Abstract: Provided herein is a method of storing an incoming dataset in a data mesh. The method may include a plurality of steps. The steps may include associating a metadata tag with a classifying feature and a storage instruction in a (first) relational database. The steps may include scanning incoming datasets to identify datasets characterized by the classifying feature. The steps may include tagging an incoming dataset to generate a tagged dataset. The steps may include storing the tagged dataset in the data mesh, according to the storage instruction. The steps may include associating, in a second relational database, the metadata tag with the initial storage location. The steps may include modifying the storage instruction. The steps may include storing the incoming dataset an additional time in the data mesh, according to the modified storage instruction.

Abstract: A method for facilitating transfer of a new dataset across a network may include the following steps: (a) querying the global dataset via a computer processor to identify an existing dataset having data points corresponding to the new dataset; (b) identifying historical data rules previously used for a set of data transfers relating to the existing dataset; and (c) using the historical data rules to assist a machine learning engine in generating data rules for use with a data transfer relating to the new dataset.

Abstract: Provided herein is a method of storing an incoming dataset in a data mesh. The method may include a plurality of steps. The steps may include associating a metadata tag with a classifying feature and a storage instruction in a (first) relational database. The steps may include scanning incoming datasets to identify datasets characterized by the classifying feature. The steps may include tagging an incoming dataset to generate a tagged dataset. The steps may include storing the tagged dataset in the data mesh, according to the storage instruction. The steps may include associating, in a second relational database, the metadata tag with the initial storage location. The steps may include modifying the storage instruction. The steps may include storing the incoming dataset an additional time in the data mesh, according to the modified storage instruction.

Abstract: A system for administering regionally-based data storage and control is provided. The system may include a computer processor and a distributed memory. The distributed memory may be distributed across a plurality of regions. The distributed memory may provide data storage. The system may build a plurality of artificial intelligence (AI) models. Each of the models may correspond to a set of data rules. Each of the data rules governs the storage of data in each of the plurality of regions. The system may further create, or otherwise access, a plurality of data keys. Each of the data keys preferably corresponds to one of the regions. The system preferably centralizes the location of the plurality of data keys in a central location such that control of the data in the plurality of regions can be administered by the processor, via the plurality of data keys, from the central location.

Abstract: A method for facilitating transfer of a new dataset across a network may include the following steps: (a) querying the global dataset via a computer processor to identify an existing dataset having data points corresponding to the new dataset; (b) identifying historical data rules previously used for a set of data transfers relating to the existing dataset; and (c) using the historical data rules to assist a machine learning engine in generating data rules for use with a data transfer relating to the new dataset.

Abstract: A system and method for sharing sensitive data which may minimize the risk of unapproved data access. The system may include a data network, a data orchestrator, data storage units, controllers, user devices, and a data map. The data storage units may contain sensitive data with metadate encryptions in a dormant state. The data orchestrator may be configured to implement machine learning systems to: categorize and layer the sensitive data in the data storage units; receive a user key and a device key from the user device; look up the data map for level of access of the key-bearing user device; provide the keys as pre-authorization to the controllers; after the data storage units are woken up, provide keys to the metadata to gain access to the sensitive data; retrieve and provide the sensitive data to the user device; and revert the sensitive data back to the dormant state.

Abstract: A system and method for providing a customized response for data by creating and applying a multi-layered data map. Methods may include configuring a computer processor located in a data mesh to implement machine learning systems for identifying the content of data distributed across data storage units, determining the credentials required to access the data, identifying the locations of the data, and identifying points of interest within the data. Methods may include a machine learning system for populating a data map with the preceding information. Methods may include receiving a request for data from an accessor presenting their credentials. Methods may include consulting with the multi-layer data map and presenting information to the accessor which contains the requested data according to the accessor's credentials, the locations of the data, the level of sensitivity associated with the data, and/or points of interest within the data.

Abstract: There is provided a method for increasing available memory in an edge layer of a network, where the network stores data and has a default retention period for datasets. The method may include the steps of: (a) receiving the dataset by the edge layer of the network; (b) analyzing the dataset by the front-end filter to identify disposable data points in the dataset, (c) instructing a computer processor to remove the disposable data points from the dataset, upon passage of a specified time interval.

Abstract: An apparatus and method for using a data mesh to structure unstructured data is provided.