Abstract: Embodiments of the invention are directed to a system, method, and computer program product for processing data analytics within memory. The system first receives a request for data associated with a database. The system utilizes a feature catalog and a deep learning neural network to determine the data that comprises the one or more desired features. The system can also provide the requested data to a computing device.

Abstract: Aspects of the disclosure relate to outage prevention. A computing platform may train, using historical parameter information and historical outage information, an outage prediction model. The computing platform may receive, from at least one system, current parameter information, and may normalize the current parameter information. The computing platform may convert, using a CNN of the outage prediction model, the normalized current parameter information to a frequency domain. The computing platform may input, into at least one RNN of the outage prediction model, the frequency domain information, to produce a likelihood of outage score. The computing platform may compare the likelihood of outage score to a predetermined outage threshold. Based on identifying that the likelihood of outage score meets or exceeds the predetermined outage threshold, the computing platform may direct the at least one system to execute a performance modification to prevent a predicted outage.

Abstract: Embodiments of the invention are directed to a system, method, and computer program product for processing data analytics within memory. The system first receives a request for data associated with a database. The system utilizes a feature catalog and a deep learning neural network to determine the data that comprises the one or more desired features. The system can also provide the requested data to a computing device.

Abstract: Embodiments of the invention are directed to a system, method, and computer program product for processing data analytics within memory. The system first receives a request for data associated with a database. The system utilizes a feature catalog and a deep learning neural network to determine the data that comprises the one or more desired features. The system can also provide the requested data to a computing device.

Abstract: A technical architecture system comprises a memory, an interface, and a processor. The system stores a plurality of risk areas, receives a request to send data to a third-party entity and retrieves architecture information associated with an architecture of the third-party entity. The architecture information corresponds to at least one of the plurality of risk areas. The system also determines a risk rating of the architecture for at least one of the plurality of risk areas. The risk rating is based on the architecture information. The system may further determine a weight based on the at least one of the plurality of risk areas and determine an area score based on the risk rating and the weight. Finally, the system determines whether to grant permission to send the data to the third-party entity based at least in part on the area score.