Abstract: Systems and methods for predicting application failures using a hybrid neural network with multi-threaded inputs are provided. A method includes storing, in a first database, information relating to a plurality of digital applications, and storing, in a second database, information relating to historical performance issues associated with the plurality of digital applications. The method may include training the hybrid neural network according to the particulars disclosed herein. The method may also include detecting a trigger event relating to one of the plurality of digital applications, and via the particulars disclosed herein, using the hybrid neural network to output a set of predicted application failures.

Abstract: Systems, computer program products, and methods are described herein for continuous cognitive code logic detection and prediction using machine learning techniques. The present invention is configured to receive, from a user input device, source code scripts and target code scripts for functional code logic components of a full stack, wherein the source code scripts and the target code scripts are associated with one or more tiers; generate a training dataset based on at least the source code scripts, the target code scripts, and the functional code logic components of the full stack; train, using a machine learning algorithm, a machine learning model using the training dataset; determine a prediction accuracy associated with the machine learning model; determine that the prediction accuracy is greater than a predetermined threshold; and deploy the machine learning model on unseen source code scripts.

Abstract: Aspects of the disclosure relate to a data wrapper engine. A computing platform may receive a query comprising a request for data stored as a CLOB. The computing platform may obtain, from a data storage system, the data stored as a CLOB. The computing platform may generate a file wrapper for the data, wherein generating the file wrapper comprises converting the CLOB to a VARCHAR object and storing the VARCHAR object in the file wrapper. The computing platform may generate, using the VARCHAR object stored in the file wrapper, a SQL response to the query. The computing platform may execute the dynamic SQL response to generate a response to the query. The computing platform may send, to a user device, the response to the query and commands directing the user device to display the response to the query, which may cause the user device to display the response.

Abstract: Systems, computer program products, and methods are described herein for continuous cognitive code logic detection and prediction using machine learning techniques. The present invention is configured to receive, from a user input device, source code scripts and target code scripts for functional code logic components of a full stack, wherein the source code scripts and the target code scripts are associated with one or more tiers; generate a training dataset based on at least the source code scripts, the target code scripts, and the functional code logic components of the full stack; train, using a machine learning algorithm, a machine learning model using the training dataset; determine a prediction accuracy associated with the machine learning model; determine that the prediction accuracy is greater than a predetermined threshold; and deploy the machine learning model on unseen source code scripts.

Abstract: Aspects of the disclosure relate to a data wrapper engine. A computing platform may receive a query comprising a request for data stored as a CLOB. The computing platform may obtain, from a data storage system, the data stored as a CLOB. The computing platform may generate a file wrapper for the data, wherein generating the file wrapper comprises converting the CLOB to a VARCHAR object and storing the VARCHAR object in the file wrapper. The computing platform may generate, using the VARCHAR object stored in the file wrapper, a SQL response to the query. The computing platform may execute the dynamic SQL response to generate a response to the query. The computing platform may send, to a user device, the response to the query and commands directing the user device to display the response to the query, which may cause the user device to display the response.