A storage device that includes a non-volatile memory is provided. The non-volatile memory includes a control circuitry that is communicatively coupled to a memory block that includes memory cells arranged word lines. The control circuitry is configured to program the memory cells of a selected word line in a plurality of programming loops to store a single bit of data in each memory cell of the selected word line. The programming loops include programming operations and verify operations. The programming operations include applying a programming voltage to the selected word line, and the verify operations include applying a verify voltage to the selected word line. At least one programming loop of the plurality of programming loops further includes a pre-verify operation. The pre-verify operation includes applying a pre-read voltage to the selected word line. The pre-read voltage is less than the verify voltage.

A method and apparatus for mining a competition relationship between POIs. An embodiment of the method includes: acquiring a graphlet mining result obtained by mining map retrieval data of users which encompasses attribute information of retrieved target POIs, the graphlet mining result encompassing occurrence frequencies of respective preset situations, and a preset situation comprising: conforming to attribute information of POIs represented by a corresponding preset graphlet and a preset association relationship between attribute information of at least two POIs; for a first and second POI, determining an occurrence frequency of a preset situation corresponding to a preset graphlet where attribute information of the first and second POI co-occur, and generating a relationship feature of the first and second POI; and inputting the relationship feature into a pre-trained relationship prediction model to obtain a competition relationship prediction result of the first POI and the second POI.

A method for managing big metadata using columnar techniques includes receiving a query request requesting data blocks from a data table that match query parameters. The data table is associated with system tables that each includes metadata for a corresponding data block of the data table. The method includes generating, based on the query request, a system query to return a subset of rows that correspond to the data blocks that match the query parameters. The method further includes generating, based on the query request and the system query, a final query to return a subset of data blocks from the data table corresponding to the subset of rows. The method also includes determining whether any of the data blocks in the subset of data blocks match the query parameters, and returning the matching data blocks when one or more data blocks match the query parameters.

In some embodiments, a method receives a sequence of subsidiary features that are associated with a sequence of main features. A subsidiary feature provides subsidiary information for a main feature. A sequence of first weights for the sequence of subsidiary features is generates where a first weight in the sequence of first weights is generated based on a respective subsidiary feature. The method processes the sequence of first weights to generate a sequence of second weights. The processing uses relationships in the sequence of first weights to generate values of the second weights. The method uses the sequence of second weights to process the sequence of main features to generate an output for the sequence of main features.

A data processing server may receive a set of data objects for frequent pattern (FP) analysis. The set of data objects may be analyzed using an attribute diversity technique. For the set of data attributes of the set of data objects, the server may arrange the attributes in one or more dimensions. The server may initialize a set of centroids on data points and identify mean values of nearby data points. Based on an iteration of the mean value calculation, the server may identify a set of attributes corresponding to final mean values as being groups of similarly frequent attributes. These groups of similarly frequent attributes may be analyzed using an FP analysis procedure to identify frequent patterns of data attributes.

Techniques and solutions are described for determining association rules using a machine learning technique on a subset of data to which the association rules might apply, and from which they can be determined. In particular, association rules are determined by tracking changes to attribute values of data objects having a type. The changed attribute value can be used as a consequent in an association rule. Values of other attributes of data objects having the changed attribute value can be used as antecedents in association rules. Values used in antecedents can be constrained, such as by limiting values to those associated with scope attributes or values satisfying a threshold occurrence frequency. In some cases, determined association rules can be automatically implemented, such as to process input or stored data for data objects of the type.

A method for awarding blockchain mining fees based on recycling efforts includes: receiving recycling data for each of a plurality of mining systems including an amount of recycled materials associated with the respective mining system; receiving validation data for each mining system from third party entities including, for each mining system, a confirmation of the amount of recycled materials for the respective recycling data; selecting one of the mining systems, which is weighted based on the amount of recycled materials for the respective mining system compared to a total amount of recycled materials for all mining systems; and receiving a new block generated by the selected mining system including a block header and a plurality of blockchain data values including a blockchain data value corresponding to a blockchain transaction for payment of mining fees for the new block to a wallet associated with the selected mining system.

Systems, methods, and software of processing a tabular dataset. In one embodiment, a system extracts raw association rules from the tabular dataset. Each of the raw association rules comprises a relationship between a set of antecedents and a single consequent, and corresponds to one or more transactions. The system determines potential rule merge groups of the raw association rules based on the antecedents, and determines one or more actual rule merge groups of the raw association rules in each potential rule merge group based on the transactions. The system combines the raw association rules in an actual rule merge group to generate a merged association rule. The system then generates a set of insights based on one or more merged association rules, and performs an operation based on the set of insights.

A data server may support segment identification based on a selected user profile. For example, a user may select a user profile as the basis for identifying a segment of additional user profiles. The server may identify attributes associated with the selected user identifier and generate an expression based on the identified subset. The expression may include a normalization function corresponding to at least one attribute. The normalization function may identify correlated attribute values for an attribute associated with the selected user profile. The data server may query a data storage system to identify the additional user profiles based on the expression. The data server may also support user defined Boolean expressions such that the expression is used to identify user identifiers associated with a first attribute and a second attribute.

Techniques for partitioning data from a data stream into batches and inferring schema for individual batches based on the field values of each batch are disclosed. The system may infer different schemas corresponding to different batches of data records even though the batches are received from a common data stream or a common data source. The system may infer a schema by determining whether a field contains single values or multiple values. Then the system determines the field type(s) associated with the values. These determinations are then stored in a dictionary generated for each batch.