Abstract: Systems, computer program products, and methods are described herein managing data retrieval flow rates using artificial intelligence engines. The present disclosure is configured to assign priority of individual data pipelines within an overseer data pipeline via a data pipeline controller, wherein the overseer data pipeline comprises a set of data pipelines, wherein the data pipeline controller utilizes an artificial intelligence engine; regulate a data flow rate for individual data pipelines within the overseer data pipeline via the data pipeline controller; compress data within the set of data pipelines within the overseer data pipeline; encrypt data within the set of data pipelines within the overseer data pipeline; pause individual data pipelines within the set of data pipelines upon receiving a signal; filter data within the overseer data pipeline; and validate data within the overseer data pipeline.

Abstract: A method for database backup, search, and recovery using a quantum-computing-powered system with multi-dimensional scaling is provided. The method may include storing a first dataset in a first local database, generating a second dataset from a critical subset of the first dataset, and storing the second dataset in a second remote, cloud-based, database. The method may include receiving a search query at the first database. The method may include receiving a first indication of an outage at the first database and, in response to the first indication, routing the search query to a quantum processor and executing the search on the second dataset. The method may include receiving a second indication of a loss of data from the first dataset at the first database, and, in response to the second indication, restoring the critical subset of the first dataset to the first database from the second dataset at the second database using the quantum processor.

Abstract: Systems, computer program products, and methods are described herein managing data retrieval flow rates using artificial intelligence engines. The present disclosure is configured to assign priority of individual data pipelines within an overseer data pipeline via a data pipeline controller, wherein the overseer data pipeline comprises a set of data pipelines, wherein the data pipeline controller utilizes an artificial intelligence engine; regulate a data flow rate for individual data pipelines within the overseer data pipeline via the data pipeline controller; compress data within the set of data pipelines within the overseer data pipeline; encrypt data within the set of data pipelines within the overseer data pipeline; pause individual data pipelines within the set of data pipelines upon receiving a signal; filter data within the overseer data pipeline; and validate data within the overseer data pipeline.

Abstract: Systems, computer program products, and methods are described herein for auto-establishing data transfers between data centers for batch processing. The present disclosure is configured to identify a process associated with a primary site; determine a backup site associated with the primary site; apply the process to the backup site; generate, by an imitation artifact component, a record counter of the process as the process runs in the primary site, wherein the record counter comprises a cursor position indicating a current operation of the process; and mimic, based on the record counter, the process in the backup site as the process runs in the primary site.

Abstract: A method is provided that includes generating a first image of first memory blocks stored in a first database that includes a first hierarchical structure, and generating a second image of second memory blocks stored in a second database that includes a second hierarchical structure. The method includes processing the first and second images to identify a visual change between the first and second hierarchical structures. The method includes determining whether the first and second hierarchical structures are desynchronized based on the visual change. In response to determining that the first and second hierarchical structures are desynchronized, the method includes synchronizing a first set of user data in the first memory blocks with a second set of user data in the second memory blocks.

Abstract: A method is provided that includes generating a first image of first memory blocks stored in a first database that includes a first hierarchical structure, and generating a second image of second memory blocks stored in a second database that includes a second hierarchical structure. The method includes processing the first and second images to identify a visual change between the first and second hierarchical structures. The method includes determining whether the first and second hierarchical structures are desynchronized based on the visual change. In response to determining that the first and second hierarchical structures are desynchronized, the method includes synchronizing a first set of user data in the first memory blocks with a second set of user data in the second memory blocks.

Abstract: Universal message brokering is provided across known and future known computing platforms/systems. Message brokering occurs within adapters that are deployed directly within the computing platforms/systems. The adapters rely on Artificial Intelligence (AI) including Machine Learning (ML) to (i) identify the source and the target computing platforms/systems and (ii) convert/translate the messages from the source message format of the source computing platform/system to an identified universal message format and, upon receipt by the target computing platform/system, from the universal message format to the target message format. Further, the UMB adapters may additionally implement AI including ML to predict the volume of further message traffic and, in response, adjust message queues and/or UMB adapter activation in servers of distributed computing platforms/servers.

Abstract: Aspects of the disclosure relate to generating and replicating training data. A computing system may generate different versions of training data and corresponding different sequence numbers. A distributed machine learning model may be trained to generate different versions of the training data based on the corresponding different sequence numbers. The training data and identical copies of the distributed machine learning model may be sent to secondary computing devices. A second sequence number corresponding to a second version of the training data may be determined. The second sequence number may be sent to secondary computing devices. Based on inputting the second sequence number into the identical copy of the distributed machine learning model, copies of the second version of the training data may be generated in the secondary computing devices.

Abstract: This disclosure presents a system for rectifying server failures in distributed file systems using predictive logical markers. The system begins by receiving user details, a file name, and a block address via a distributed file system (DFS) client. If previous read information exists for the given file and user, a trained machine learning (ML) model predicts logical markers for file fragments. The ML model then transmits remaining block addresses to the DFS client. Concurrently, the system facilitates a data input stream, communicating predicted block addresses between the DFS client and the ML model. Block location information is retrieved from a distributed ledger via a primary node. The data input stream is processed via a secondary node, allowing efficient rectification of server failures.

Abstract: This disclosure presents a system for rectifying server failures in distributed file systems using predictive logical markers. The system begins by receiving user details, a file name, and a block address via a distributed file system (DFS) client. If previous read information exists for the given file and user, a trained machine learning (ML) model predicts logical markers for file fragments. The ML model then transmits remaining block addresses to the DFS client. Concurrently, the system facilitates a data input stream, communicating predicted block addresses between the DFS client and the ML model. Block location information is retrieved from a distributed ledger via a primary node. The data input stream is processed via a secondary node, allowing efficient rectification of server failures.

Abstract: This disclosure presents a system for rectifying server failures in distributed file systems using predictive logical markers. The system begins by receiving user details, a file name, and a block address via a distributed file system (DFS) client. If previous read information exists for the given file and user, a trained machine learning (ML) model predicts logical markers for file fragments. The ML model then transmits remaining block addresses to the DFS client. Concurrently, the system facilitates a data input stream, communicating predicted block addresses between the DFS client and the ML model. Block location information is retrieved from a distributed ledger via a primary node. The data input stream is processed via a secondary node, allowing efficient rectification of server failures.

Abstract: A method for database backup, search, and recovery using a quantum-computing-powered system with multi-dimensional scaling is provided. The method may include storing a first dataset in a first local database, generating a second dataset from a critical subset of the first dataset, and storing the second dataset in a second remote, cloud-based, database. The method may include receiving a search query at the first database. The method may include receiving a first indication of an outage at the first database and, in response to the first indication, routing the search query to a quantum processor and executing the search on the second dataset. The method may include receiving a second indication of a loss of data from the first dataset at the first database, and, in response to the second indication, restoring the critical subset of the first dataset to the first database from the second dataset at the second database using the quantum processor.

Abstract: A memory stores a first cache and a second cache. A processor copies a first portion of data from a first table stored in a database into a second table. The processor further determines that a second portion of data from the first table will be overwritten and copies the second portion into a third table. The processor further determines that a probability that a user will access a third portion of the first table is greater than a threshold and copies the third portion into the first cache. The processor further determines a fourth portion of the first table that the user accesses at a frequency greater than a set frequency and copies the fourth portion into the second cache.

Abstract: A memory stores a first and second cache. A processor stores a first query in a transaction log. The processor selects information from the transaction log, uses it to determine that a user will likely access a first portion of the database at a future date, and copies that portion into the first cache prior to the date. The processor further copies a second, frequently accessed portion of the database into the second cache. The processor additionally determines that a portion of data is in both caches and deletes it from the second cache. The processor receives a second query from the user, determines that it is directed at data in the first cache and accesses that cache. The processor also determines that the second query is not directed at data in the first cache but is directed at data in the second cache, and accesses that cache.

Abstract: A memory stores a first cache and a second cache. A processor copies a first portion of data from a first table stored in a database into a second table. The processor further determines that a second portion of data from the first table will be overwritten and copies the second portion into a third table. The processor further determines that a probability that a user will access a third portion of the first table is greater than a threshold and copies the third portion into the first cache. The processor further determines a fourth portion of the first table that the user accesses at a frequency greater than a set frequency and copies the fourth portion into the second cache.

Abstract: A memory stores a first cache and a second cache. A processor copies a first portion of data from a first table stored in a database into a second table, performs statistical analysis on the first portion, and stores the results into the second table. The processor further determines that a second portion of data from the first table will be overwritten, copies the second portion into a third table, performs statistical analysis on the second portion and stores the results into the third table. The processor further determines that a probability that a user will access a third portion of the first table is greater than a threshold and copies the third portion into the first cache. The processor further determines a fourth portion of the first table that the user accesses at a frequency greater than a set frequency and copies the fourth portion into the second cache.

Abstract: A resource offloading system comprises a relational database configured to store relational queries including searching features, a server configured to offload the relational queries to non-relational queries, and a non-relational database configured to store the non-relational queries. The server is configured to generate access patterns for each relational query based on searching features included in the relational query, classify the access pattern based on a terminology defined by a searching feature into a classification, rank the classified access patterns, generate candidate access patterns from the ranked access patterns, create non-relational queries based on the candidate access patterns which are ranked beyond a threshold, and store the non-relational queries in the non-relational database.

Abstract: A memory stores a first and second cache. A processor stores a first query in a transaction log. The processor selects information from the transaction log, uses it to determine that a user will likely access a first portion of the database at a future date, and copies that portion into the first cache prior to the date. The processor further copies a second, frequently accessed portion of the database into the second cache. The processor additionally determines that a portion of data is in both caches and deletes it from the second cache. The processor receives a second query from the user, determines that it is directed at data in the first cache and accesses that cache. The processor also determines that the second query is not directed at data in the first cache but is directed at data in the second cache, and accesses that cache.

Abstract: A memory stores a first and second cache. A processor stores a first query in a transaction log. The processor selects information from the transaction log, uses it to determine that a user will likely access a first portion of the database at a future date, and copies that portion into the first cache prior to the date. The processor further copies a second, frequently accessed portion of the database into the second cache. The processor additionally determines that a portion of data is in both caches and deletes it from the second cache. The processor receives a second query from the user, determines that it is directed at data in the first cache and accesses that cache. The processor also determines that the second query is not directed at data in the first cache but is directed at data in the second cache, and accesses that cache.

Abstract: A memory stores a first cache and a second cache. A processor copies a first portion of data from a first table stored in a database into a second table, performs statistical analysis on the first portion, and stores the results into the second table. The processor further determines that a second portion of data from the first table will be overwritten, copies the second portion into a third table, performs statistical analysis on the second portion and stores the results into the third table. The processor further determines that a probability that a user will access a third portion of the first table is greater than a threshold and copies the third portion into the first cache. The processor further determines a fourth portion of the first table that the user accesses at a frequency greater than a set frequency and copies the fourth portion into the second cache.