Abstract: Systems, computer program products, and methods are described herein for improving computer energy consumption and computer processing by implementing a pluggable carbon emission gateway. The present disclosure is configured to identify, by a pluggable emission gateway, at least one application programming interface request; generate at least one transaction per second load attribute; identify at least one application consumption; determine at least one emission; identify an emission threshold for the at least one emission; compare the at least one emission to the emission threshold; apply, in an instance where the at least one emission associated with the at least one API request exceeds the emission threshold, at least one API parameter adjustment for the API request; and generate an updated application by applying the at least one API parameter adjustment to at least one of the at least one application or the at least one API request.

Abstract: A method for managing metaverse instances includes registering a physical control level for a user. The physical control level includes information about physical locations and data items that the user is allowed to access within an entity. A digital identity is generated for the user. The digital identity includes a virtual control level for an avatar of the user and allows the avatar to access a metaverse instance of a metaverse of the entity. The virtual control level is synchronized with the physical control level. Computing resources to be allocated to the metaverse instance are determined. A first instruction is sent to a metaverse server to open the metaverse instance. A second instruction is sent to the metaverse server to allocate the computing resources to the metaverse instance. A third instruction is sent to the metaverse server to generate the avatar. The avatar is allowed access to the metaverse instance.

Abstract: Embodiments of the present invention provide a system for replication and transmission of critical data using ultra reliable low latency communication network architecture. The system is configured for determining initiation of a real-time request from one or more channels to one or more receiving applications, determining that the real-time request does not comprise a criticality header, generating the criticality header for the real-time request, determining if the real-time request is eligible for transmission via an ultra-reliable low latency communication network, and transmitting the real-time request to a cluster of servers based on determining if the real-time request is eligible for transmission via an ultra-reliable low latency communication network.

Abstract: A system is provided for distributed and authenticated provisioning of encrypted electronic data. In particular, the system may comprise an intelligent monitoring engine that may continuously monitor and log actions and/or requests executed by an endpoint device to identify one or more data request patterns associated with the endpoint device. Based on identifying the data request patterns, the system may transfer encrypted electronic data to the endpoint device for storage, where the encrypted electronic data may be configured with a time to live on the endpoint device. The system may further comprise an unauthorized activity monitor that may monitor the actions taken by the endpoint device to detect potentially unauthorized activity involving the endpoint device. If no unauthorized activity is detected, the system may permit the endpoint device to decrypt the encrypted electronic data to be presented to a user.

Abstract: A system includes a database, a memory, and a processor. The database stores a set of transactions, each of which includes a set of fields. The processor receives a query from a user through a channel. The query is directed to a transaction stored in the database. The transaction includes a set of fields. The processor applies a machine learning policy adapted to determine a view, i.e., a subset of the set of fields that the user is likely to access using the channel. The processor further accesses the database and retrieves the view. The processor then sends the view to the channel.

Abstract: A system includes a first database, a second database, a first processor, a second processor, a third processor, and a fourth processor. The first database includes a first segment assigned to the first processor and a first transaction type and a second segment assigned to the second processor and a second transaction type. Each of the first and second processors receives a request, assigned to its associated transaction type, from the third hardware processor, to store data in the first database. The processor then writes the data into the segment of first database assigned to the processor/transaction type. The processor further stores a command to write the data into the second database in a queue. The fourth processor then executes the commands stored in the queue, thereby writing the data into the second database.

Abstract: A system includes a first database, a second database, a first processor, a second processor, a third processor, and a fourth processor. The first database includes a first segment assigned to the first processor and a first transaction type and a second segment assigned to the second processor and a second transaction type. Each of the first and second processors receives a request, assigned to its associated transaction type, from the third hardware processor, to store data in the first database. The processor then writes the data into the segment of first database assigned to the processor/transaction type. The processor further stores a command to write the data into the second database in a queue. The fourth processor then executes the commands stored in the queue, thereby writing the data into the second database.

Abstract: A system includes a database, a memory, and a processor. The database stores a set of transactions, each of which includes a set of fields. The processor receives a query from a user through a channel. The query is directed to a transaction stored in the database. The transaction includes a set of fields. The processor applies a machine learning policy adapted to determine a view, i.e., a subset of the set of fields that the user is likely to access using the channel. The processor further accesses the database and retrieves the view. The processor then sends the view to the channel.