The present disclosure describes a system and method for optimizing SQL queries, specifically addressing challenges in handling and optimization of nested Common Table Expressions (CTEs). The system comprises a SQL optimization engine configured to receive SQL scripts from a SQL editor application and output optimized SQL to a query engine for execution on a database. The optimization engine utilizes three primary stages: a CTE normalization stage, a materialization stage, and a caching stage. The CTE normalization stage unnests nested CTEs into single-level CTEs. The materialization stage implements a materialized Create Table As Select (CTAS) strategy for materializing the base query. The caching stage enables reusability of the materialized base query across multiple queries, increasing efficiency and performance. This system provides technical solutions to enhance the capabilities of SQL engines that lack native support for nested CTEs, offering improved query performance and management of large datasets.
In some implementations, the techniques described herein relate to a system including: a database storing a dataset, the database including a persistent storage device; and a connector, the connector including a computing device configured to access data stored in the database and transfer data and commands to an external system, the connector configured to: update a deltalog responsive to a change in the dataset, the deltalog storing one or more of additions or deletions to data in the dataset, determine that a list refresh is needed for the external system, compute a changeset based on the deltalog and the dataset, the changeset representing one or more of additions or deletions to the dataset, and issue at least one application programming interface call to the external system based on the changeset to update a list of records stored by the external system.
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuéesArchitectures de systèmes de bases de données distribuées à cet effet
Disclosed are techniques for generating features to train a predictive model to predict a customer lifetime value or churn rate. In one embodiment, a method is disclosed comprising receiving a record that includes a plurality of fields and selecting a value associated with a selected field in the plurality of fields. The method then queries a lookup table comprising a mapping of values to aggregated statistics using the value and receives an aggregated statistic based on the querying. Next, the method generates a feature vector by annotating the record with the aggregated statistic. The method uses this feature vector as an input to a predictive model.
The present disclosure describes a system and method for optimizing SQL queries, specifically addressing challenges in handling and optimization of nested Common Table Expressions (CTEs). The system comprises a SQL optimization engine configured to receive SQL scripts from a SQL editor application and output optimized SQL to a query engine for execution on a database. The optimization engine utilizes three primary stages: a CTE normalization stage, a materialization stage, and a caching stage. The CTE normalization stage unnests nested CTEs into single-level CTEs. The materialization stage implements a materialized Create Table As Select (CTAS) strategy for materializing the base query. The caching stage enables reusability of the materialized base query across multiple queries, increasing efficiency and performance. This system provides technical solutions to enhance the capabilities of SQL engines that lack native support for nested CTEs, offering improved query performance and management of large datasets.
In some implementations, the techniques described herein relate to a method including: loading a current and a new model, the new model including the most recent version of the current model; computing a migration duration based on computed properties, namely the jitter in predictions between the current and the new models based on imputing the same inference data to both models; blending outputs of the current model with outputs of the new model according to weights computed for a current time step in the migration process; and serving new predictions using the new model when the migration duration expires.
The disclosed embodiments relate to devices, computer-readable media, and methods for generating training data for training an ordinal, regression-based classifier, the method including grouping client data based on client keys associated with the client data, pairwise matching records in the client data to generate feature signatures and inferring a label based on client key statuses for the pairwise-matched records, and building a training dataset from the inferred labels and feature signatures, the training dataset used to train the classifier.
Disclosed are techniques for generating features to train a predictive model to predict a customer lifetime value or churn rate. In one embodiment, a method is disclosed comprising receiving a record that includes a plurality of fields and selecting a value associated with a selected field in the plurality of fields. The method then queries a lookup table comprising a mapping of values to aggregated statistics using the value and receives an aggregated statistic based on the querying. Next, the method generates a feature vector by annotating the record with the aggregated statistic. The method uses this feature vector as an input to a predictive model.
Disclosed are techniques for generating features to train a predictive model to predict a customer lifetime value or churn rate. In one embodiment, a method is disclosed comprising receiving a record that includes a plurality of fields and selecting a value associated with a selected field in the plurality of fields. The method then queries a lookup table comprising a mapping of values to aggregated statistics using the value and receives an aggregated statistic based on the querying. Next, the method generates a feature vector by annotating the record with the aggregated statistic. The method uses this feature vector as an input to a predictive model.
Disclosed are techniques for trimming large clusters of related records. In one embodiment, a method is disclosed comprising receiving a set of clusters, each cluster in the clusters including a plurality of records. The method extracts an oversized cluster in the set of clusters and performs a breadth-first search (BFS) on the oversized cluster to generate a list of visited records. The method terminates the BFS upon determining that the size of the list of visited records exceeds a maximum size and generates a new cluster from the list of visited records and adding the new cluster to the set of clusters. By recursively performing BFS traverse over the oversized cluster and extracting smaller new clusters from it, the oversized cluster is eventually partitioned into a set of sub-clusters with the size smaller than the predefined threshold.
In some aspects, the techniques described herein relate to a method including: receiving a vector, the vector including a plurality of features related to a user; predicting a return probability for the user based on the vector using a first predictive model; adjusting the return probability using a fitted sigmoid function to generate an adjusted return probability; and predicting a lifetime value of the user using the adjusted return probability and at least one other prediction by combining the adjusted return probability and the at least one other prediction.
Disclosed are techniques for trimming large clusters of related records. In one embodiment, a method is disclosed comprising receiving a set of clusters, each cluster in the clusters including a plurality of records. The method extracts an oversized cluster in the set of clusters and performs a breadth-first search (BFS) on the oversized cluster to generate a list of visited records. The method terminates the BFS upon determining that the size of the list of visited records exceeds a maximum size and generates a new cluster from the list of visited records and adding the new cluster to the set of clusters. By recursively performing BFS traverse over the oversized cluster and extracting smaller new clusters from it, the oversized cluster is eventually partitioned into a set of sub-clusters with the size smaller than the predefined threshold.
The example embodiments are directed toward predicting the lifetime value of a user using an ensemble model. In an embodiment, a system is disclosed, including a generative model for generating a first prediction representing a first lifetime value of a user during a forecasting period and a discriminative model configured for generating a second prediction representing a second lifetime value of the user during the forecasting period. The system further includes a meta-model for receiving the first prediction and the second prediction and generating a third prediction based on the first prediction and the second prediction, the third prediction representing a third lifetime value of the user during the forecasting period.
The example embodiments are directed toward improvements in predicting an ideal audience size. In an embodiment, a method is disclosed comprising receiving a set of users associated with an object attribute; selecting samples from the set of users; computing hit rates for the samples, a respective hit rate in the hit rates computed by calculating a total number of users in a respective sample associated with an interaction associated with the object attribute; and selecting a recommended sample from the samples, the recommended sample comprising a sample having an associated hit rate that meets a preconfigured hit rate threshold.
The example embodiments are directed toward improvements in generating affinity groups. In an embodiment, a method is disclosed comprising generating probabilities of object interactions for a plurality of users, a given object recommendation ranking for a respective user comprising a ranked list of object attributes; calculating interaction probabilities for each user over a forecasting window; calculating affinity group rankings based on the probabilities of object interactions and the interaction probabilities for each user; and grouping the plurality of users based on the affinity group rankings.
The present disclosure relates to merging database tables. Systems and methods may involve performing a comparison between the first set of records and the second set of records and identifying a plurality of record pairs based on the comparison. Each record pair may comprise a record in the first set of records and a record in the second set of records. In addition, A feature signature may be generated for each record pair by comparing field values in each record pair. The feature signature may be classified to identify at least one related record pair. A merged database table may be generated such that it comprises the at least one related record pair and comprises a set of unique records among selected from the first set of records and the second set of records.
G06F 7/32 - Interclassement, c.-à-d. association de données disposées dans un ordre de succession donné sur au moins deux supports d'enregistrement en vue de préparer un support unique ou une série unique de supports présentant toutes les données originales dans l'ordre de succession donné
G06F 16/00 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet
G06F 7/14 - Interclassement, c.-à-d. association d'au moins deux séries de supports d'enregistrement, chacun étant rangé dans le même ordre de succession, en vue de former une série unique rangée dans le même ordre de succession
G06F 16/215 - Amélioration de la qualité des donnéesNettoyage des données, p. ex. déduplication, suppression des entrées non valides ou correction des erreurs typographiques
The present disclosure relates to merging database tables. Systems and methods may involve performing a comparison between the first set of records and the second set of records and identifying a plurality of record pairs based on the comparison. Each record pair may comprise a record in the first set of records and a record in the second set of records. In addition, A feature signature may be generated for each record pair by comparing field values in each record pair. The feature signature may be classified to identify at least one related record pair. A merged database table may be generated such that it comprises the at least one related record pair and comprises a set of unique records among selected from the first set of records and the second set of records.
G06F 16/00 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet
G06F 7/32 - Interclassement, c.-à-d. association de données disposées dans un ordre de succession donné sur au moins deux supports d'enregistrement en vue de préparer un support unique ou une série unique de supports présentant toutes les données originales dans l'ordre de succession donné
G06F 16/215 - Amélioration de la qualité des donnéesNettoyage des données, p. ex. déduplication, suppression des entrées non valides ou correction des erreurs typographiques
G06F 7/14 - Interclassement, c.-à-d. association d'au moins deux séries de supports d'enregistrement, chacun étant rangé dans le même ordre de succession, en vue de former une série unique rangée dans le même ordre de succession
18.
Maintaining stable record identifiers in the presence of updated data records
The present disclosure relates to optimizing one or more database tables that may include one or more redundant records. Records are clustered and assigned stable identifiers. In this manner, the underlying records within a cluster are not removed or deleted. As updates to the database are made, new clustering analyses are performed using the underlying records and any updates made. Newly identified clusters are reassigned stable identifiers.
The present disclosure relates to methods and systems for applying version control of configurations to a software application, such as, a cloud-based application. Each version may be stored as a plurality of configuration nodes within a configuration tree structure. Versions are tracked in a configuration version history. Different versions may be merged together and applied to the software application.
The present disclosure relates to evaluating whether two data records reflect the same entity using a classifier in the absence of ground truth. Without ground truth, it is difficult to determine the precision or recall of a classifier. The present disclosure generates a list comprising a series of unique feature signatures and a set of sample record pairs for each unique feature signature. In some embodiments, users may provide labels for the set of sample record pairs for each unique feature signature.
The present disclosure relates to optimizing one or more database tables that may include one or more redundant records. Records are clustered and assigned stable identifiers. In this manner, the underlying records within a cluster are not removed or deleted. As updates to the database are made, new clustering analyses are performed using the underlying records and any updates made. Newly identified clusters are reassigned stable identifiers.
The present disclosure relates to methods and systems for applying version control of configurations to a software application, such as, a cloud-based application. Each version may be stored as a plurality of configuration nodes within a configuration tree structure. Versions are tracked in a configuration version history. Different versions may be merged together and applied to the software application.
The present disclosure relates to methods and systems for applying version control of configurations to a software application, such as, a cloud-based application. Each version may be stored as a plurality of configuration nodes within a configuration tree structure. Version changes may lead to the creation or modification of configuration nodes. Configurations may be tested in a sandbox and undergo validation checks before being applied to the software application.
G06F 8/71 - Gestion de versions Gestion de configuration
G06F 21/53 - Contrôle des utilisateurs, des programmes ou des dispositifs de préservation de l’intégrité des plates-formes, p. ex. des processeurs, des micrologiciels ou des systèmes d’exploitation au stade de l’exécution du programme, p. ex. intégrité de la pile, débordement de tampon ou prévention d'effacement involontaire de données par exécution dans un environnement restreint, p. ex. "boîte à sable" ou machine virtuelle sécurisée
The present disclosure relates clustering similar data records together in a hierarchical clustering scheme. Each tier in a cluster corresponds to a minimal match score, which reflects a degree of confidence. In this respect, a higher confidence may lead to smaller sized clusters while a lower confidence may lead to larger sized clusters. Ordinal classification may be used to generate hierarchical clusters. In some embodiments, hierarchical clustering with conflict resolution is used to resolve user-defined hard conflicts in each tier of the clustering results.
The present disclosure relates to methods and systems for applying version control of configurations to a software application, such as, a cloud-based application. Each version may be stored as a plurality of configuration nodes within a configuration tree structure. Version changes may lead to the creation or modification of configuration nodes. Configurations may be tested in a sandbox and undergo validation checks before being applied to the software application.
G06F 8/71 - Gestion de versions Gestion de configuration
G06F 21/53 - Contrôle des utilisateurs, des programmes ou des dispositifs de préservation de l’intégrité des plates-formes, p. ex. des processeurs, des micrologiciels ou des systèmes d’exploitation au stade de l’exécution du programme, p. ex. intégrité de la pile, débordement de tampon ou prévention d'effacement involontaire de données par exécution dans un environnement restreint, p. ex. "boîte à sable" ou machine virtuelle sécurisée
The present disclosure relates to fuse multiple database tables together. The fields of the database tables may be normalized using semantic fields. Under a first approach, database tables are deduplicated by consolidating redundant records. This may be done by performing pairwise comparisons to identify related pairs of records and then clustering the related pairs of records. Then, the deduplicated database tables are merged by performing another pairwise comparison. Under a second approach, the database tables may be concatenated. Thereafter, records are subject to pairwise comparisons and then clustered to create a merged database table.
G06F 16/00 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet
G06F 7/14 - Interclassement, c.-à-d. association d'au moins deux séries de supports d'enregistrement, chacun étant rangé dans le même ordre de succession, en vue de former une série unique rangée dans le même ordre de succession
G06F 16/215 - Amélioration de la qualité des donnéesNettoyage des données, p. ex. déduplication, suppression des entrées non valides ou correction des erreurs typographiques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Market and advertising analysis and research; providing
information in the field of business data collection,
business organization, and business management via a
website; business monitoring and consulting services,
namely, market analysis and market studies of web sites and
applications of others to provide strategy, insight,
marketing, sales, operation, product design, particularly
specializing in the use of analytic and statistic models for
the understanding and predicting of consumers, businesses,
and market trends and actions. Providing temporary use of non-downloadable computer
software for data management; providing temporary use of
non-downloadable computer software for data analytics;
providing temporary use of non-downloadable computer
software programs and database management programs for
creating, accessing, viewing, reviewing, manipulating,
categorizing, analyzing, formatting, and preparing reports
from data and information regarding product manufacturers
and distributors, retail stores, department stores,
specialty retailers, films and television programming, media
rating information, radio airplay, book sales, music sales,
video sales, media research, sales and profit information,
retail and on-premises business locations, demographic
information, and advertising, promotion, trade and marketing
planning; providing temporary use of non-downloadable
computer software for use in analyzing advertising,
marketing, sales, and product information in connection with
marketing sales, promotion, and other marketing activities;
providing temporary use of non-downloadable computer
software for planning and scheduling advertising and
marketing in the fields of advertising and marketing
research, market research, media research and rating,
corporate and business information, travel and hospitality,
quick service restaurants, sports and entertainment,
telecommunications, financial services, and insurance;
providing temporary use of non-downloadable computer
software for planning advertising and determining and
estimating media and marketing plan effectiveness and for
measuring consumer responsiveness to advertising; providing
temporary use of non-downloadable computer software programs
that match product advertisements, the budget allocated for
advertising the individual products, and probable buying
habits of an expected audience; providing temporary use of
non-downloadable computer software tools for analyzing
cross-platform data sets for media planning; providing
temporary use of non-downloadable computer software for data
management; providing temporary use of on-line
non-downloadable software enabling users to collect,
cleanse, and prepare data for marketing and other business
purposes.
The present disclosure relates to fuse multiple database tables together. The fields of the database tables may be normalized using semantic fields. Under a first approach, database tables are deduplicated by consolidating redundant records. This may be done by performing pairwise comparisons to identify related pairs of records and then clustering the related pairs of records. Then, the deduplicated database tables are merged by performing another pairwise comparison. Under a second approach, the database tables may be concatenated. Thereafter, records are subject to pairwise comparisons and then clustered to create a merged database table.
G06F 16/00 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet
G06F 7/14 - Interclassement, c.-à-d. association d'au moins deux séries de supports d'enregistrement, chacun étant rangé dans le même ordre de succession, en vue de former une série unique rangée dans le même ordre de succession
G06F 16/215 - Amélioration de la qualité des donnéesNettoyage des données, p. ex. déduplication, suppression des entrées non valides ou correction des erreurs typographiques
The present disclosure relates clustering similar data records together in a hierarchical clustering scheme. Each tier in a cluster corresponds to a minimal match score, which reflects a degree of confidence. In this respect, a higher confidence may lead to smaller sized clusters while a lower confidence may lead to larger sized clusters. Ordinal classification may be used to generate hierarchical clusters. In some embodiments, hierarchical clustering with conflict resolution is used to resolve user-defined hard conflicts in each tier of the clustering results.
The present disclosure relates clustering similar data records together in a hierarchical clustering scheme. Each tier in a cluster corresponds to a minimal match score, which reflects a degree of confidence. A hierarchical cluster ID is generated for respective data records. The hierarchical cluster ID may be made up of a series of values, wherein each value reflects a tier within the hierarchical clustering scheme. A user may enter a partial hierarchical cluster ID to select clusters associated with a lower confidence. Thus, in some embodiments, the hierarchical cluster ID is variable in length in a manner that corresponds to the tiers in the hierarchical clustering scheme.
09 - Appareils et instruments scientifiques et électriques
35 - Publicité; Affaires commerciales
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Software; data and file management and database software; business software; business management software; computer software for analysing and processing market information; downloadable electronic reports; computer software for advertising. Market and advertising analysis and research; providing an internet website portal relating to information in the field of business data collection, business organization, and business management; market research service; analyzing and compiling business data; business monitoring and consulting services, namely, tracking web sites and applications of others to provide strategy, insight, marketing, sales, operation, product design, particularly specializing in the use of analytic and statistic models for the understanding and predicting of consumers, businesses, and market trends and actions; market research and market analysis; computerised market research; market research consultancy; business and market research; providing market research statistics; interpretation of market research data; market research data collection services; analysis of market research data; business analysis and information services, and market research; business data analysis; information and data compiling and analyzing relating to business management; business consultancy services relating to data processing; data processing for the collection of data for business purposes; none of the above being in the nature of or related to human resources consultancy. Providing temporary use of nondownloadable computer software for data management; providing temporary use of nondownloadable computer software for data analytics; providing temporary use of nondownloadable computer software programs and database management programs for creating, accessing, viewing, reviewing, manipulating, categorizing, analyzing, formatting, and preparing reports from data and information regarding product manufacturers and distributors, retail stores, department stores, specialty retailers, films and television programming, media rating information; providing temporary use of nondownloadable computer software programs and database management programs for creating, accessing, viewing, reviewing, manipulating, categorizing, analyzing, formatting, and preparing reports from data and information regarding radio airplay, book sales, music sales, video sales, media research, sales and profit information, retail and on-premises business locations, demographic information, and advertising, promotion, trade and marketing planning; providing temporary use of nondownloadable computer software for use in analyzing advertising, marketing, sales, and product information in connection with marketing sales, promotion, and other marketing activities; providing temporary use of nondownloadable computer software for planning and scheduling advertising and marketing in the fields of advertising and marketing research, market research, media research and rating, corporate and business information, travel and hospitality, quick service restaurants, sports and entertainment, telecommunications, financial services, and insurance; providing temporary use of nondownloadable computer software for planning advertising and determining and estimating media and marketing plan effectiveness and for measuring consumer responsiveness to advertising; providing temporary use of nondownloadable computer software programs that match product advertisements, the budget allocated for advertising the individual products, and probable buying habits of an expected audience; providing temporary use of nondownloadable computer software tools for analyzing cross-platform data sets for media planning; providing temporary use of nondownloadable computer software for data management; providing a website relating to technology that allows users to collect, cleanse, and prepare data for marketing and other business purposes; design and development of software for database management; computer services for the analysis of data; programming of software for market research purposes; programming of software for online advertising; providing temporary use of non-downloadable business software.
09 - Appareils et instruments scientifiques et électriques
35 - Publicité; Affaires commerciales
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Software; data and file management and database software; business software; business management software; computer software for analysing and processing market information; downloadable electronic reports; computer software for advertising. Market and advertising analysis and research; providing an internet website portal relating to information in the field of business data collection, business organization, and business management; market research service; analyzing and compiling business data; business monitoring and consulting services, namely, tracking web sites and applications of others to provide strategy, insight, marketing, sales, operation, product design, particularly specializing in the use of analytic and statistic models for the understanding and predicting of consumers, businesses, and market trends and actions; market research and market analysis; computerised market research; market research consultancy; business and market research; providing market research statistics; interpretation of market research data; market research data collection services; analysis of market research data; business analysis and information services, and market research; business data analysis; information and data compiling and analyzing relating to business management; business consultancy services relating to data processing; data processing for the collection of data for business purposes; none of the above being in the nature of or related to human resources consultancy. Providing temporary use of nondownloadable computer software for data management; providing temporary use of nondownloadable computer software for data analytics; providing temporary use of nondownloadable computer software programs and database management programs for creating, accessing, viewing, reviewing, manipulating, categorizing, analyzing, formatting, and preparing reports from data and information regarding product manufacturers and distributors, retail stores, department stores, specialty retailers, films and television programming, media rating information; providing temporary use of nondownloadable computer software programs and database management programs for creating, accessing, viewing, reviewing, manipulating, categorizing, analyzing, formatting, and preparing reports from data and information regarding radio airplay, book sales, music sales, video sales, media research, sales and profit information, retail and on-premises business locations, demographic information, and advertising, promotion, trade and marketing planning; providing temporary use of nondownloadable computer software for use in analyzing advertising, marketing, sales, and product information in connection with marketing sales, promotion, and other marketing activities; providing temporary use of nondownloadable computer software for planning and scheduling advertising and marketing in the fields of advertising and marketing research, market research, media research and rating, corporate and business information, travel and hospitality, quick service restaurants, sports and entertainment, telecommunications, financial services, and insurance; providing temporary use of nondownloadable computer software for planning advertising and determining and estimating media and marketing plan effectiveness and for measuring consumer responsiveness to advertising; providing temporary use of nondownloadable computer software programs that match product advertisements, the budget allocated for advertising the individual products, and probable buying habits of an expected audience; providing temporary use of nondownloadable computer software tools for analyzing cross-platform data sets for media planning; providing temporary use of nondownloadable computer software for data management; providing a website relating to technology that allows users to collect, cleanse, and prepare data for marketing and other business purposes; design and development of software for database management; computer services for the analysis of data; programming of software for market research purposes; programming of software for online advertising; providing temporary use of non-downloadable business software.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
(1) Market and advertising analysis and research; business data collection, business organization and business management consultancy provided on-line from an internet website portal; market research service; business data analysis services in the field of consumer behaviour; business monitoring and consulting services, namely, tracking web sites and applications of others to provide strategy, insight, marketing, sales, operation, product design, particularly specializing in the use of analytic and statistic models for the understanding and predicting of consumers, businesses, and market trends and actions
(2) Application service provider (ASP) services featuring computer software and providing online non-downloadable software, namely, computer software for data storage, backup, security; application service provider (ASP) services featuring computer software and providing online non-downloadable software, namely, computer software for data analytics in the field of consumer behaviour; providing temporary use of nondownloadable computer software programs and database management programs for creating, accessing, viewing, reviewing, manipulating, categorizing, analyzing, formatting, and preparing reports from data and information regarding product manufacturers and distributors, retail stores, department stores, specialty retailers, films and television programming, media rating information, radio airplay, book sales, music sales, video sales, media research, sales and profit information, retail and on-premises business locations, demographic information, and advertising, promotion, trade and marketing planning; providing temporary use of nondownloadable computer software for use in analyzing advertising, marketing, sales, and product information in connection with marketing sales, promotion, and other marketing activities; providing temporary use of nondownloadable computer software for planning and scheduling advertising and marketing in the fields of advertising and marketing research, market research, media research and rating, corporate and business information, travel and hospitality, quick service restaurants, sports and entertainment, telecommunications, financial services, and insurance; providing temporary use of nondownloadable computer software for planning advertising and determining and estimating media and marketing plan effectiveness and for measuring consumer responsiveness to advertising; providing temporary use of nondownloadable computer software programs that match product advertisements, the budget allocated for advertising the individual products, and probable buying habits of an expected audience; application service provider (ASP) services featuring computer software and providing online non-downloadable software, namely, computer software for media planning, namely, media placements; hosting a website that provides technology that allows users to collect, cleanse, and prepare data for marketing and other business purposes, namely, for business data collection, business organization, and business management
The present disclosure relates to dynamically merging database tables according to user specified parameters. A user may specify a threshold confidence level that relates to a likelihood that two database records represent the same real-world entity. In addition, a user may specify a merge rule such as desired fields or a manner for consolidating the variations of the information in desired fields from the related records. The original database tables are preserved so that users can iteratively create new dynamically merged database tables by varying the parameters.
G06F 16/00 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet
G06F 7/32 - Interclassement, c.-à-d. association de données disposées dans un ordre de succession donné sur au moins deux supports d'enregistrement en vue de préparer un support unique ou une série unique de supports présentant toutes les données originales dans l'ordre de succession donné
G06F 7/14 - Interclassement, c.-à-d. association d'au moins deux séries de supports d'enregistrement, chacun étant rangé dans le même ordre de succession, en vue de former une série unique rangée dans le même ordre de succession
G06F 16/215 - Amélioration de la qualité des donnéesNettoyage des données, p. ex. déduplication, suppression des entrées non valides ou correction des erreurs typographiques
The present disclosure relates to evaluating whether two data records reflect the same entity using a classifier in the absence of ground truth. Without ground truth, it is difficult to determine the precision or recall of a classifier. The present disclosure generates output data comprising a list of unique signatures generated from a set of records that are compared with each other. The output data may also comprise corresponding record pairs limited to a predetermined sample size for each unique feature signature.
The present disclosure relates to optimizing one or more database tables that may include one or more redundant records. Records are clustered and assigned stable identifiers. In this manner, the underlying records within a cluster are not removed or deleted. As updates to the database are made, new clustering analyses are performed using the underlying records and any updates made. Newly identified clusters are reassigned stable identifiers.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Market and advertising analysis and research; providing an Internet website portal featuring information in the field of business data collection, business organization, and business management; market research service; analyzing and compiling business data; business monitoring and consulting services, namely, tracking web sites and applications of others to provide strategy, insight, marketing, sales, operation, product design, particularly specializing in the use of analytic and statistic models for the understanding and predicting of consumers, businesses, and market trends and actions Providing temporary use of nondownloadable computer software for data management; providing temporary use of nondownloadable computer software for data analytics; providing temporary use of nondownloadable computer software programs and database management programs for creating, accessing, viewing, reviewing, manipulating, categorizing, analyzing, formatting, and preparing reports from data and information regarding product manufacturers and distributors, retail stores, department stores, specialty retailers, films and television programming, media rating information, radio airplay, book sales, music sales, video sales, media research, sales and profit information, retail and on-premises business locations, demographic information, and advertising, promotion, trade and marketing planning; providing temporary use of nondownloadable computer software for use in analyzing advertising, marketing, sales, and product information in connection with marketing sales, promotion, and other marketing activities; providing temporary use of nondownloadable computer software for planning and scheduling advertising and marketing in the fields of advertising and marketing research, market research, media research and rating, corporate and business information, travel and hospitality, quick service restaurants, sports and entertainment, telecommunications, financial services, and insurance; providing temporary use of nondownloadable computer software for planning advertising and determining and estimating media and marketing plan effectiveness and for measuring consumer responsiveness to advertising; providing temporary use of nondownloadable computer software programs that match product advertisements, the budget allocated for advertising the individual products, and probable buying habits of an expected audience; providing temporary use of nondownloadable computer software tools for analyzing cross-platform data sets for media planning; providing temporary use of nondownloadable computer software for data management; providing a website featuring technology that allows users to collect, cleanse, and prepare data for marketing and other business purposes
