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.

The present disclosure describes techniques for performing a smart tree lookup operation in a balanced tree. The techniques according to the present disclosure comprise identifying at least one data entry to be searched within the balanced tree, extracting a plurality of keys of the balanced tree, determining whether all or a subset of keys of the plurality of keys are required for searching the at least one data entry within the balanced tree, in response to the determination that the subset of keys of the plurality of keys are required for searching the at least one data entry, generating a first compare function for each of the at least one data entry using the subset of keys, and traversing a first path for the at least one data entry based on the first compare function. Accordingly, the techniques of the present disclosure enable efficient balanced tree lookup operation.

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis with respect to structured data objects. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis with respect to structured data objects by utilizing at least one of cross-table data similarity score generation machine learning models and unsupervised anomalous table row detection machine learning models.

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.

Methods, systems, and computer storage media for providing a unified multilayer-based index for a contextual geoanalytics engine in a data analytics system. The contextual geoanalytics engine is configured to aggregate point-of-interest geographical data from multiple data sources into an aggregate or composite dataset. The contextual geoanalytics engine then transforms and maps the data into a homogenous dataset—i.e., a location embedding record that is homogenous representation of an aggregated dataset—comparable across global geographical regions. The homogenous dataset is accessible via the unified multilayer-based index that is a single geographical index, where the homogenous dataset is a composite of different datasets. The data includes different data types, where the data types are stored in different layers while sharing a common index (i.e., unified multilayer-based index). In this way, the unified multilayer-based index is a shared common index with a plurality of different layers associated with data used in geographically-based analytics.

A system for transmitting data objects among user profiles receives a request to transmit a particular number of a first type of data object to a receiver profile. The system determines whether a sender profile is associated with the particular number of the first type of data object. In response to determining that the sender profile is not associated with the particular number of the first type of data object, the system identifies one or more other types of data objects that correspond to the particular number of the first type of data object. The system initiates a user interaction session. The system generates a block within a blockchain network to store user interaction session metadata. The system transmits the identified one or more other types of data objects to the receiver profile. The system stores, in the block, a completion token that indicates the user interaction session is completed.

A system for transmitting data objects among user profiles receives a request to transmit a particular number of a first type of data object to a receiver profile. The system determines whether a sender profile is associated with the particular number of the first type of data object. In response to determining that the sender profile is not associated with the particular number of the first type of data object, the system identifies one or more other types of data objects that correspond to the particular number of the first type of data object. The system initiates a user interaction session. The system generates a block within a blockchain network to store user interaction session metadata. The system transmits the identified one or more other types of data objects to the receiver profile. The system stores, in the block, a completion token that indicates the user interaction session is completed.

A host, a storage system, and an operating method of the host are provided. The host includes a host memory configured to store a tree structure including a leaf node and an index node, an index management module configured to manage an index based on the tree structure and generate a first log corresponding to the leaf node based on a first update request corresponding to a first key-value entry included in the leaf node, and a device driver configured to generate a first write command corresponding to the first log and transmit the generated first write command to a key-value storage device, so as to store the first log in the key-value storage device. The index management module is configured to generate a first new key-value entry, the first new-key value entry including a first value updated based on the first update request, as the first log.

A system and method for detecting anomalies in very large datasets is disclosed. The method includes calculating statistics for data elements in a data set over a range of time periods. These statistics are arranged into a 2D array and analyzed using a machine learning algorithm to detect anomalous regions. The method also includes steps of analyzing time series of the data based on detected anomalous regions, correcting any errors in the datasets, and storing the corrected values in a separate database to maintain data integrity.

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.