating highly concurrent data processing with minimal latency.

Abstract: Various methods, apparatuses/systems, and media for automatically detecting discrepancy between preset data and input data are provided. A processor implements a platform and language agnostic smart data discrepancy detection module that automatically detects discrepancy between preset data and input data (i.e., larger than the preset data) during data processing of a commercial transaction involving a gratuity, particularly when a credit card payment is being made. The processor also implements an optimized processes of conducting a commercial transaction that includes a gratuity data based on user preferences that are obtainable from an application during execution thereof, and automatically alerts the user when data discrepancy is detected, i.e., when the gratuity data is more than preset gratuity data set by the user on the application.

Abstract: Various methods, apparatuses/systems, and media for automatically upgrading an application are disclosed. A processor creates a dynamic machine learning (ML) model; trains the dynamic ML model and scans for SDK upgrade for the application against the dynamic ML model by implementing ML algorithm for predictions. The processor executes the SDK upgrade in response to detecting that the training of the dynamic ML model is completed to trigger the processor to perform the following automated processes: implement the ML algorithm against the trained dynamic ML model to generate predictive results data for deprecated reference corresponding to the application; evaluate the predictive results data to determine whether there is a match for the deprecated reference; and when it is determined that there is a match for the deprecated reference, automatically replace code and upgrade the application to newer version of the programming language specification.

Abstract: A method for facilitating automated application management based on activity is disclosed. The method includes monitoring metrics that correspond to an application, the metrics including a network traffic metric; determining, for the application, whether the metrics satisfy a predetermined threshold, the predetermined threshold including an idle time threshold; deactivating the application when the corresponding metrics satisfy the predetermined threshold; receiving, from a client application, a request for the deactivated application; initiating a health check for the application in response to the request; and activating the application based on a result of the health check.

Abstract: Various methods, apparatuses/systems, and media for automatically upgrading an application are disclosed. A processor creates a dynamic machine learning (ML) model; trains the dynamic ML model and scans for SDK upgrade for the application against the dynamic ML model by implementing ML algorithm for predictions. The processor executes the SDK upgrade in response to detecting that the training of the dynamic ML model is completed to trigger the processor to perform the following automated processes: implement the ML algorithm against the trained dynamic ML model to generate predictive results data for deprecated reference corresponding to the application; evaluate the predictive results data to determine whether there is a match for the deprecated reference; and when it is determined that there is a match for the deprecated reference, automatically replace code and upgrade the application to newer version of the programming language specification.

Abstract: Various methods, apparatuses/systems, and media for automatically upgrading an application are disclosed. A processor creates a dynamic machine learning (ML) model; trains the dynamic ML model and scans for SDK upgrade for the application against the dynamic ML model by implementing ML algorithm for predictions. The processor executes the SDK upgrade in response to detecting that the training of the dynamic ML model is completed to trigger the processor to perform the following automated processes: implement the ML algorithm against the trained dynamic ML model to generate predictive results data for deprecated reference corresponding to the application; evaluate the predictive results data to determine whether there is a match for the deprecated reference; and when it is determined that there is a match for the deprecated reference, automatically replace code and upgrade the application to newer version of the programming language specification.

Abstract: Various methods, apparatuses/systems, and media for automatically fixing health of a certain component of an application are disclosed. A processor sets a corresponding predetermined threshold value for health of each component of a plurality of components of an application required for running the application; constantly monitors health of the plurality of components. When it is determined, in response to constantly monitoring, that the health of a certain component has reached to a value that is equal to the corresponding predetermined threshold value of the certain component, the processor automatically calls corresponding application programming interface (API) to trigger automated processes that includes collecting all necessary data corresponding to fixing of the health of the certain component related to the application from a plurality of data sources; creating a data file on demand that includes the necessary data; and fixing the health of the certain component based on the data file.