Abstract: A method for deploying executable programming code, in real-time, leveraging an augmented reality (“AR”) device may be provided. The AR device may be a part of a network of AR devices. Each AR device within the network may be in electronic communication with each other during a duration of time that data displayed is captured, converted, analyzed, code generated and further visualized as a preview in an AR display through the AR device. The method may include capturing, continuously, images of data displayed on a display screen. The method may further include analyzing each captured image in order to identify a captured image comprising a plurality of text associated with computer processing steps for processing a set of documents. In response to the identifying, converting the captured image into a text file and searching to find an optimal function for each step in the set of computer processing steps.

Abstract: A method includes receiving production data items from a data warehouse. New data items are received and converted to structured new data items. The production data items are converted from classical to quantum bits to determine converted production data items. The structured new data items are converted from classical to quantum bits to determine converted structured new data items. The converted production data items are entangled with the converted structured new data items one or more times until a correlation score is greater than a correlation threshold to obtain correlated data items. The correlated data items are converted from quantum to classical bits to determine converted correlated data items. Data dictionary is created for the structured new data items based on the converted correlated data items. Data objects are created for the structured new data items based on the data dictionary. The data objects are deployed to the data warehouse.

Abstract: A method for deploying executable programming code, in real-time, leveraging an augmented reality (“AR”) device may be provided. The AR device may be a part of a network of AR devices. Each AR device within the network may be in electronic communication with each other during a duration of time that data displayed is captured, converted, analyzed, code generated and further visualized as a preview in an AR display through the AR device. The method may include capturing, continuously, images of data displayed on a display screen. The method may further include analyzing each captured image in order to identify a captured image comprising a plurality of text associated with computer processing steps for processing a set of documents. In response to the identifying, converting the captured image into a text file and searching to find an optimal function for each step in the set of computer processing steps.

Abstract: Systems, computer program products, and methods are described herein for implementing intelligent data analysis. The present invention is configured to receive, from a computing device of a user, a first data file, wherein the first data file is associated with a base version of a data source; receive, from the computing device of the user, a second data file, wherein the second data file is associated with an updated version of the data source; initiate an intelligent data analysis engine on the first data file and the second data file; analyze the second data file to determine one or more discrepancies in relation to the first data file; determine one or more discrepancy types associated with the one or more discrepancies; retrieve, from an action datastore, one or more offset actions to rectify the one or more discrepancies; and automatically execute the one or more offset actions.

Abstract: Systems, computer program products, and methods are described herein for implementing intelligent data analysis. The present invention is configured to receive, from a computing device of a user, a first data file, wherein the first data file is associated with a base version of a data source; receive, from the computing device of the user, a second data file, wherein the second data file is associated with an updated version of the data source; initiate an intelligent data analysis engine on the first data file and the second data file; analyze the second data file to determine one or more discrepancies in relation to the first data file; determine one or more discrepancy types associated with the one or more discrepancies; retrieve, from an action datastore, one or more offset actions to rectify the one or more discrepancies; and automatically execute the one or more offset actions.

Abstract: Embodiments of the invention are directed to a system, method, or computer program product for dynamic latency reduction through edge computation based on a multi-layered mechanism. In this regard, the invention is structured for proactive de-centralized monitoring and resolution of network issue events and cross-pollination of resolutions of network issue events between network components and dynamic reallocation of technology resources therebetween for load balancing. In some embodiments, the invention comprises an entity communication network comprising a plurality of network device groups, one or more edge nodes, and a central server system. The invention determines, via an edge node, a first resolution vector for a first network issue event. The invention is configured to implement, via a micro edge adaptor of a first component system, the first resolution vector at a component system.

Abstract: An apparatus includes a memory and a hardware processor. The memory stores a plurality of previous software errors and a plurality of solutions to the plurality of previous software errors. The processor receives a video of a user describing an error in a software application and tracks a facial feature of the user to determine an emotional intensity. The processor converts an audio portion of the video to a text file and analyzes the text file to determine the error. In response to the emotional intensity exceeding a threshold, the processor determines, based on the plurality of previous software errors, a series of steps that, when performed, replicate the error, determines, based on the series of steps and the plurality of solutions, a change to the source code of the application that fixes the error, and implements the change to the software code.

Abstract: An apparatus includes a memory and a hardware processor. The memory stores a plurality of previous software errors and a plurality of solutions to the plurality of previous software errors. The processor receives a video of a user describing an error in a software application and tracks a facial feature of the user to determine an emotional intensity. The processor converts an audio portion of the video to a text file and analyzes the text file to determine the error. In response to the emotional intensity exceeding a threshold, the processor determines, based on the plurality of previous software errors, a series of steps that, when performed, replicate the error, determines, based on the series of steps and the plurality of solutions, a change to the source code of the application that fixes the error, and implements the change to the software code.

Abstract: A code merging system is configured to manage the storage of source code on a local system in the software development cycle. A cloud environment hosts working source code, which is composed of metadata artifact components. Copies of some of the metadata artifact components are transferred to a source code control and merged with master source code in either a partial merge process or a full merge process.

Abstract: Systems, apparatus and methods for intelligent deployment(s) of application objects are provided. The systems, apparatus and methods may include one or more dynamic parameters retrieved from metadata table(s). The parameter(s) may be used to calibrate the deployment(s). The parameter(s) may be associated with previous failed deployment(s). Calibration may be automatic. Calibration may include email sending and/or email previewing components. A testing environment may be used prior to actual deployment.

Abstract: A smart transmission system is configured to upload data from a plurality of sources to a target application hosted in a cloud environment. A server receives a data stream from the plurality of sources. Using a priority algorithm, the server analyzes the data stream to determine which files and accounts are most critical for the target application's function. The files and their associated accounts are partitioned into tranches, and the tranches are uploaded to the target application in order of priority.

Abstract: A smart transmission system is configured to upload data from a plurality of sources to a target application hosted in a cloud environment. A server receives a data stream from the plurality of sources. Using a priority algorithm, the server analyzes the data stream to determine which files and accounts are most critical for the target application's function. The files and their associated accounts are partitioned into tranches, and the tranches are uploaded to the target application in order of priority.

Abstract: A code merging system is configured to manage the storage of source code on a local system in the software development cycle. A cloud environment hosts working source code, which is composed of metadata artifact components. Copies of some of the metadata artifact components are transferred to a source code control and merged with master source code in either a partial merge process or a full merge process.

Abstract: A code merging system is configured to manage the storage of source code on a local system in the software development cycle. A cloud environment hosts working source code, which is composed of metadata artifact components. Copies of some of the metadata artifact components are transferred to a source code control and merged with master source code in either a partial merge process or a full merge process.

Abstract: Systems, apparatus and methods for intelligent deployment(s) of application objects are provided. The systems, apparatus and methods may include one or more dynamic parameters retrieved from metadata table(s). The parameter(s) may be used to calibrate the deployment(s). The parameter(s) may be associated with previous failed deployment(s). Calibration may be automatic. Calibration may include email sending and/or email previewing components. A testing environment may be used prior to actual deployment.

Abstract: Systems, apparatus and methods for intelligent deployment(s) of application objects are provided. The systems, apparatus and methods may include one or more dynamic parameters retrieved from metadata table(s). The parameter(s) may be used to calibrate the deployment(s). The parameter(s) may be associated with previous failed deployment(s). Calibration may be automatic. Calibration may include email sending and/or email previewing components. A testing environment may be used prior to actual deployment.

Abstract: Systems, apparatus and methods for intelligent deployment(s) of application objects are provided. The systems, apparatus and methods may include one or more dynamic parameters retrieved from metadata table(s). The parameter(s) may be used to calibrate the deployment(s). The parameter(s) may be associated with previous failed deployment(s). Calibration may be automatic. Calibration may include email sending and/or email previewing components. A testing environment may be used prior to actual deployment.

Abstract: A method comprises receiving a first request from a source, the first request comprising an identification of a first person, a source of the first request, and an indicator whether the first person is currently available for communication. The method further comprises assigning a weight to each of the identification of the person, the source, and the indicator whether the first person is currently available for communication, wherein the assigned weights are based on historical data about the first person and summing each of the assigned weights to produce a prioritization factor. The method further comprises sending the first request to a first telephone dialer if the prioritization factor for the first request is above or equal to a threshold and sending the first request to a second telephone dialer if the prioritization factor for the first request is below the threshold.