Multi-source, multi-dimensional, cross-entity, multimedia merchant analytics database platform apparatuses, methods and systems
The MULTI-SOURCE, MULTI-DIMENSIONAL, CROSS-ENTITY, MULTIMEDIA MERCHANT ANALYTICS DATABASE PLATFORM APPARATUSES, METHODS AND SYSTEMS (“MDB”) transform data aggregated from various computer resources using MDB components into updated entity profiles and/or social graphs. In one implementation, the MDB aggregates data records including search results, purchase transaction data, service usage data, service enrollment data, and social data. The MDB identifies data field types within the data records and their associated data values. From the data field types and their associated data values, the MDB identifies an entity. The MDB generates correlations of the entity to other entities identifiable from the data field types and their associated data values. The MDB also associates attributes to the entity by drawing inferences related to the entity from the data field types and their associated data values. Using the generated correlations and associated attributes, the MDB generates an updated profile and social graph of the entity. The MDB provides the updated profile and social graph for an automated web form filling request.
1. A merchant analytics platform processor-implemented method comprising:
obtaining, by a pay network server, a hypertext transfer protocol (HTTP) GET message from a merchant server, the HTTP GET message including a request for a merchant analytics recommendation including a user identifier;
parsing, by the pay network server, the HTTP GET message to extract the user identifier;
upon obtaining the HTTP GET message and extracting the user identifier, querying, by the pay network server, a distributed linking node mesh for entities correlated with the user identifier;
receiving, by the pay network server, aggregated user entity correlation data;
generating, by the pay network server, a user behavior profile based on the aggregated user entity correlation data;
determining, by the pay network server, a product or service using the user behavior profile;
based on the determination of the product or service, generating, by the pay network server, an HTTP POST message including an indication of the product or service; and
providing, by the pay network server, the HTTP POST message to the merchant server in response to the request for the merchant analytics recommendation;
wherein the distributed linking node mesh includes a node representing an observable entity and a node representing a deduced entity derived through aggregating information associated with the user.
2. The method of claim 1 , wherein the user identifier is user payment account information.
3. The method of claim 2 , additionally comprising:
querying a transaction database for a user identity associated with the user payment account information.
4. The method of claim 1 , additionally comprising:
anonymizing the aggregated user entity correlation data.
5. The method of claim 4 , wherein anonymizing includes masking user payment account information.
6. The method of claim 4 , wherein anonymizing includes swapping aggregated user entity correlation data.
7. The method of claim 6 , wherein swapping aggregated user entity correlation data preserves the sum of the user entity correlation components.
8. The method of claim 4 , wherein anonymizing includes applying multiple functions to the aggregated user entity correlation data based on the user entity correlation data type.
9. The method of claim 1 , wherein generating a user behavior profile includes determining an entity not present in the distributed node linking mesh.
10. The method of claim 9 , additionally comprising:
inserting the determined entity into the distributed node linking mesh.
11. The method of claim 10 , additionally comprising:
updating the distributed node linking mesh; and
querying the distributed node linking mesh for updated entities correlated with the user identifier.
12. The method of claim 1 , wherein the aggregated user entity correlation data includes aggregated search results data.
13. The method of claim 11 , wherein the aggregated user entity correlation data includes aggregated transaction data.
14. The method of claim 11 , wherein the aggregated user entity correlation data includes aggregated service usage data.
15. The method of claim 1 , wherein the aggregated user entity correlation data includes aggregated enrollment data.
16. The method of claim 1 , wherein the aggregated user entity correlation data includes aggregated email data.
17. The method of claim 1 , wherein the aggregated user entity correlation data includes aggregated social media data.
18. The method of claim 1 , additionally comprising:
querying a merchant inventory database to determine a current inventory level of the product or service.
19. The method of claim 18 , additionally comprising:
determining a second product or service using the user behavior profile.
20. A merchant analytics platform processor-implemented method comprising:
obtaining, by a pay network server, a hypertext transfer protocol (HTTP) GET message from a merchant server, the HTTP GET message including a request for a merchant analytics recommendation including a user identification package, wherein the user identification package includes user contact information and an approximate user location;
parsing, by the pay network server, the HTTP GET message to extract the user identification package;
upon obtaining the HTTP GET message and extracting the user identification package, querying, by the pay network server, a distributed linking node mesh for entities correlated with the user identification package;
receiving, by the pay network server, aggregated user entity correlation data;
querying, by the pay network server, an anonymization database for at least one anonymization operation applicable to the aggregated user entity correlation data;
applying, by the pay network server, a first at least one anonymization operation to the aggregated user entity correlation data;
determining, by the pay network server, that the aggregated user entity correlation data is not sufficiently anonymized;
applying, by the pay network server, a second at least one anonymization operation to the aggregated user entity correlation data;
querying, by the pay network server, a user behavior template database for a user behavior template model;
generating, by the pay network server, a user behavior profile based on the user behavior template model and the aggregated user entity correlation data;
determining, by the pay network server, using the user behavior profile, a product or service having the highest likelihood of being purchased by the user;
querying, by the pay network server, a merchant inventory database to determine a current inventory level of the product or service;
based on the determination of the product or service and the current inventory level of the product or service, generating, by the pay network server, an HTTP POST message including an indication of the product or service; and
providing, by the pay network server, the HTTP POST message to the merchant server in response to the request for the merchant analytics recommendation;
wherein the distributed linking node mesh includes a node representing an observable entity and a node representing a deduced entity derived through aggregating information associated with the user.