Back to results
US 11,907,186 B2Granted
System and method for electronic data archival in a distributed data network
AssigneeBank of America Corporation
InventorRaja Arumugam Maharaja
Life of this patent
Estimating the term…
Abstract
Embodiments of the invention are directed to a system, method, or computer program product for an approach to electronic data archival in a distributed data network. The system allows for replicating and transmitting data for archival purposes from a source to a destination using a machine learning algorithm. The machine learning algorithm selectively distributes only data which has changed within a database, and thus prevents the necessity of repetitively archiving an entire database. The system categorizes changed data based on the characteristics of the data, and thereafter distributes the data via a distributed data network.
Claims17
A system for electronic data archival in a distributed data network, the system comprising:
- a memory device with computer-readable program code stored thereon;
- a communication device;
- a processing device operatively coupled to the memory device and the communication device, wherein the processing device is configured to execute the computer-readable program code to:
- receive schema comprising data;
- parse and divide the data into blocks;
- create a distributed ledger and assign a separate distributed ledger for each schema owner, wherein a source and a destination each comprise a machine learning algorithm;
- use the machine learning algorithm at the source to create and assign an assortment identifier to each block, wherein the assortment identifier is unchanged if there are no changes to the data in the block;
- identify changes to the data in the schema based on changes to the assortment identifier and distribute data into categories using the machine learning algorithm at the source, the categories used by the machine learning algorithm to determine how to transmit the data, wherein the categories comprise:
- a first category for receiving new data, wherein the new data is added to a new block, and the new block is added into the first category,
- a second category for receiving attribute level changes, wherein a position indicator and the data to be changed are denoted and placed into the second category;
- a third category for receiving small volume schema, wherein a generative adversarial network is implemented to convert the data to an image, and the image is placed into the third category;
- and
- dynamically generated one or more additional categories, wherein the data is placed into the one or more additional categories;
- and
- distribute the categories to the distributed ledger.
12 more elements
A computer program product for electronic data archival in a distributed data network, the computer program product comprising at least one non-transitory computer-readable medium having computer-readable program code portions embodied therein, the computer-readable program code portions comprising:
- receiving schema comprising data;
- parsing and dividing the data into blocks;
- creating a distributed ledger and assigning a separate distributed ledger for each schema owner, wherein a source and a destination each comprise a machine learning algorithm;
- using the machine learning algorithm at the source to create and assign an assortment identifier to each block, wherein the assortment identifier is unchanged if there are no changes to the data in the block;
- identifying changes to the data in the schema based on changes to the assortment identifier and distribute data into categories using the machine learning algorithm at the source, the categories used by the machine learning algorithm to determine how to transmit the data, wherein the categories comprise:
- a first category for receiving new data, wherein the new data is added to a new block, and the new block is added into the first category;
- a second category for receiving attribute level changes, wherein a position indicator and the data to be changed are denoted and placed into the second category;
- a third category for receiving small volume schema, wherein a generative adversarial network is implemented to convert the data to an image, and the image is placed into the third category;
- and
- dynamically generated one or more additional categories, wherein the data is placed into the one or more additional categories;
- and
- distributing the categories to the distributed ledger.
9 more elements
A computer-implemented method for electronic data archival in a distributed data network, the method comprising:
- providing a computing system comprising a computer processing device and a non-transitory computer readable medium, where the non-transitory computer readable medium comprises configured computer program instruction code, such that when said computer program instruction code is operated by said computer processing device, said computer processing device performs the following operations:
- receiving schema comprising data;
- parsing and dividing the data into blocks;
- creating a distributed ledger and assigning a separate distributed ledger for each schema owner, wherein a source and a destination each comprise a machine learning algorithm;
- using the machine learning algorithm at the source to create and assign an assortment identifier to each block, wherein the assortment identifier is unchanged if there are no changes to the data in the block;
- identifying changes to the data in the schema and distribute data into categories using the machine learning algorithm at the source, the categories used by the machine learning algorithm to determine how to transmit the data, wherein the categories comprise:
- a first category for receiving new data, wherein the new data is added to a new block, and the new block is added into the first category;
- a second category for receiving attribute level changes, wherein a position indicator and the data to be changed are denoted and placed into the second category;
- a third category for receiving small volume schema, wherein a generative adversarial network is implemented to convert the data to an image, and the image is placed into the third category;
- and
- dynamically generated one or more additional categories, wherein the data is placed into the one or more additional categories;
- and
- distributing the categories to the distributed ledger.
10 more elements
Dependencies are inferred from the claim text.