Abstract: A method for creating a graph database implemented knowledge mesh is disclosed. The method includes receiving, by a computer system, data from a plurality of different streams and identifying, by the computer system executing at least one machine learning model, a plurality of triples included in the data. The method also comprises filtering, by the computer system, the plurality of triples to identify a relevant subset of triples by applying an ontological filter, applying, by the computer system, a disambiguation routine to the relevant subset of triples to correlate entities included in the relevant subset to other entities in a graph database and determine a degree of confidence with each correlation, and creating, by the computer system, a knowledge mesh using the graph database. The knowledge mesh comprises the relevant subset of triples, each correlation identified by application of the disambiguation routine, and the degree of confidence with each correlation.

Abstract: A dynamic data extraction, distillation, and enhancement system is disclosed that includes a dynamic extraction, distillation, and enhancement framework. The framework includes an allocator, extractor, and deconstructor stored in a non-transitory memory that, when executed by a processor, receive files in different formats from data sources, determine a native format of each file, identify and extract an embedded object from a file, deconstruct the file into components, assign each file to one of a plurality of streams based on the native format of the file, assign the embedded object to a stream based on a format of the embedded object, and assign a deconstructed component to a stream based on a format of the deconstructed component. The native format includes one of text, video, image, or audio. Each stream corresponds to one native format. The streams include a text stream, an audio stream, a video stream, and an image stream.

Abstract: A method of enhanced data orchestration (EDO). The method comprises receiving a first message by a mediation application executing on a computer system and analyzing the first message by the mediation application based at least in part on invoking a machine learning (ML) model by the mediation application, wherein the analyzing determines that a feature of the first message is a probable first component of an item of personally identifiable information (PII). The method further comprises receiving a second message by the mediation application, determining by the mediation application that a feature of the second message when combined with the feature of the first message constitutes an item of PII, and treating the first message and the second message in accordance with predefined PII handling protocols by the mediation application.

Abstract: A method of enhanced data orchestration (EDO). The method comprises receiving a first message by a mediation application executing on a computer system and analyzing the first message by the mediation application based at least in part on invoking a machine learning (ML) model by the mediation application. The analyzing determines that a feature of the first message is a probable first component of an item of personally identifiable information (PII). The method further comprises receiving a second message by the mediation application, determining by the mediation application that a feature of the second message when combined with the feature of the first message constitutes an item of PII, and treating the first message and the second message in accordance with predefined PII handling protocols by the mediation application.

Abstract: A system of building an enhanced data orchestration (EDO) enterprise service bus. The system also comprises an EDO builder application stored in the at least one non-transitory memory that when executed by the at least one processor, presents a visual interface that responds to drag and drop command inputs that build a sequence of data stream processes, presents a command input that when activated causes a properties dialog box to be presented that promotes a user defining data from a data stream to be passed in to a selected data stream process and defining data to be output by the selected data stream process, and builds an EDO enterprise service bus application based on pre-trained ML models, based on a base enterprise service bus application, based on the drag and drop command inputs, and based on user definitions of data provided through the properties dialog box.