Linking physical locations and online channels in a database

A method of linking physical locations and online locations with a brand in a database, comprising:

• receiving, by a device and from one or more data sources, information indicating a plurality of data sets,
• wherein the plurality of data sets indicate information associated with respective physical locations or online locations;
• identifying, by the device, a data set, from the plurality of data sets, that indicates information associated with an online location,
• wherein the information includes at least one of an entity name, an address, a phone number, a uniform resource locator, an entity identifier, or metadata;
• parsing, by the device, the data set to identify information for a set of features,
• wherein parsing the information comprises:
• identifying the set of features by obtaining the set of features from structured data by performing natural language processing to extract the set of features from unstructured data;
• iteratively training a machine training model, comprising at least one of a neural network or a support vector, to generate a trained machine learning model,
• wherein the trained machine learning model is trained using the set of features;
• determining, by the device and based on applying the trained machine learning model to the information for the set of features, that a score associated with an output of the trained machine learning model satisfies a threshold,
• wherein the score indicates a likelihood that the online location is associated with a brand;
• determining, based on the score, the brand associated with the online location;
• pairing, by the device, the online location with the brand in the database such that the online location is linked with a first physical location of the brand in the database;
• and
• adding information associated with the online location to a graph associated with the brand,
• wherein the graph provides link information associated with at least one online location and at least one physical location related to the brand, and
• wherein the graph comprises links indicating that the at least one online location and at least one physical location are associated with the same brand;
• receiving a request to generate one or more temporary credentials associated with the brand;
• identifying, based on the graph, the at least one online location and the at least one physical location associated with the brand;
• and
• generating based on identifying the at least one online location and the at least one physical location associated with the brand, the one or more temporary credentials,
• wherein the one or more temporary credentials are enabled to complete one or more transactions at the at least one online location and the at least one physical location.

A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:

• one or more instructions that, when executed by one or more processors of a device, cause the device to:
• receive information associated with physical locations of entities and online channels of entities,
• wherein the information includes a plurality of data sets, and
• wherein each data set, of the plurality of data sets, indicates information associated with a physical location or an online channel;
• process the information based on one or more features to identify candidate data sets,
• wherein the candidate data sets include two or more data sets, of the plurality of data sets, with at least one data set associated with a physical location and at least one data set associated with an online channel, and
• wherein processing the information comprises:
• identifying the candidate data sets by obtaining the candidate data sets from structured data by performing natural language processing to extract the candidate data sets from unstructured data;
• iteratively training a machine training model, comprising at least one of a neural network or a support vector, to generate a trained machine learning model,
• wherein the trained machine learning model is trained using the candidate data sets;
• apply the trained machine learning model to the candidate data sets to determine a score,
• wherein the score indicates a likelihood that the two or more data sets are associated with a same entity;
• link the two or more data sets in a database based on a determination that the score satisfies a threshold,
• wherein the linking indicates that the two or more data sets are associated with the same entity;
• and
• add information associated with the two or more data sets to a graph associated with the same entity;
• receive a request to generate one or more temporary credentials associated with the entity;
• identify, based on the graph, at least one online location and at least one physical location associated with the entity;
• and
• generate, based on identifying the at least one online location and the at least one physical location associated with the entity, the one or more temporary credentials,
• wherein the one or more temporary credentials are enabled to complete one or more transactions at the at least one online location and the at least one physical location.