A memory device includes a memory cell array including a plurality of memory cells on which a programming loop is executed a plurality of times; a voltage generator configured to apply a verifying voltage to the memory cells, for verifying at least one programming state of the memory cells; and a voltage controller configured to control the voltage generator to change a level of the verifying voltage as a program loop count increases, based on temperature information about a temperature inside or outside the memory device.

A data importer for a sales prospecting system imports one or more data tables that each may include one or more records. The data importer first (a) imports a data table into an intermediate table. The data importer then (b) determines if the imported data table depends on another data table and moves one or more records from the imported data table that have no missing dependencies to a corresponding working table; and (c) determines a set of previously imported data tables that refer to the imported data table. The data importer then, for each previously imported data table, repeats (b) and (c) above.

This content development apparatus includes at least one storage medium and at least one processor. The storage medium is configured to store a plurality of resource data pertaining to content being created; and store a database pertaining to the resource data. The processor is configured to execute a plurality of editing processes; generate first information created for each type of the resource data and at least including a location of each of the resource data, and second information expressing an association between different types of the first information; store the first and the second information in the at least one storage medium; respond to a request from one of the editing processes to acquire, using a designated resource data, information indicating a different type of the resource data associated with the designated resource data; notify the editing process; and update the database.

Determine first count of first records storing first value in first field, second count of second records storing second value in second field, third count of third records storing third value in third field. Determine count threshold using first, second and third counts, dispersion measure based on dispersion of values stored in second field by first records and other dispersion measure based on other dispersion of values stored in third field by first records. Train machine-learning model to determine dispersion measure threshold based on dispersion and other dispersion measures. If first count is greater than count threshold, and dispersion measure is greater than dispersion measure threshold, create match index based on first and second fields. Receive prospective record storing first value in first field, second value in second field. Use match index to identify record storing first value in first field, second value in second field as matching prospective record.

A computer system is configured to receive a plurality of previous user selections by a user of previous database entries, each of which has as plurality of database field. The computer system is configured to determine weights for the various database fields included in the previous user selections and rank subsequent search results for a subsequent search of the database based on the determined weights, where the one or more weights affect a ranking of a search result based on a match associated with the particular database field. The computer system is further configured to receive customized search result layout settings specifying that one or more specified database fields are displayed to the user when the search results are displayed, where one or more weights for the particular database field are based on the customized search result layout settings.

A computer system is configured to receive a plurality of previous user selection by a user of previous database entries, each of which has as plurality of database field. The computer system is configured to analyze the plurality of previous user selections to identify how frequently the same values are included in the various previous database entries. The computer system is configured to determine weights for the various database fields and rank subsequent search results for a subsequent search of the database based on the determined weights.

Systems, methods, and related techniques and apparatus containing instructions which when executed by one or more computing devices for determining dataset rankings by determining, from the lineage order requirement, one or more first lineage level datasets from the collection of datasets, generating one or more first lineage level asset ranks respectively for each one of the one or more first lineage level datasets, determining at least one second lineage level dataset having an outflow to the one or more first lineage level datasets, and generating a first dataset rank for the at least one second lineage level dataset as a first function of the outflow and at least one of the one or more first lineage level asset ranks.

Embodiments perform configuration checking between data types of table fields, in order to determine mismatches therebetween. A configuration check request including a parameter identifying a system is received. A first data type is retrieved based upon the parameter. The first data type is compared with a second, different retrieved data type to determine a mismatch. In some embodiments the first data type and the second data type may be retrieved from different systems, with the mismatch revealing inter-system inconsistency. According to certain embodiments, the first data type and the second data type may be retrieved from a same system, with the mismatch revealing intra-system inconsistency. A configuration check report is generated from the mismatch and communicated to a user, for use in proactively correcting inconsistency. Embodiments may also retrieve values of the data types, as part of value help functionality.

A non-tokenized string is received. For example, a non-tokenized string could be a credit card number. The non-tokenized string is partitioned into a plurality of non-tokenized substrings. For example, if the credit card number is 16 digits long, it may be partitioned into substrings that are three, six, and seven digits in length. The non-tokenized substrings are used as an index into a plurality of lookup tables. As a result of the indexing, a plurality of tokenized substrings are retrieved. The plurality of tokenized substrings are combined into a tokenized string. The tokenized string is used as a token that represents the credit card number without disclosing the actual credit card number. The reverse of the above process can also occur.

A data analysis system is proposed for providing fine-grained low latency access to high volume input data from possibly multiple heterogeneous input data sources. The input data is parsed, optionally transformed, indexed, and stored in a horizontally-scalable key-value data repository where it may be accessed using low latency searches. The input data may be compressed into blocks before being stored to minimize storage requirements. The results of searches present input data in its original form. The input data may include access logs, call data records (CDRs), e-mail messages, etc. The system allows a data analyst to efficiently identify information of interest in a very large dynamic data set up to multiple petabytes in size. Once information of interest has been identified, that subset of the large data set can be imported into a dedicated or specialized data analysis system for an additional in-depth investigation and contextual analysis.