An asynchronous accounting method for a blockchain is provided. After a block reaches a consensus, different types of data contained in block information of the block are written into message queues corresponding to the respective types of data. Each of the message queues correspond to a different one of the types of data contained in the block information. A next round of block consensus processing is started after the different types of data are written into the message queues corresponding to the respective types of data. The different types of data are read from the message queues corresponding to the respective types of data to perform persistence processing of a blockchain ledger.

A data movement system is provided for moving data using a data-to-file-to-data movement path. The data movement system includes a source database, a target database, a configuration database, and a data movement server. The data movement is in communication with the source database, the target database, and the configuration database. The processor is configured to receive a configuration record including source details and target details. The processor is also configured to define an extraction query based on the source details and to apply the extraction query to the source database to obtain an extraction load. The processor is further configured to generate a load file based on the extraction load, to define a load script based on the target details, and to apply the load script to the load file to obtain a load query. The processor is also configured to update the target database with the load query.

Embodiments of the invention are directed to database replication using heterogenous encoding. Aspects include obtaining a database and analyzing a data pattern of data in the database. Aspects also include identifying a plurality of candidate encoding formats and evaluating a computing cost for encoding the database for each of the plurality of candidate encoding formats. Aspects further include selecting an encoding format from the plurality of candidate encoding formats based at least in part on the computing cost and storing a backup copy of the database using the encoding format.

A method for facilitating data synchronization across a plurality of platforms is provided. The method includes retrieving a change event, the change event corresponding to an event stream from a first platform; parsing the change event to identify a record and a data operation; examining a synchronization database to determine whether a corresponding record is persisted in a database of a second platform; inserting the record into the synchronization database when the corresponding record is not persisted in the platform, the inserted record including a change indicator; and updating, by using the synchronization database, the database of the second platform to include the record.

A computer-implemented system, platform, computer program product, and/or method for reducing data processing that includes identifying data properties used to generate features used as input to data analytic models; associating the data properties used to generate the features to corresponding features; determining whether an incoming data record is a previously processed data record; determining, in response to an incoming data record being a previously processed data record, whether the incoming data record matches the previously processed data record; identifying data properties in the incoming data record that have changed; determining features associated with the data properties in the incoming data record that have changed; and generating the features associated with the data properties in the incoming data record that have changed.

A computerized method for transforming a user interface according to machine learning includes selecting a persona and determining whether a first condition is true for an associated data structure. In response to determining the first condition is true, the method includes determining whether a second condition is true. In response to determining the second condition is not true, the method includes loading a first trained machine learning model, inputting a first set of explanatory variables to generate a first metric, and transforming the user interface according to the first metric. In response to determining the second condition is true, the method includes determining whether a third condition is true. In response to determining the third condition is true, loading a second trained machine learning model, inputting a second set of explanatory variables to generate a second metric, and transforming the user interface according to the second metric.

A first stack running on a processor receives the transaction data, reference data, and context data. The reference data is independent of the transaction and of a user. The context data is associated with the user but is independent of the transaction. The first stack strips the transaction of derivable data to obtain stripped data. The derivable data includes data that can be derived from the stripped data, the context data, and the reference data. The derivable data can stream the stripped data to a global database available and redundant across multiple geographical regions. After the first stack fails, a second stack can resume the transaction by retrieving the stripped data from the global database, and retrieving the context data, and the reference data. The second stack can recreate the transaction data based on the stripped data, the context data, and the reference data, and can resume the transaction.

Techniques for request throttling in a provider network environment are described. A throttle handler controls whether requests will be processed through maintaining a token-based record, per type of request, having a token value indicative of a number of requests that can be processed over a time period. For a request, the token value of the token-based record corresponding to the request type is updated based on calculating an elapsed time between a last update time of the token-based record and the current time, calculating an intermediate token value as the existing token value plus a value of the elapsed time multiplied by a rate, and updating the token value to be the minimum between the intermediate token value and a burst value. The request is serviced when the updated token value is determined to be greater than or equal to a number of tokens needed to perform the request.

Embodiments provide methods and systems for merchant data cleansing in payment network. Method performed by server system includes accessing electronic payment transaction records from transaction database. Each electronic payment transaction record includes merchant data fields. Method includes determining set of electronic payment transaction records with ambiguous merchant data fields having matching probability scores less than predetermined threshold value computed by probabilistic matching model and identifying at least one issue for non-matching of each of set of electronic payment transaction records. Method includes determining data model based on at least one issue of each of set of electronic payment transaction records. Data model is one of: phone-to-city model, payment aggregator model, and merchant name normalization model. Method includes updating set of electronic payment transaction records with unambiguous merchant data fields corresponding to ambiguous merchant data fields by applying data model to each of set of electronic payment transaction records.

Source database precommitted transactions are resolved in a target database of a database replication system when selected source database precommitted transactions are subsequently aborted in the source database.