An example operation may include one or more of acquiring, by a promotion processor node, consumer exploration of a product data from a blockchain, determining, by the promotion processor node, features of the product, receiving, by the promotion processor node, a promotion plan from at least one product retailer node, and executing a smart contract to generate a plurality of promotion tokens based on the features of the product and the promotion plan.

A publish and subscribe architecture can be utilized to manage records, which can be used to accomplish the various functional goals. At least one template having definitions for managing production and consumption of data within an unconfigured group of computing resources is maintained. Records organized by topic collected from multiple disparate previously configured producers are utilized to initiate configuration of the unconfigured group of computing resources. Records within a topic are organized by a corresponding topic sequence. A first portion of the computing resources are configured as consumers based on the at least one template. The consumers to consume records at a pace independent of record production. A second portion of the computing resources are configured as producers based on the at least one template. The producers to produce records at a pace independent of record consumption.

Embodiments detect fraud of risk targets that include both customer accounts and cashiers. Embodiments receive historical point of sale (“POS”) data and divide the POS data into store groupings. Embodiments create a first aggregation of the POS data corresponding to the customer accounts and a second aggregation of the POS data corresponding to the cashiers. Embodiments calculate first features corresponding to the customer accounts and second features corresponding to the cashiers. Embodiments filter the risk targets based on rules and separate the filtered risk targets into a plurality of data ranges. For each combination of store groupings and data ranges, embodiments train an unsupervised machine learning model. Embodiments then apply the unsupervised machine learning models after the training to generate first anomaly scores for each of the customer accounts and cashiers.

The present disclosure provides a method of user clustering, and the method includes: acquiring a clustering condition for a predetermined user group, wherein the clustering condition includes a time selecting condition and an event selecting condition; determining at least one target time period for each user behavior data in a user behavior database based on the time selecting condition; determining association data indicating a relationship between the each user behavior data and each target time period based on the each user behavior data and the each target time period; and selecting target association data for a time period to be monitored based on the time period to be monitored and the event selecting condition, so as to determine a target user belonging to the predetermined user group according to the target association data. The present disclosure also provides an apparatus of user clustering, a computer device and a non-transitory medium.

Providing access to usage reports on a cloud-based data warehouse including maintaining, by a management module, a metadata table on the cloud-based data warehouse, wherein the metadata table comprises usage reports for a plurality of organizations; receiving, by the management module, a request for the metadata table from an administrator account for a first organization of the plurality of organizations; granting, by the management module, the administrator account permission to access a filtered portion of the metadata table, wherein the filtered portion of the metadata table is generated by filtering the metadata table by an organization identifier of the first organization; and providing, by the management module, the filtered portion of the metadata table to the administrator account.

A data classification apparatus includes a data transformation unit that generates a feature vector by using classification target data, a classification estimation process observation unit that acquires, from a classification estimation unit that estimates classification of the classification target data and including a plurality of weak classifiers, observation information in a classification process based on the feature vector, and generates a classification estimation process feature vector based on the observation information, and an error determination unit that determines, in accordance with an input of the classification estimation process feature vector generated by the classification estimation process observation unit and a classification result output from the classification estimation unit to which the feature vector is input, whether the classification result is correct.

The present disclosure relates to database technology and in particular to dynamically updating and customizing database partitions. A computer-implemented engine is disclosed for identifying and retrieving a number of data records applicable to generate a response to a request, the engine having access to at least two partitions. Partition statistics are generated indicating correlations between the data records and, based on that partition statistics, the data records having the strongest correlation with each other are relocated to partitions so that the number of partitions which have to be queried in order to generate a response to a data request is minimized. Furthermore, the computational load caused when generating responses is more equally distributed across the partitions.

A non-transitory computer-readable storage medium storing an explanatory information output program for causing a computer to execute processing includes obtaining a contribution of each of a plurality of factors to an output result of a machine learning model in a case of inputting each of a plurality of pieces of data, each of the plurality of factors being included in each of the plurality of pieces of data; clustering the plurality of pieces of data based on the contribution of each of the plurality of factors to generate a plurality of groups of factors; and outputting explanatory information that includes a diagram representing magnitude of the contribution of each of the plurality of factors to the output result in a case of inputting data included in the group for each of the plurality of groups.

A method for graph data processing comprises obtaining graph data which includes a plurality of nodes and data corresponding to the plurality of nodes respectively; classifying the plurality of nodes into at least one category of a plurality of categories, wherein the plurality of categories are associated with a plurality of node relationship patterns; determining, from a plurality of candidate parameter value sets of a graph convolutional network (GCN) model, parameter value subsets respectively matching at least one category, wherein the plurality of candidate parameter value sets are determined by training the GCN model respectively for the plurality of node relationship patterns; and using the parameter value subsets respectively matching the at least one category to respectively perform a graph convolution operation in the GCN model on data corresponding to the nodes classified into the at least one category to obtain a processing result for the graph data.

A database storing data associated with an identifier unique to each sample, the data including first data representative of at least one of composition data, processing data, and property data for the each sample, and second data representative of microstructure data for the each sample. The microstructure data includes a feature determined based on a temperature dependence of magnetization for the each sample.