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 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.

A data analytics system may include a first risk relationship data store containing electronic records that represent a plurality of risk relationships between the enterprise and a first risk relationship provider. Similarly, a second risk relationship data store containing electronic records that represent a plurality of risk relationships between the enterprise and a second risk relationship provider. A back-end application computer server may include a data mining engine that analyzes a set of electronic records in the first and second risk relationship data stores to identify flags corresponding to risk drivers. A predictive analytics engine may then calculate a risk score associated with the set of electronic records based on the associated entity attribute values and the identified flags corresponding to risk drivers. An insight platform may automatically generate a recommended action for the enterprise to lower the calculated risk score.

Methods and devices for propagating transactions in a network of nodes, each node having one or more connections to other nodes. The method includes receiving a plurality of incoming transactions over a time period; combining the plurality of incoming transactions using network coding to generate a composite message; sending the composite message to one or more nodes in the network; and determining an adjusted time period based on an equilibrium constant parameter and a count of transactions in the plurality of incoming transactions received over the time period.

The present disclosure discloses a data mining method, a data mining apparatus, an electronic device and a storage medium, relates to the technical field of the Internet, and in particular to the Internet big data processing technology. The method includes: acquiring a current article to be mined; obtaining information values required for each data identification strategy of multiple data identification strategies from the current article, and each data identification strategy is used for identifying a preset type of data; identifying a data type of the current article according to the information values required for each data identification strategy to obtain a data type identification result; and determining whether the current article belongs to any preset type of data according to the data type identification result.

Seed patterns are derived from a sequence database. Execution costs for types of seed patterns are computed. Each seed pattern is iteratively distributed to distributed nodes along with that seed pattern's assigned execution cost. The distributed nodes processing in parallel to mine the sequence database for super patterns found in the sequence database. When a distributed node exhausts its execution budget, any remaining mining needed for the seed pattern being mined is reallocated to another distributed node having remaining execution budget.

Systems and methods identify and provide interesting facts about an entity. An example method includes selecting documents associated with at least one unique fact trigger, the documents being from a document repository. The method also includes generating entity-sentence pairs from the documents and, for a first entity of the entities represented by the entity-sentence pairs, clustering the entity-sentence pairs for the first entity using salient terms occurring in the sentence. The method also includes determining a representative sentence for each of the clusters and providing at least one of the representative sentences in response to a query that identifies the first entity. Another example method includes determining that a query relates to an entity in a knowledge base, determining that the entity has an associated unique fact list, and providing at least one of the unique facts in the list in response to the query.

A verifying entity of a decentralized system is configured to receive information associated with at least one ledger entry of a distributed ledger of the decentralized system, to verify at least a portion of one or more computations associated with the received information, to generate evidence of at least one error in the at least a portion of the one or more computations associated with the received information, and to associate the generated evidence with a public key of the verifying entity. The decentralized system may comprise a proof of space based mining system, and the at least one ledger entry of the distributed ledger may comprise at least one proof of space for the proof of space based mining system.

An example method of displaying a data visualization includes displaying a plurality of selectable fields and receiving user selections of two different fields from the plurality of selectable fields. The method also includes generating, in accordance with the received user selections, data marks to be displayed in a data visualization, each data mark corresponding to a respective retrieved tuple of data from a multidimensional database, where (i) each data mark has an x-position defined according to data for a first field in the respective tuple and (ii) each data mark has a y-position defined according to data for a second field in the respective tuple. The method also includes displaying the data visualization that includes the generated data marks.