Autonomous data analytics with real-time feature selection includes obtaining autonomous-analysis data in accordance with exploratory data portion identifiers and autonomous-analysis predicate data that indicates a predicate analytical object that represents a request for data analysis, wherein the autonomous-analysis data differs from requested results data responsive to the request for data analysis, and wherein obtaining the autonomous-analysis data includes obtaining, as exploratory data portion identifiers, a subset of data portion identifiers available in the data access and analysis system, the subset of data portion identifiers having a first defined maximum size, wherein obtaining the subset of data portion identifiers includes obtaining, by a relational analysis unit of the data access and analysis system, a first portion of the subset of data portion identifiers in accordance with the request for data analysis, data portion utility data stored in the data access and analysis system, and one or more defined utility heuristics.
G06F 18/2113 - Sélection du sous-ensemble de caractéristiques le plus significatif en classant ou en filtrant l'ensemble des caractéristiques, p. ex. en utilisant une mesure de la variance ou de la corrélation croisée des caractéristiques
2.
Natural Language To Query Language Transformation Using Prompting Templates
Natural language to query language transformation using prompting templates includes obtaining natural language input data, first embeddings data for the natural language input data, prompting template data, ranked list data, candidate prompting data, second embeddings data for the candidate prompting data, score data indicating similarity between the first embeddings data and the second embeddings data, language model input data including the natural language input data and a defined cardinality of demonstrations from the candidate prompting data, and language model generated data from a language model responsive to the language model input data, transforming the language model generated data to obtain a data query expressing the natural language input data in accordance with a defined structured query language implemented by the database, and obtaining results data generated by execution of the data query by the database.
Generating a first database query in accordance with a first structured query language includes obtaining first results data responsive to execution of the first database query by a database system, determining that the first results data indicates that the database system is incompatible with the first database query, generating first database operation mapping configuration data, wherein the first database operation mapping configuration data includes first database operation definition data describing the database operation, obtaining second database operation mapping configuration data, wherein the second database operation mapping configuration data includes the first database operation mapping configuration data mapped to second database operation definition data describing the database operation in accordance with the second structured query language, generating a second database query, obtaining second results data responsive to execution of the second database query by the database system, outputting data representing the second results data.
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable computer software using artificial intelligence (AI) for providing content, data-based insights, guidance, and suggestions to facilitate business decision-making and strategic planning, by searching, analyzing, integrating, managing, and reporting on business data from multiple sources, namely, local and cloud-based databases, online data sources, software programs, and third-party software services and platforms, and by accessing, connecting to and orchestrating external tools and software services; Downloadable computer software using artificial intelligence (AI) for searching, analyzing, and presenting data and providing data-based insights, answers, guidance, and suggestions in response to natural language queries; Downloadable database management software for use in business management intelligence; Downloadable computer software using artificial intelligence (AI) for searching, analyzing, integrating, managing, and reporting business data; Downloadable computer software using artificial intelligence (AI) for business analytics; Downloadable computer software using artificial intelligence (AI) for searching, analyzing, and presenting data in response to natural language queries; Downloadable computer software using artificial intelligence (AI) for conducting autonomous and semi-autonomous multi-step data analysis to generate answers and explanatory insights in response to natural language queries; Downloadable computer software using artificial intelligence (AI) for generating data visualizations in response to natural language queries, namely, creating charts and other graphical representations of data; Downloadable computer software using artificial intelligence (AI) for generating computer code that incorporates data analytics functionality into software applications; Downloadable computer software using artificial intelligence (AI) for natural language processing, generation, understanding, reasoning, and analysis; Downloadable computer software using artificial intelligence (AI) for simulating natural conversation, predictive analytics, data analytics, data generation, and responding to user input; Downloadable software for analytics, namely, software for use in searching, analyzing, integrating, managing, and reporting on business data from multiple sources, namely, local and cloud-based databases, online data sources, software programs, and third-party software services and platforms; Downloadable software for business intelligence, namely, software for use in searching, analyzing, integrating, managing, and reporting business data; Downloadable software for providing integrated business management intelligence by combining information from various databases and presenting it in a user interface; Downloadable software for providing explanations and insights into data analysis results, namely, identifying trends, correlations, outliers, and changes in data; Downloadable software for the graphical representation of search results and data; Downloadable software for providing interactive dashboards that display data in charts, tables and as metrics; Downloadable software for computer code generation; Downloadable software for use in natural language processing; Downloadable software for use as a data analytics engine; Downloadable software for utilizing application templates for automating the generation of custom data analysis tools for business, academic, government and scientific data analytics; Downloadable software for providing a user interface for analyzing data; Downloadable software for allowing users to create, upload, bookmark, view, annotate and share data; Downloadable software for providing actionable insights relating to business data; Downloadable software for predictive modeling; Downloadable software for creating searchable databases of information and data; Downloadable software for assisting in the creation and maintenance of data models for use in data analysis; Downloadable software for implementing software algorithms and libraries for performing data analysis and predictive forecasting; Downloadable chatbot software using artificial intelligence (AI) for simulating conversations; Downloadable computer search engine software Software as a service (SAAS) services featuring software using artificial intelligence (AI) for providing content, data-based insights, guidance, and suggestions to facilitate business decision-making and strategic planning, by searching, analyzing, integrating, managing, and reporting on business data from multiple sources, namely, local and cloud-based databases, online data sources, software programs, and third-party software services and platforms, and by accessing, connecting to and orchestrating external tools and software services; Software as a service (SAAS) services featuring software using artificial intelligence (AI) for searching, analyzing, and presenting data and providing data-based insights, answers, guidance, and suggestions in response to natural language queries; Software as a service (SAAS) services featuring software using artificial intelligence (AI) for generating computer code that incorporates data analytics functionality into software applications; Software as a service (SAAS) services featuring software using artificial intelligence (AI) for for business analytics; Software as a service (SAAS) services featuring software using artificial intelligence (AI) for searching, analyzing, integrating, managing, and reporting business data; Software as a service (SAAS) services featuring software using artificial intelligence (AI) for searching, analyzing, and presenting data in response to natural language queries; Software as a service (SAAS) services featuring software using artificial intelligence (AI) for conducting autonomous and semi-autonomous multi-step data analysis to generate answers and explanatory insights in response to natural language queries; Software as a service (SAAS) services featuring software using artificial intelligence (AI) for natural language processing, generation, understanding, reasoning and analysis; Software as a service (SAAS) services featuring software using artificial intelligence (AI) for generating data visualizations in response to natural language queries, namely, creating charts and other graphical representations of data; Hosting an on-line community web site featuring user forums, peer-to-peer collaboration, user group networking, and information sharing for customers and partners; Technical support services, namely, troubleshooting of computer software problems; Hosting an on-line community web site featuring collaboration, support, and shared resources for users of analytics software; Hosting an on-line community web site featuring user forums, collaboration, support case submission and management, and shared resources for users of analytics software; Hosting an on-line community web site featuring sharing and exchanging knowledge, best practices, and educational content in the field of data analytics and software use; Software as a service (SAAS) services featuring software for providing integrated business management intelligence by combining information from various databases and presenting it in a user interface; Software as a service (SAAS) services featuring software for analytics, namely, software for use in searching, analyzing, integrating, managing, and reporting on business data from multiple sources, namely, local and cloud-based databases, online data sources, software programs, and third-party software services and platforms; Software as a service (SAAS) services featuring software for database management; Software as a service (SAAS) services featuring software for use in integrated business management intelligence; Software as a service (SAAS) services featuring software for providing explanations and insights into data analysis results, namely, identifying trends, correlations, outliers, and changes in data; Software as a service (SAAS) services featuring software for computer code generation; Software as a service (SAAS) services featuring software for use in natural language processing; Software as a service (SAAS) services featuring software for utilizing application templates for automating the generation of custom data analysis tools for business, academic, government and scientific data analytics; Software as a service (SAAS) services featuring software for providing a user interface for analyzing data; Software as a service (SAAS) services featuring software for allowing users to create, upload, bookmark, view, annotate and share data; Software as a service (SAAS) services featuring software for providing actionable insights relating to business data; Software as a service (SAAS) services featuring software for predictive modeling; Software as a service (SAAS) services featuring software for providing interactive dashboards that display data in charts, tables and as metrics; Software as a service (SAAS) services featuring software for use in the graphical representation of search results and data; Software as a service (SAAS) services featuring software for business intelligence, namely, software for use in searching, analyzing, integrating, managing, and reporting business data; Software as a service (SAAS) services featuring software for data analytics, namely data engine software; Software as a service (SAAS) services featuring software for use as a search engine; Software as a service (SAAS) services featuring software for creating searchable databases of information and data; Software as a service (SAAS) services featuring software for assisting in the creation and maintenance of data models for use in data analysis; Software as a service (SAAS) services featuring software for implementing software algorithms and libraries for performing data analysis and predictive forecasting; Information technology consultancy relating to installation, maintenance and repair of computer software; Computer software consulting; Consulting in the field of configuration management for computer hardware and software; Providing temporary use of online non-downloadable computer chatbot software for simulating conversations; Artificial intelligence as a service (AIAAS) services featuring software using artificial intelligence for data analytics; Artificial intelligence as a service (AIAAS) services featuring software using artificial intelligence (AI) for simulating natural conversation, predictive analytics, data analytics, and data generation, and responding to user input; Artificial intelligence as a service (AIAAS) services featuring software using artificial intelligence (AI) for analyzing data and interacting with humans
Obtaining results data using agentic analysis includes obtaining, by a data access and analysis system, natural language input data expressing an input analytical request related to data stored in a database accessible by the data access and analysis system, performing iterations of agentic analysis, wherein agentic analysis includes obtaining, from a language model, responsive to first language model input data generated in accordance with the natural language input data, first language model generated output, wherein, at least one iteration includes obtaining, by the data access and analysis system, in accordance with the first language model generated output, structured data generated by the database executing a data query automatically generated by the data access and analysis system in response to the first language model generated output, and outputting data for presenting at least a portion of the first language model generated output corresponding to a sequentially last iteration.
09 - Appareils et instruments scientifiques et électriques
41 - Éducation, divertissements, activités sportives et culturelles
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable database management software for use in business management intelligence; Downloadable computer software using artificial intelligence (AI) for searching, analyzing, integrating, managing, and reporting business data; Downloadable computer software using artificial intelligence (AI) for business analytics; Downloadable software for analytics, namely, software for use in searching, analyzing, integrating, managing, and reporting on business data from multiple sources, namely, local and cloud-based databases, online data sources, software programs, and third-party software services and platforms; Downloadable software for business intelligence, namely, software for use in searching, analyzing, integrating, managing, and reporting business data; Downloadable software for providing integrated business management intelligence by combining information from various databases and presenting it in a user interface; Downloadable computer software using artificial intelligence (AI) for searching, analyzing, and presenting data in response to natural language queries; Downloadable software for providing explanations and insights into data analysis results, namely, identifying trends, correlations, outliers, and changes in data; Downloadable software for the graphical representation of search results and data; Downloadable software for providing interactive dashboards that display data in charts, tables and as metrics; Downloadable computer software using artificial intelligence (AI) for conducting autonomous and semi-autonomous multi-step data analysis to generate answers and explanatory insights in response to natural language queries; Downloadable computer software using artificial intelligence (AI) for generating data visualizations in response to natural language queries, namely, creating charts and other graphical representations of data; Downloadable computer software using artificial intelligence (AI) for generating computer code that incorporates data analytics functionality into software applications; Downloadable software for computer code generation; Downloadable software for use in natural language processing; Downloadable computer search engine software; Downloadable software for use as a data analytics engine; Downloadable software for utilizing application templates for automating the generation of custom data analysis tools for business, academic, government and scientific data analytics; Downloadable software for providing a user interface for analyzing data; Downloadable software for allowing users to create, upload, bookmark, view, annotate and share data; Downloadable software for providing actionable insights relating to business data; Downloadable software for predictive modeling; Downloadable software for creating searchable databases of information and data; Downloadable software for assisting in the creation and maintenance of data models for use in data analysis; Downloadable software for implementing software algorithms and libraries for performing data analysis and predictive forecasting; Downloadable chatbot software using artificial intelligence (AI) for simulating conversations; Downloadable computer software using artificial intelligence (AI) for for natural language processing, generation, understanding, reasoning, and analysis; Downloadable computer software using artificial intelligence (AI) for simulating natural conversation, predictive analytics, data analytics, data generation, and responding to user input Arranging and conducting of business conferences in the field of computer software, information technology, artificial intelligence and analytics; Training services in the field of computer software; Educational services, namely, providing webinars, tutorials, workshops and training in the fields of computer software, data analytics, data visualization and business intelligence; Educational services, namely, conducting online courses in the fields of computer software, data analytics, data visualization, and business intelligence and distribution of training materials in connection therewith Hosting an on-line community web site featuring user forums, peer-to-peer collaboration, user group networking, and information sharing for customers and partners; Technical support services, namely, troubleshooting of computer software problems; Hosting an on-line community web site featuring collaboration, support, and shared resources for users of analytics software; Hosting an on-line community web site featuring user forums, collaboration, support case submission and management, and shared resources for users of analytics software; Hosting an on-line community web site featuring sharing and exchanging knowledge, best practices, and educational content in the field of data analytics and software use; Software as a service (SAAS) services featuring software for providing integrated business management intelligence by combining information from various databases and presenting it in a user interface; Software as a service (SAAS) services featuring software for analytics, namely, software for use in searching, analyzing, integrating, managing, and reporting on business data from multiple sources, namely, local and cloud-based databases, online data sources, software programs, and third-party software services and platforms; Software as a service (SAAS) services featuring software for database management software for use in integrated business management intelligence; Software as a service (SAAS) services featuring software using artificial intelligence (AI) for business analytics; Software as a service (SAAS) services featuring software using artificial intelligence (AI) for searching, analyzing, integrating, managing, and reporting business data; Software as a service (SAAS) services featuring software using artificial intelligence (AI) for searching, analyzing, and presenting data in response to natural language queries; Software as a service (SAAS) services featuring software using artificial intelligence (AI) for conducting autonomous and semi-autonomous multi-step data analysis to generate answers and explanatory insights in response to natural language queries; Software as a service (SAAS) services featuring software for providing explanations and insights into data analysis results, namely, identifying trends, correlations, outliers, and changes in data; Software as a service (SAAS) services featuring software for computer code generation; Software as a service (SAAS) services featuring software for use in natural language processing; Software as a service (SAAS) services featuring software that utilizes application templates for automating the generation of custom data analysis tools for business, academic, government and scientific data analytics; Software as a service (SAAS) services featuring software for providing a user interface for analyzing data; Software as a service (SAAS) services featuring software for allowing users to create, upload, bookmark, view, annotate and share data; Software as a service (SAAS) services featuring software for providing actionable insights relating to business data; Software as a service (SAAS) services featuring software for predictive modeling; Software as a service (SAAS) services featuring software for providing interactive dashboards that display data in charts, tables and as metrics; Software as a service (SAAS) services featuring software for use in the graphical representation of search results and data; Software as a service (SAAS) services featuring software for business intelligence, namely, software for use in searching, analyzing, integrating, managing, and reporting business data; Software as a service (SAAS) services featuring software for data analytics, namely data engine software; Software as a service (SAAS) services featuring software using artificial intelligence (AI) for generating data visualizations in response to natural language queries, namely, creating charts and other graphical representations of data; Software as a service (SAAS) services featuring software using artificial intelligence (AI) for generating computer code that incorporates data analytics functionality into software applications; Software as a service (SAAS) services featuring software for use as a search engine; Software as a service (SAAS) services featuring software for creating searchable databases of information and data; Software as a service (SAAS) services featuring software for assisting in the creation and maintenance of data models for use in data analysis; Software as a service (SAAS) services featuring software for implementing software algorithms and libraries for performing data analysis and predictive forecasting; Information technology consultancy relating to installation, maintenance and repair of computer software; Computer software consulting; Consulting in the field of configuration management for computer hardware and software; Providing temporary use of online non-downloadable computer chatbot software for simulating conversations; Artificial intelligence as a service (AIAAS) services featuring software using artificial intelligence for data analytics; Software as a service (SAAS) services featuring software using artificial intelligence (AI) for natural language processing, generation, understanding, reasoning and analysis; Artificial intelligence as a service (AIAAS) services featuring software using artificial intelligence (AI) for simulating natural conversation, predictive analytics, data analytics, and data generation, and responding to user input; Artificial intelligence as a service (AIAAS) services featuring software using artificial intelligence (AI) for analyzing data and interacting with humans
Automatic target visualization adaptation includes obtaining a natural language string expressing a request for data, obtaining large language model generated structured query language data expressing the natural language string, obtaining, by transforming the large language model generated structured query language data, first resolved request data, obtaining target-parameters data for a target visualization type, obtaining target-parameters deficit data, obtaining a co-occurring parameter, obtaining, by transforming a combination of the first resolved request data and the co-occurring parameter, an automatically generated data query, obtaining from a database, results data generated by execution of the automatically generated data query by the database, and outputting data for presenting the results data in accordance with the target visualization type.
Interaction data integrity protection in a data access and analysis system includes obtaining user account data including a user account identifier and a public key, receiving, from a user device, first system interaction data indicating a request to interact with the data access and analysis system, sending, to the user device, first interaction response data including interaction challenge data, wherein the interaction challenge data includes a system interaction identifier that uniquely identifies the request to interact with the data access and analysis system, receiving, from the user device, second system interaction data signed with a private key of the asymmetric key pair, wherein the second system interaction data includes the system interaction identifier, validating the second system interaction data using the public key, and recording validated system interaction data obtained by the validating, the validated system interaction data including the second system interaction data signed with the private key.
G06F 21/64 - Protection de l’intégrité des données, p. ex. par sommes de contrôle, certificats ou signatures
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
9.
Mapping Natural Language To Queries Using A Query Grammar
Systems and methods for mapping natural language to queries using a query grammar are described. For example, methods may include generating, based on a string, a set of tokens of a database syntax; generating a query graph for the set of tokens from a finite state machine representing a query grammar, wherein nodes of the finite state machine represent token types, directed edges of the finite state machine represent valid transitions between token types in the query grammar, vertices of the query graph correspond to respective tokens of the set of tokens, and directed edges of the query graph represent a transition between two tokens in a sequencing of the tokens; determining, based on a tour of the query graph, a sequence of the tokens in the set of tokens, forming a database query; and invoking a search of a database using a query based on the database query to obtain search results.
Automatic data-analysis formula phrase generation includes obtaining a natural language string expressing a request for an automatically generated data-analysis formula phrase, obtaining first prompt data including a first proper subset of defined data-analysis formula phrases previously defined in a data access and analysis system in accordance with a defined data-analysis-formula grammar and associated with defined data-analysis formula phrase categories, obtaining first large language model generate data responsive to the first prompt data, obtaining a proper subset of the defined data-analysis formula phrase categories, obtaining second prompt data including a second proper subset of the defined data-analysis formula phrases identified in accordance with the proper subset of the defined data-analysis formula phrase categories, and obtaining second large language model generated data responsive to the second large language model input data that includes the automatically generated data-analysis formula phrase.
Semantic analysis to resolve ambiguous user input data with respect to a request for data includes identifying tokens based on the text string, identifying a tables in a database, wherein a token indicates a column from a table and a token indicates a column from another table, identifying candidate join paths for joining tables, obtaining respective candidate results using the candidate join paths, outputting combined candidate results including values from the respective candidate results, obtaining second user input data indicating a selected value, identifying the request for data as unambiguous and identifying a selected join path based on the selected value, and, in response to identifying the request for data as unambiguous, outputting data responsive to the request for data using the selected join path.
G06F 16/38 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
Updating a constituent-data index includes determining whether sampling is currently contraindicated for the column, in response to determining that sampling is currently contraindicated for the column, omitting sampling the column, in response to determining that sampling is currently other than contraindicated for the column, sampling the column, determining whether updating the constituent-data index is currently contraindicated for the column, in response to determining that updating the constituent-data index is currently contraindicated for the column, omitting updating the constituent-data index with respect to the column, and in response to determining that updating is currently other than contraindicated for the column, updating the constituent-data index with respect to the column.
G06F 16/20 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet de données structurées, p. ex. de données relationnelles
G06F 16/22 - IndexationStructures de données à cet effetStructures de stockage
G06F 16/2458 - Types spéciaux de requêtes, p. ex. requêtes statistiques, requêtes floues ou requêtes distribuées
Updating a constituent-data index includes determining whether sampling is currently contraindicated for the column, in response to determining that sampling is currently contraindicated for the column, omitting sampling the column, in response to determining that sampling is currently other than contraindicated for the column, sampling the column, determining whether updating the constituent-data index is currently contraindicated for the column, in response to determining that updating the constituent-data index is currently contraindicated for the column, omitting updating the constituent-data index with respect to the column, and in response to determining that updating is currently other than contraindicated for the column, updating the constituent-data index with respect to the column.
G06F 16/20 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet de données structurées, p. ex. de données relationnelles
G06F 16/22 - IndexationStructures de données à cet effetStructures de stockage
G06F 16/2458 - Types spéciaux de requêtes, p. ex. requêtes statistiques, requêtes floues ou requêtes distribuées
14.
Token based dynamic data indexing with integrated security
Semantic analysis to resolve ambiguous user input data with respect to a request for data includes identifying tokens based on the text string, identifying a tables in a database, wherein a token indicates a column from a table and a token indicates a column from another table, identifying candidate join paths for joining tables, obtaining respective candidate results using the candidate join paths, outputting combined candidate results including values from the respective candidate results, obtaining second user input data indicating a selected value, identifying the request for data as unambiguous and identifying a selected join path based on the selected value, and, in response to identifying the request for data as unambiguous, outputting data responsive to the request for data using the selected join path.
G06F 16/38 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
A visualization data is obtained for an answer object. A generation-identifier is associated with the visualization data. The generation-identifier corresponds to access control data associated with a first user. A snapshot of the first visualization data is received from a user device of the first user. The first snapshot is obtained using instructions that, when executed at the user device, obtain the first snapshot as the first visualization data is displayed at the user device of the first user. An association is stored between the generation-identifier and the snapshot. The answer object is then identified responsive to data expressing a usage intent received from a device of a second user. Responsive to access control data associated with the second user matching the generation-identifier, instructions for rendering the first snapshot at the device of the second user are output.
A distributed database that includes multiple database instances receives a data-query that includes an aggregation clause on a first column of a table. The table is partitioned into shards according to a sharding criterion based on the first column such that all rows having the same value for the first column are included in the same shard. The shards are distributed to the multiple database instances. Respective intermediate results are received from at least some of the database instances. Each intermediate result received from a respective database instance that includes a respective shard aggregates values of the first column in the respective shard. The respective intermediate results are combined to obtain a final result of the data-query. The final result is then output.
G06F 16/22 - IndexationStructures de données à cet effetStructures de stockage
G06F 16/248 - Présentation des résultats de requêtes
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
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable computer software using artificial intelligence (AI) for searching, analyzing, integrating, managing, visualizing, and reporting business data sets using natural language queries; Downloadable computer software using artificial intelligence (AI) for searching, analyzing, integrating, managing, visualizing, and reporting business data sets by generating data set query instructions based on user natural language queries; Downloadable chatbot software using artificial intelligence (AI) for searching, analyzing, integrating, managing, visualizing, and reporting business data; Downloadable chatbot software using artificial intelligence (AI) for enabling users to query data sets using natural language; Downloadable chatbot software using artificial intelligence (AI) for analyzing business data to provide actionable insights relating to business data; none of the aforementioned goods related to music, podcasts, or entertainment Software as a service (SAAS) services featuring software using artificial intelligence (AI) for searching, analyzing, integrating, managing, visualizing, and reporting business data sets using natural language queries; Software as a service (SAAS) services featuring software using artificial intelligence (AI) for searching, analyzing, integrating, managing, visualizing, and reporting business data sets by generating data set query instructions based on user natural language queries; Providing temporary use of online non-downloadable chatbot software using artificial intelligence (AI) for searching, analyzing, integrating, managing, visualizing, and reporting business data; Providing temporary use of online non-downloadable chatbot software using artificial intelligence (AI) for enabling users to query data sets using natural language; Providing temporary use of online non-downloadable chatbot software using artificial intelligence (AI) for analyzing business data to provide actionable insights relating to business data; none of the aforementioned services related to music, podcasts, or entertainment
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable computer software using artificial intelligence (AI) for searching, analyzing, integrating, managing, visualizing, and reporting business data sets using natural language queries; Downloadable computer software using artificial intelligence (AI) for searching, analyzing, integrating, managing, visualizing, and reporting business data sets by generating data set query instructions based on user natural language queries; Downloadable chatbot software using artificial intelligence (AI) for searching, analyzing, integrating, managing, visualizing, and reporting business data; Downloadable chatbot software using artificial intelligence (AI) for enabling users to query data sets using natural language; Downloadable chatbot software using artificial intelligence (AI) for providing actionable insights relating to business data; none of the aforementioned goods related to music, podcasts, or entertainment Software as a service (SAAS) services featuring software using artificial intelligence (AI) for searching, analyzing, integrating, managing, visualizing, and reporting business data sets using natural language queries; Software as a service (SAAS) services featuring software using artificial intelligence (AI) for searching, analyzing, integrating, managing, visualizing, and reporting business data sets by generating data set query instructions based on user natural language queries; Providing temporary use of online non-downloadable chatbot software using artificial intelligence (AI) for searching, analyzing, integrating, managing, visualizing, and reporting business data; Providing temporary use of online non-downloadable chatbot software using artificial intelligence (AI) for enabling users to query data sets using natural language; Providing temporary use of online non-downloadable chatbot software using artificial intelligence (AI) for providing actionable insights relating to business data; none of the aforementioned services related to music, podcasts, or entertainment
Obtaining artificial data analysis request data includes obtaining triggering event data including at least one primary data source identifier, obtaining large language model input data for obtaining the artificial data analysis request, obtaining large language model generated data output by a large language model in response to the large language model input data, wherein the large language model generated data includes at least one candidate artificial data analysis request tuple, and validating the large language model generated data, wherein validating the large language model generated data includes validating the at least one candidate artificial data analysis request tuple in accordance with a defined data-analytics grammar implemented by the data access and analysis system.
Generating a first data query in accordance with a first structured query language includes obtaining first results data responsive to execution of the first data query by a database system, determining that the first result data indicates that the database system is incompatible with the first data query, generating first database operation mapping configuration data, wherein the first database operation mapping configuration data includes first database operation definition data describing the database operation, obtaining second database operation mapping configuration data, wherein the second database operation mapping configuration data includes the first database operation mapping configuration data mapped to second database operation definition data describing the database operation in accordance with the second structured query language, generating a second data query, obtaining second results data responsive to execution of the second data query by the database system, outputting data representing the second results data.
Identifying join candidate includes identifying pairs of columns within a data source. Each pair includes a first column in a first table of the data source and a second column of a second table of the data source. For each pair, a casting similarity index is assigned from a predefined scale that includes a first casting similarity level indicative of a very low casting similarity level, where the casting similarity index is determined based on an extent to which data values from the first column are convertable to match a data type of the second column within the each pair, and where the very low casting similarity level is assigned to the each pair in a case that the first column has a boolean type and the second column has a float type. The join candidate is presented on a device of a user for selection by the user.
Automatic data modeling in includes identifying an analytical object in response to first data expressing usage intent, generating an analytical model generation data query for the analytical object, obtaining a trained analytical model generated in accordance with the analytical model generation query and trained using results data obtained in accordance with the analytical object, generating a resolved request representing second data expressing usage intent and indicating a request for results data obtained using the trained analytical model, generating an analytical model results data query for obtaining the results data in accordance with the trained analytical model and the analytical object, and outputting data for presenting a visualization of the results data obtained by executing the analytical model results data query, wherein a first portion of the results data corresponds with the analytical object and a second portion of the results data corresponds with the trained analytical model.
Systems and methods for query generation based on a logical data model with one-to-one joins are described. For example, methods may include accessing a join graph representing tables in a database; receiving a first query; selecting a connected subgraph of the join graph that includes the two or more tables referenced in the first query; accessing an indication that a directed edge of the connected subgraph corresponds to a one-to-one join; modifying the connected subgraph based on the indication to obtain a modified subgraph; generating one or more leaf queries based on the modified subgraph; generating a query graph that specifies joining of results from queries based on the one or more leaf queries; invoking a transformed query on the database that is based on the query graph and the queries based on the one or more leaf queries.
G06F 15/16 - Associations de plusieurs calculateurs numériques comportant chacun au moins une unité arithmétique, une unité programme et un registre, p. ex. pour le traitement simultané de plusieurs programmes
G06F 16/21 - Conception, administration ou maintenance des bases de données
G06F 16/22 - IndexationStructures de données à cet effetStructures de stockage
Operating a low-latency database analysis system with phrase translation may include obtaining a locale-specific phrase localization rule and a canonical phrase localization rule for a phrase, generating a locale-specific index and a locale-specific finite state machine for the locale using the localization definition data and a canonical finite state machine, generating a resolved-request by obtaining a locale-specific token representing locale-specific input data by traversing the locale-specific index, obtaining a canonical token associated with locale-specific token, obtaining a locale-specific phrase by traversing the locale-specific finite state machine, obtaining a canonical phrase corresponding to the locale-specific phrase, the canonical phrase including the canonical token, generate a data-query based on the canonical phrase, obtaining results data responsive to the data expressing the usage intent by executing a query corresponding to the data-query by an in-memory database of the low-latency database analysis system, and outputting the results data for presentation to a user.
First images that are screenshots from a first version of a software component are obtained. Second images that are screenshots from a second version are obtained. A collection of image deviations that includes pair-wise image deviations between pairs of images are identified. A pair of images includes a first image from the first images and a corresponding second image from the second images. An image deviation indicates a portion of the second image identified as differing from a spatially corresponding portion of the first image. The image deviations are grouped into deviation groups. At least some of the second images are associated with at least some of the deviation groups. A subset of the second images corresponding to a deviation group is output responsive to a selection of an indication of the deviation group.
G06F 18/22 - Critères d'appariement, p. ex. mesures de proximité
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
G06F 8/71 - Gestion de versions Gestion de configuration
G06F 18/23213 - Techniques non hiérarchiques en utilisant les statistiques ou l'optimisation des fonctions, p. ex. modélisation des fonctions de densité de probabilité avec un nombre fixe de partitions, p. ex. K-moyennes
A current set of context features for a database query that is associated with a string is identified. The database query includes a sequence of tokens of a database syntax, and the current set of context features includes words from the string and tokens from the database query. An inference record is selected from an inference store based on a comparison of the current set of context features to context features of inference records in the inference store. The database query is modified using a resolution of the inference record to obtain an inferred database query. The resolution includes one or more tokens of the database syntax. A search of a database is invoked using a query based on the inferred database query to obtain search results.
Intent-resolution using a phrase index may include obtaining data expressing a usage intent, the data expressing the usage intent including an unresolved data portion, identifying a phrase fragment based on the data expressing the usage intent and a defined phrase pattern, the phrase fragment including the unresolved data portion, identifying, by a processor, an indexed phrase as by searching a phrase index based on the phrase fragment, wherein the indexed phrase at least partially matches the phrase fragment in accordance with the defined phrase pattern, in response to identifying the indexed phrase, obtaining a resolved request representing the data expressing the usage intent in accordance with the indexed phrase, generating a data query in accordance with the resolved request and a defined structured query language, obtaining results data responsive to execution of the data query by a database that implements the defined structured query language, and outputting the results data.
A shutdown criterion is determined to be met with respect to a cluster. Responsive to determining that the shutdown criterion is met, a request router is configured to route requests addressed to the cluster to a destination that indicates that the cluster is unavailable. A request to shut down the cluster is transmitted. A request to start the cluster is received. The request is initiated via the destination that indicates that the cluster is unavailable. In response to receiving the request to start the cluster, the cluster is started and the request router is configured to direct, to the cluster, requests addressed to the cluster.
Generating object morphisms during object search includes obtaining object-search request data, wherein the object-search request data includes object-search terms, obtaining resolved-request data representing the object-search terms, determining that a first analytical object partially consistent with the resolved-request data is available, wherein the first analytical object is consistent with a first portion of the resolved-request data, generating candidate object-morphism data with respect to the first analytical object in accordance with a second portion of the resolved-request data, outputting object-search response data including the candidate object-morphism data for presentation to a user, obtaining data indicating a selected object morphism from the candidate object-morphism data, generating a second analytical object in accordance with the first analytical object and the selected object morphism, wherein the second analytical object differs from the first analytical object, and outputting response data including the second analytical object for presentation to the user.
A metric predictor model for predicting values of a data metric is selected. The metric predictor model is trained using historical data related to the data metric. A predicted value of the data metric is obtained using the metric predictor model. A current value of the data metric is obtained using data other than the historical data. A difference between the predicted value and the current value is determined to meet a reporting criterion. In response to determining that the difference meets the reporting criterion, a notification descriptive of the difference is output.
Operating a low-latency database analysis system using domain-specific chronometry may include obtaining chronometry configuration data including chronometric instance data describing an instance of a chronometric unit of a domain-specific chronometry dataset that describes an era, such that the chronometry configuration data includes respective chronometric instance data describing each instance of the first chronometric unit of the domain-specific chronometry dataset for the era of the domain-specific chronometry dataset, generating, in the low-latency database analysis system, a domain-specific chronometry dataset in accordance with the chronometry configuration data, such that the domain-specific chronometry dataset describes a chronometric unit such that a temporal location expressed with reference to the chronometric unit and indicative of an epoch value differs from a temporal location indicative of the epoch value and expressed in accordance with a canonical chronometry, and storing the domain-specific chronometry dataset in the low-latency database analysis system.
G06F 16/00 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet
G06F 16/2458 - Types spéciaux de requêtes, p. ex. requêtes statistiques, requêtes floues ou requêtes distribuées
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
A request for database results is received from a query coordinator at a database instance of a distributed database. The request includes a query execution instruction of a query plan and an indication of override instructions corresponding to the query execution instruction. The override instructions are such that they do not modify the query plan. The database instance includes the override instructions in a set of high-level language query instructions. The database instance performs just-in-time compiling of the set of high-level language query instructions to obtain a machine language query for performing the query execution instruction of the query plan. The database instance executes the machine language query to obtain the database results. The database instance then transmits the database results to the query coordinator.
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
33.
Record-replay testing framework with machine learning based assertions
A replay tool configured in a learning mode is used to replay a recorded interaction workflow to obtain respective learning-mode test data responsive to a request from a client device to a server. A baseline response template is obtained from the respective learning-mode test data. A baseline response time of the request is also obtained from the respective learning-mode test data. The recorded interaction workflow is replayed in a testing mode to obtain testing-mode test data. Responsive to determining that a response body included in the testing-mode test data is inconsistent with the baseline response template, a first anomaly message is output. Responsive to determining that the response time included in the testing-mode test data is not within a tolerance of the baseline response time, a second anomaly message is output.
Generating object morphisms during object search includes obtaining object-search request data, wherein the object-search request data includes object-search terms, obtaining resolved-request data representing the object-search terms, determining that a first analytical object partially consistent with the resolved-request data is available, wherein the first analytical object is consistent with a first portion of the resolved-request data, generating candidate object-morphism data with respect to the first analytical object in accordance with a second portion of the resolved-request data, outputting object-search response data including the candidate object-morphism data for presentation to a user, obtaining data indicating a selected object morphism from the candidate object-morphism data, generating a second analytical object in accordance with the first analytical object and the selected object morphism, wherein the second analytical object differs from the first analytical object, and outputting response data including the second analytical object for presentation to the user.
Query planning in a distributed database that includes a table partitioned into shards according to a sharding criterion and distributed to database instances includes receiving a data-query. The data-query includes a “distinct count” clause on a first column and a “group by” clause on least a second column. A query plan is formulated to include respective instructions for converting, at at least some of the database instances, distinct values of the first column grouped by values of the second column into a count of the distinct values grouped by the values of the second column to obtain respective intermediate results; instructions for receiving the respective intermediate results from at least a subset of the at least some of the database instances; and instructions for concatenating the respective intermediate results using a summing operation to obtain the first “distinct count” of the first column grouped by the second column.
G06F 16/22 - IndexationStructures de données à cet effetStructures de stockage
G06F 16/248 - Présentation des résultats de requêtes
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
Obtaining an approximate unique count for a column from a table from a database includes, generating, for a value from an unevaluated row, a hash value in a defined range of hash values, determining a cardinality of leading zeros in the hash value, identifying a bucket with respect to the hash value from a plurality of buckets corresponding to the defined range of hash values, wherein the buckets from the plurality of buckets correspond with respective non-overlapping portions of the defined range of hash values, such that the hash value is in the portion of the defined range of hash values corresponding to the bucket, and appending to an unsorted sparse representation a bucket identifier for the bucket and the cardinality of the leading zeros, and, in response to a determination that unevaluated rows are unavailable in the table, determining the approximate unique count using the unsorted sparse representation.
G06F 12/0864 - Adressage d’un niveau de mémoire dans lequel l’accès aux données ou aux blocs de données désirés nécessite des moyens d’adressage associatif, p. ex. mémoires cache utilisant des moyens pseudo-associatifs, p. ex. associatifs d’ensemble ou de hachage
G06F 16/901 - IndexationStructures de données à cet effetStructures de stockage
G06F 9/50 - Allocation de ressources, p. ex. de l'unité centrale de traitement [UCT]
Low-latency autonomous-analysis includes obtaining data expressing a usage intent with respect to a low-latency database analysis system that intent omits data corresponding to user input expressly requesting low-latency autonomous-analysis, obtaining requested results data based on the data expressing the usage intent, outputting requested visualization data representing at least a portion of the requested results data for presentation to a user, and, in response to outputting the requested visualization data, obtaining low-latency autonomous-analysis data by performing low-latency autonomous-analysis based on the data expressing the usage intent by identifying an autonomous-analysis predicate based on the requested visualization data, obtaining a defined autonomous-analysis latency constraint, obtaining the low-latency autonomous-analysis data based on the autonomous-analysis predicate in accordance with the defined autonomous-analysis latency constraint, such that the low-latency autonomous-analysis data differs from the requested results data, and outputting at least a portion of the low-latency autonomous-analysis data for presentation to a user.
A request to execute a database command is transmitted from a device to a database command execution device. The request includes a first indicator of a first set of tokens available at the device at a time that the request is transmitted. A response to the request is received. The response includes a second indicator of a second set of tokens available at the database command execution device at a time that the request is received at the database command execution device. Responsive to a determination that the second indicator is different from the first indicator, a request for updated tokens is transmitted to the database command execution device. The first set of tokens is updated based on a received response to the request for the updated tokens. A list of tokens the updated first set of tokens that match a partial token received as an input is output.
A first replay log is replayed to generate a first replay result. Replaying the first replay log includes replacing, in the first replay result, a first value of a first field included in a first command in the first replay log with a first hash value responsive to a determination that the first field is not utilized as a condition in at least one command included in the first replay log. A second replay log is replayed to generate a second replay result. The first replay result and the second replay result are compared to verify that the first replay log and the second replay log are equivalent.
Semantic analysis to resolve ambiguous user input data with respect to a request for data includes identifying tokens based on the text string, identifying a tables in a database, wherein a token indicates a column from a table and a token indicates a column from another table, identifying candidate join paths for joining tables, obtaining respective candidate results using the candidate join paths, outputting combined candidate results including values from the respective candidate results, obtaining second user input data indicating a selected value, identifying the request for data as unambiguous and identifying a selected join path based on the selected value, and, in response to identifying the request for data as unambiguous, outputting data responsive to the request for data using the selected join path.
G06F 16/38 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
Automatic data modeling in a low-latency data access and analysis system includes identifying an analytical-object in response to first data expressing usage intent, generating an analytical model generation data-query for the analytical-object, obtaining a trained analytical model generated in accordance with the analytical model generation query and trained using results data obtained in accordance with the analytical-object, generating a resolved-request representing second data expressing usage intent and indicating a request for results data obtained using the trained analytical model, generating an analytical model results data-query for obtaining the results data in accordance with the trained analytical model and the analytical-object, and outputting data for presenting a visualization of the results data obtained by executing the analytical model results data-query, wherein a first portion of the results data corresponds with the analytical-object and a second portion of the results data corresponds with the trained analytical model.
Systems and methods for conversational user experiences and conversational database analysis disclosed herein improve the efficiency and accessibility of low-latency database analytics. The method may include obtaining data expressing a usage intent with respect to the low-latency database analysis system, wherein the data expressing the usage intent includes a current request string expressed in a natural language, a current context associated with the current request string, and a previously generated context associated with a previously generated resolved-request, identifying, from the current request string, a conversational phrase corresponding to a conversational phrase pattern from a defined set of conversational phrase patterns, generating a resolved-request based on the identified conversational phrase, including the resolved-request in the current context, obtaining results data responsive to the resolved-request from a distributed in-memory database, generating a response including the results data and the current context, and outputting the response.
Intent-resolution using a phrase index may include obtaining data expressing a usage intent, the data expressing the usage intent including an unresolved data portion, identifying a phrase fragment based on the data expressing the usage intent and a defined phrase pattern, the phrase fragment including the unresolved data portion, identifying, by a processor, an indexed phrase as by searching a phrase index based on the phrase fragment, wherein the indexed phrase at least partially matches the phrase fragment in accordance with the defined phrase pattern, in response to identifying the indexed phrase, obtaining a resolved request representing the data expressing the usage intent in accordance with the indexed phrase, generating a data query in accordance with the resolved request and a defined structured query language, obtaining results data responsive to execution of the data query by a database that implements the defined structured query language, and outputting the results data.
A method and system may be implemented for automatically analyzing data in a database. A method for use in a low-latency database analysis system may include generating a schema. The schema may be based on a portion of an external database. The method may include storing the schema in an in-memory database. The method may include receiving a data-query. The method may include generating a resolved-request. The resolved-request may be based on the data-query and the stored schema. The stored schema may be used for executing the query on the external database. The method may include receiving results data responsive to the data-query from the external database. The method may include outputting the results data for display on a user interface.
G06F 16/21 - Conception, administration ou maintenance des bases de données
G06F 16/248 - Présentation des résultats de requêtes
G06F 16/25 - Systèmes d’intégration ou d’interfaçage impliquant les systèmes de gestion de bases de données
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
Distributed pseudo-random subset generation includes obtaining a data-query indicating a first table having a first column including unique values, a second table having a second column including unique values, a join clause joining the first table and the second table on the first column and the second column, and a limit value, pseudo-random filtering the first table to obtain left intermediate data and left filtering criteria, pseudo-random filtering the second table to obtain right intermediate data and right filtering criteria, obtaining intermediate results data by full outer joining the left intermediate data and the right intermediate data, obtaining results data by filtering the intermediate results data using most-restrictive filtering criteria among the left filtering criteria and the right filtering criteria, and outputting the results data, wherein outputting the results data includes limiting the cardinality of rows of the results data to be at most the limit value.
Operating a low-latency database analysis system using domain-specific chronometry may include obtaining chronometry configuration data including chronometric instance data describing an instance of a chronometric unit of a domain-specific chronometry dataset that describes an era, such that the chronometry configuration data includes respective chronometric instance data describing each instance of the first chronometric unit of the domain-specific chronometry dataset for the era of the domain-specific chronometry dataset, generating, in the low-latency database analysis system, a domain-specific chronometry dataset in accordance with the chronometry configuration data, such that the domain-specific chronometry dataset describes a chronometric unit such that a temporal location expressed with reference to the chronometric unit and indicative of an epoch value differs from a temporal location indicative of the epoch value and expressed in accordance with a canonical chronometry, and storing the domain-specific chronometry dataset in the low-latency database analysis system.
G06F 16/00 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet
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
G06F 16/2458 - Types spéciaux de requêtes, p. ex. requêtes statistiques, requêtes floues ou requêtes distribuées
47.
Phrase translation for a low-latency database analysis system
Operating a low-latency database analysis system with phrase translation may include obtaining a locale-specific phrase localization rule and a canonical phrase localization rule for a phrase, generating a locale-specific index and a locale-specific finite state machine for the locale using the localization definition data and a canonical finite state machine, generating a resolved-request by obtaining a locale-specific token representing locale-specific input data by traversing the locale-specific index, obtaining a canonical token associated with locale-specific token, obtaining a locale-specific phrase by traversing the locale-specific finite state machine, obtaining a canonical phrase corresponding to the locale-specific phrase, the canonical phrase including the canonical token, generate a data-query based on the canonical phrase, obtaining results data responsive to the data expressing the usage intent by executing a query corresponding to the data-query by an in-memory database of the low-latency database analysis system, and outputting the results data for presentation to a user.
Identifying table joins includes obtaining respective casting similarities between pairs of columns of a first table and a second table. Each pair of columns includes a first column of the first table and a second column of the second table. Ones of the pairs of columns not satisfying a casting similarity condition are discarded to obtain first join candidates. Respective string similarities for the first join candidates are obtained. Ones of the first join candidates not satisfying a string similarity condition are discarded to obtain second join candidates. Final join candidates are obtained using the respective casting similarities and the respective string similarities of the second join candidates. A selected join candidate of the final join candidates is received from a user.
Object scriptability includes receiving a high-level language script describing at least one data-analysis object, including a node representing the data-analysis object in a graph-based data structure including a plurality of nodes, where each node from the plurality of nodes represents a respective data-analysis object in a data analysis system, where each node from the plurality of nodes is connected to at least one other node from the plurality of nodes by an edge, and where the edge represents a relationship between the respective objects in the data analysis system.
State-sequence pathing in a low-latency data access and analysis system includes obtaining, by the low-latency data access and analysis system, predicate data responsive to a request for data expressed in previously obtained data expressing usage intent, obtaining, by the low-latency data access and analysis system, state-sequence pathing criteria identified with respect to the predicate data, obtaining, by the low-latency data access and analysis system, state-sequence path data in accordance with the predicate data and the state-sequence pathing criteria, wherein the state-sequence path data aggregates data representing multiple state-sequence paths, wherein a respective state-sequence path represents an ordered sequence of states of a system, wherein the states are represented individually by the predicate data, generating, by the low-latency data access and analysis system, state-sequence path visualization data for presenting a visualization of the state-sequence path data, and outputting, by the low-latency data access and analysis system, the state-sequence path visualization data.
Enabling auto-completion of database commands includes receiving, at a database command execution device from a client device, a request to execute a database command where the request includes a first indicator of a first set of tokens of the database that is available at the client device; executing the database command; transmitting, from the database command execution device to the client device, a response to the request that includes a status of the execution of the database command and a second indicator of a second set of tokens of the database that is different from the first set of tokens; receiving, at the database command execution device and from the client device, an update-tokens request that includes the first indicator; and transmitting, from the database command execution device and to the client device, data indicative of differences between the second set of tokens and the first set of tokens.
Database replay log compaction verification includes identifying at least one replay log of a table that includes first database manipulation commands; obtaining a compacted replay log that includes second database manipulation commands that are insert commands, where an insert command includes a column and a corresponding value; replaying, to obtain a first replay result, the first database manipulation commands; replaying, to obtain a second replay result, the second database manipulation commands; and, responsive to one row of the first replay result not matching a corresponding row of the second replay result, sending a notification including a non-match. Replaying the first database manipulation commands includes identifying condition columns of the table; responsive to the condition columns not including the column, obtaining a row corresponding to the insert command, where the row includes a modified value of the corresponding value of the column; and adding the row to the first replay result.
Improved systems and methods for database analysis are described herein. A method includes generating a graph-based ontological data structure including nodes connected by edges in a low-latency database analysis system, wherein a respective node represents an object in the low-latency database analysis system, and wherein the respective node comprises a body comprising content of the respective object and a header comprising information about the object, receiving a modification request to the graph-based ontological data structure, wherein the modification request comprises data representing a change to the graph-based ontological data structure is received from a component of the low-latency database analysis system, verifying that the change clears conflicts, and applying the change to the graph-based ontological data structure after verifying that the change clears conflicts.
Systems and methods for mapping natural language to queries using a query grammar are described. For example, methods may include generating, based on a string, a set of tokens of a database syntax; generating a query graph for the set of tokens from a finite state machine representing a query grammar, wherein nodes of the finite state machine represent token types, directed edges of the finite state machine represent valid transitions between token types in the query grammar, vertices of the query graph correspond to respective tokens of the set of tokens, and directed edges of the query graph represent a transition between two tokens in a sequencing of the tokens; determining, based on a tour of the query graph, a sequence of the tokens in the set of tokens, forming a database query; and invoking a search of a database using a query based on the database query to obtain search results.
A method includes obtaining first data expressing a first usage intent; obtaining an answer object; obtaining, in accordance with first access control data, a first visualization data for the answer object; associating, with the first visualization data, a first generation-identifier that corresponds to at least a subset of the first access control data; generating first instructions for rendering the first visualization data on a user device of the first user; receiving a first snapshot of the first visualization data; storing a first association between the first generation-identifier and the first snapshot; obtaining second data expressing a second usage intent from a second user; identifying the answer object responsive to the second data expressing the second usage intent from the second user; and, responsive to second access control data corresponding to the second user matching the first generation-identifier, outputting second instructions for rendering the first snapshot to the second user.
Systems and methods for query generation based on merger of subqueries are described. For example, methods may include accessing a first join graph representing tables in a database, wherein the first join graph has vertices corresponding to respective tables in the database and directed edges corresponding to join relationships; receiving a first query specification that references data in two or more of the tables of the database to specify multiple subqueries in a set of subqueries; checking that two or more subqueries from the set of subqueries have the same join graph; checking that the two or more subqueries have the same set of grouping columns; responsive, at least in part, to the two or more subqueries having the same join graph and the same set of grouping columns, merging the two or more subqueries to obtain a consolidated query.
Injecting override instructions associated with query executions instructions performed on a distributed database includes receiving a data-query; generating, by a first database instance, a query plan that includes a first query execution instruction for transmission to a second database instance; transmitting, by the first database instance, a request for partial results to the second database instance, where the request includes the first query execution instruction and an indication of override instructions corresponding to the first query execution instruction; responsive to a determination that the request includes the indication, including, by the second database instance, the override instructions in a set of high-level language query instructions; obtaining, by the second database instance, a machine language query based on the set; executing, at the second database instance, the machine language query to obtain the partial results; and transmitting, by the second database instance, the partial results to the first database instance.
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
Described are methods and systems for improved cardinality estimation. A method may include obtaining a data-query, obtaining a row, generating a hash value, determining a cardinality of leading zeros in the hash value, identifying a bucket with respect to the hash value, including a bucket identifier and the cardinality of leading zeros in a representation, determining the approximate unique count, and outputting the approximate unique count as results data responsive to the portion of the data-query.
G06F 12/0864 - Adressage d’un niveau de mémoire dans lequel l’accès aux données ou aux blocs de données désirés nécessite des moyens d’adressage associatif, p. ex. mémoires cache utilisant des moyens pseudo-associatifs, p. ex. associatifs d’ensemble ou de hachage
G06F 16/901 - IndexationStructures de données à cet effetStructures de stockage
Search guidance includes generating user interface data for at least a portion of a user interface including a user input element, a data-analytics request construct card, and a data-analytics guidance portion. The data-analytics request construct card includes text describing usage of a data-analytics request construct card grammatical function. The user interface data is output for presentation to a user. Data-analytic request construct card data expressing usage intent is received and updated user interface data is generated. The updated user interface data corresponds to an updated user input element in accordance with the data-analytic request construct card data and an updated data-analytics guidance portion in accordance with the data-analytic request construct card data. The updated user interface data is output. Resolved-request data is generated in accordance with the data-analytic request construct card data. A visualization representing results data obtained in accordance with the resolved-request data is output.
Querying a distributed database including a table sharded into shards distributed to database instances includes receiving a data-query that includes an aggregation clause on a first column and a grouping clause on a second column; obtaining and outputting results data. Obtaining the results data includes receiving, by a query coordinator, intermediate results data; and combining, by the query coordinator, the intermediate results to obtain the results data. Receiving the intermediate results data includes receiving, from a first database instance, first aggregation values indicating, on a per-group basis in accordance with the grouping clause, a respective aggregation value of distinct values of the first column in accordance with the aggregation clause, and receiving, from a second database instance, second aggregation values indicating, on a per-group basis in accordance with the grouping clause, a respective aggregation value of distinct values of the first column in accordance with the aggregation clause.
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
G06F 16/22 - IndexationStructures de données à cet effetStructures de stockage
G06F 16/248 - Présentation des résultats de requêtes
Operating a low-latency data access and analysis system using domain-specific chronometry may include obtaining, in the low-latency data access and analysis system, data expressing usage intent with respect to the low-latency data access and analysis system, in response to obtaining the data expressing usage intent, obtaining ontological data for a chronometric object in the low-latency data access and analysis system indicated by the data expressing usage intent, identifying a chronometry dataset from a plurality of chronometry datasets, wherein the plurality of chronometry datasets includes a domain-specific chronometry dataset and a canonical chronometry dataset, obtaining results data in accordance with the chronometry dataset and the chronometric object, generating output data representing the results data in accordance with the chronometry dataset, and outputting the output data for presentation via a user interface.
A low-latency database analysis system using an object index may include obtaining data expressing a usage intent, and, in response to a determination that the data expressing the usage intent includes object search request data including a user identifier and zero or more object search terms, obtaining, from an object-index responsive to the object search request data, object indexing data for an object, obtaining object detail data for the object, obtaining an object visualization capture for the object, and outputting object search response data including the object visualization capture and at least a portion of the object detail data for presentation to a user.
Indexing in a low-latency data access and analysis system includes accessing, by an indexing unit of a low-latency data access and analysis system, constituent data from a data source of the low-latency data access and analysis system and indexing the constituent data in an index of the low-latency data access and analysis system by an indexing unit of the low-latency data access and analysis system. Indexing includes partitioning the constituent data based on a characteristic of the constituent data into at least a first partition and a second partition, segmenting the first partition into a first segment of the first partition, sharding the first segment into a first shard of the first segment of the first partition, segmenting, using hash-partitioning, the second partition into one or more segments of the second partition, and for respective segments of the second partition, sharding the respective segment into one or more respective shards.
G06F 16/22 - IndexationStructures de données à cet effetStructures de stockage
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
45 - Services juridiques; services de sécurité; services personnels pour individus
Produits et services
Advertising, marketing and promotion services; Promoting the goods and services of others; Provision of an online marketplace for buyers and sellers of goods and services Online social networking services
Performing, by a low-latency data access and analysis system, automatic grammar switching includes, in response to obtaining data indicating a request to switch from using a first defined grammar to using a second defined grammar, wherein the request includes data representing a first resolved-request generated for a first input string using the first defined grammar, generating, using the second defined grammar, a second resolved-request for the first input string, automatically generating a data-query in accordance with the second resolved-request, and outputting output data including results data obtained by executing the data-query.
Providing a search interface for a database includes receiving string data entered via a user interface. A sequence of tokens representative of the string data is determined. Determining the sequence of tokens includes applying natural language processing to the string data. A first database query is generated in accordance with the sequence of tokens. Respective text representations for tokens in the sequence of tokens are presented via the user interface. Feedback data related to the sequence of tokens is received via the user interface. A token of the sequence of tokens is modified based on the feedback data to obtain a modified sequence of tokens. A second database query is generated based on the modified sequence of tokens. Results data are obtained from the database responsive to execution of the second database query by the database. Data based on the results data are output for presentation in the user interface.
Lossless switching between search grammars is described herein. A method may include generating a first resolved-request using a relational search grammar in response to receiving a request for data, where the first resolved-request includes an ordered sequence of tokens, and in response to receiving a request for data indicating the first resolved-request and indicating a request to use a natural language search grammar, generating a second resolved-request in accordance with the natural language search grammar by identifying tokens and token binding data associated with the relational search grammar resolved-request as query refinements, generating the second resolved-request consistent with the first resolved-request by tokenizing the request for data associated with the natural language search grammar in accordance with the natural language search grammar using the query refinements, and outputting search results obtained using the request for data associated with the natural language search grammar using the natural language search grammar.
Search guidance methods and system are described. A method includes generating user interface data for at least a portion of a user interface for performing data-analytics with respect to a low-latency database analysis system, the portion including a text string user input element, a data-analytics construct card type, and a data-analytics guidance portion. The data-analytics construct card type includes text on usage of a data-analytics construct card grammatical function with respect to a data-analytics grammar used by the system to process data associated with the data-analytics construct card type. The user interface data is output for presentation to a user. Data associated with the data-analytics construct card construct type is received, the text string user input element is output with the data, and an updated data-analytics guidance portion is output in response to the data. A request for data including the data is processed and results are output.
A low-latency database analysis system outputs visualization data for presenting a visualization representing results data responsive to the request for data, and, in response to outputting the visualization data, generates a diverse plurality of candidate modifications for the request for data, outputs candidate modification data for presenting the diverse plurality of candidate modifications in association with the presentation of the visualization representing the results data, in response to outputting the candidate modification data, obtains user input data identifying a candidate modification from the diverse plurality of candidate modifications as a selected modification, in response to the user input data, executes a data-query expressing the request for data modified by the selected modification to obtain second results data responsive to the request for data modified by the selected modification, and in response to the second results data, outputs visualization data for presenting a visualization representing the second results data.
G06N 20/10 - Apprentissage automatique utilisant des méthodes à noyaux, p. ex. séparateurs à vaste marge [SVM]
G06Q 20/38 - Protocoles de paiementArchitectures, schémas ou protocoles de paiement leurs détails
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Providing temporary use of on-line non-downloadable software that implements application templates for automating the generation of custom data analysis tools for business, academic, government and scientific data analytics; Software as a Service (SaaS) services featuring software for utilizing application templates for automating the generation of custom data analysis tools for business, academic, government and scientific data analytics; none of the aforementioned services related to music, podcasts, or entertainment
71.
Phrase translation for a low-latency database analysis system
Operating a low-latency database analysis system with phrase translation may include obtaining a locale-specific phrase localization rule and a canonical phrase localization rule for a phrase, generating a locale-specific index and a locale-specific finite state machine for the locale using the localization definition data and a canonical finite state machine, generating a resolved-request by obtaining a locale-specific token representing locale-specific input data by traversing the locale-specific index, obtaining a canonical token associated with locale-specific token, obtaining a locale-specific phrase by traversing the locale-specific finite state machine, obtaining a canonical phrase corresponding to the locale-specific phrase, the canonical phrase including the canonical token, generate a data-query based on the canonical phrase, obtaining results data responsive to the data expressing the usage intent by executing a query corresponding to the data-query by an in-memory database of the low-latency database analysis system, and outputting the results data for presentation to a user.
Low-latency autonomous-analysis includes obtaining data expressing a usage intent with respect to a low-latency database analysis system that intent omits data corresponding to user input expressly requesting low-latency autonomous-analysis, obtaining requested results data based on the data expressing the usage intent, outputting requested visualization data representing at least a portion of the requested results data for presentation to a user, and, in response to outputting the requested visualization data, obtaining low-latency autonomous-analysis data by performing low-latency autonomous-analysis based on the data expressing the usage intent by identifying an autonomous-analysis predicate based on the requested visualization data, obtaining a defined autonomous-analysis latency constraint, obtaining the low-latency autonomous-analysis data based on the autonomous-analysis predicate in accordance with the defined autonomous-analysis latency constraint, such that the low-latency autonomous-analysis data differs from the requested results data, and outputting at least a portion of the low-latency autonomous-analysis data for presentation to a user.
Operating a low-latency database analysis system using domain-specific chronometry may include obtaining chronometry configuration data including chronometric instance data describing an instance of a chronometric unit of a domain-specific chronometry dataset that describes an era, such that the chronometry configuration data includes respective chronometric instance data describing each instance of the first chronometric unit of the domain-specific chronometry dataset for the era of the domain-specific chronometry dataset, generating, in the low-latency database analysis system, a domain-specific chronometry dataset in accordance with the chronometry configuration data, such that the domain-specific chronometry dataset describes a chronometric unit such that a temporal location expressed with reference to the chronometric unit and indicative of an epoch value differs from a temporal location indicative of the epoch value and expressed in accordance with a canonical chronometry, and storing the domain-specific chronometry dataset in the low-latency database analysis system.
G06F 16/00 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet
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
G06F 16/2458 - Types spéciaux de requêtes, p. ex. requêtes statistiques, requêtes floues ou requêtes distribuées
Operating a low-latency database analysis system using domain-specific chronometry may include obtaining, in the low-latency database analysis system, data expressing a usage intent with respect to the low-latency database analysis system, in response to obtaining the data expressing the usage intent, obtaining ontological data for a chronometric object in the low-latency database analysis system indicated by the data expressing the usage intent, identifying a chronometry dataset from a plurality of chronometry datasets, wherein the plurality of chronometry datasets includes a domain-specific chronometry dataset and a canonical chronometry dataset, obtaining results data in accordance with the chronometry dataset and the chronometric object, generating output data representing the results data in accordance with the chronometry dataset, and outputting the output data for presentation via a user interface.
G06F 16/20 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet de données structurées, p. ex. de données relationnelles
G06F 16/2458 - Types spéciaux de requêtes, p. ex. requêtes statistiques, requêtes floues ou requêtes distribuées
G06F 16/22 - IndexationStructures de données à cet effetStructures de stockage
G06F 16/248 - Présentation des résultats de requêtes
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable software for use in graphical representation of search results; Downloadable software for use in graphical representation of data; downloadable software for providing a user interface for analyzing data; downloadable software that allows users to create, upload, bookmark, view, annotate and share data Providing temporary use of online non-downloadable software for use in graphical representation of search results; Providing temporary use of online non-downloadable software for use in graphical representation of data; Providing temporary use of online non-downloadable software for providing a user interface for analyzing data; Providing temporary use of online non-downloadable software that allows users to create, upload, bookmark, view, annotate and share data
76.
Token based dynamic data indexing with integrated security
Semantic analysis to resolve ambiguous user input data with respect to a request for data includes identifying tokens based on the text string, identifying a tables in a database, wherein a token indicates a column from a table and a token indicates a column from another table, identifying candidate join paths for joining tables, obtaining respective candidate results using the candidate join paths, outputting combined candidate results including values from the respective candidate results, obtaining second user input data indicating a selected value, identifying the request for data as unambiguous and identifying a selected join path based on the selected value, and, in response to identifying the request for data as unambiguous, outputting data responsive to the request for data using the selected join path.
G06F 16/38 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
Intent-resolution using a phrase index may include obtaining data expressing a usage intent, the data expressing the usage intent including an unresolved data portion, identifying a phrase fragment based on the data expressing the usage intent and a defined phrase pattern, the phrase fragment including the unresolved data portion, identifying, by a processor, an indexed phrase as a candidate phrase by searching a phrase index based on the phrase fragment, wherein the candidate phrase at least partially matches the phrase fragment in accordance with the defined phrase pattern, and outputting the candidate phrase for presentation to a user as a candidate for resolving the unresolved portion.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Providing on-line non-downloadable software using artificial intelligence for analyzing data; providing on-line non-downloadable software for natural language processing; providing on-line non-downloadable software for providing actionable insights relating to business data; none of the aforementioned services related to music, podcasts, or entertainment
Systems and methods for multi-layered key-value storage are described. For example, methods may include receiving two or more put requests that each include a respective primary key and a corresponding respective value; storing the two or more put requests in a buffer in a first datastore; determining whether the buffer is storing put requests that collectively exceed a threshold; responsive to the determination that the threshold has been exceeded, transmitting a write request to a second datastore, including a subsidiary key and a corresponding data file that includes the respective values of the two or more put requests at respective offsets in the data file; for the two or more put requests, storing respective entries in an index in the first datastore that associate the respective primary keys with the subsidiary key and the respective offsets; and deleting the two or more put requests from the buffer.
Query execution on compressed in-memory data includes receiving, at a processor of an instance of a distributed in-memory database, a query for data from a table stored in the distributed in-memory database as compressed table data, obtaining results data responsive to the query from the table, and outputting the results data for presentation to a user. Obtaining results data includes allocating memory to identify allocated memory for decompressing the compressed table data, obtaining uncompressed table data by decompressing the compressed table data into the allocated memory, and obtaining the results data from the uncompressed table data. The allocated memory is deallocated in response to obtaining the results data. Compressing a table to form compressed table data is also described.
Data-query execution with distributed machine-language query management in a low-latency database analysis system may include obtaining, at a distributed in-memory database, a data-query expressing a request for data in a defined structured query language associated with the distributed in-memory database, automatically generating a high-level language query representing at least a portion of the data-query, obtaining a machine language query corresponding to the high-level language query, executing the machine language query to obtain results data, and outputting the results data. Obtaining the machine language query may include determining whether the machine language query is cached, and in response to a determination that the machine language query is unavailable, sending a request for the machine language query to a distributed machine-language-query management instance.
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
A low-latency database analysis system using an object index may include obtaining data expressing a usage intent, and, in response to a determination that the data expressing the usage intent includes object search request data including a user identifier and zero or more object search terms, obtaining, from an object-index responsive to the object search request data, object indexing data for an object, obtaining object detail data for the object, obtaining an object visualization capture for the object, and outputting object search response data including the object visualization capture and at least a portion of the object detail data for presentation to a user.
Intent-resolution using a phrase index may include obtaining data expressing a usage intent, the data indicating an unresolved data portion, identifying a phrase fragment based on the data expressing the usage intent and a phrase pattern, the phrase fragment including the unresolved data portion, identifying candidate tokens, identifying candidate phrases by traversing a phrase index based on the phrase fragment, wherein identifying the candidate phrases includes, in response to a determination that the phrase index includes an indexed phrase at least partially matching the phrase fragment in accordance with the phrase pattern, the indexed phrase is identified as one of the candidate phrases, identifying candidate resolved-requests, weighting and sorting the candidate tokens, the candidate phrases, and the candidate resolved-request, to obtain sorted candidate resolutions, and outputting one or more of the sorted candidate resolutions for presentation to a user as respective candidates for resolving the unresolved data portion.
Systems and methods for query generation based on merger of subqueries are described. For example, methods may include accessing a first join graph representing tables in a database, wherein the first join graph has vertices corresponding to respective tables in the database and directed edges corresponding to join relationships; receiving a first query specification that references data in two or more of the tables of the database to specify multiple subqueries in a set of subqueries; checking that two or more subqueries from the set of subqueries have the same join graph; checking that the two or more subqueries have the same set of grouping columns; responsive, at least in part, to the two or more subqueries having the same join graph and the same set of grouping columns, merging the two or more subqueries to obtain a consolidated query.
Systems and methods for mapping natural language to queries using a query grammar are described. For example, methods may include generating, based on a string, a set of tokens of a database syntax; generating a query graph for the set of tokens using a finite state machine representing a query grammar, wherein nodes of the finite state machine represent token types, directed edges of the finite state machine represent valid transitions between token types in the query grammar, vertices of the query graph correspond to respective tokens of the set of tokens, and directed edges of the query graph represent a transition between two tokens in a sequencing of the tokens; determining, based on the query graph, a sequence of the tokens in the set of tokens, forming a database query; and invoking a search of a database using a query based on the database query to obtain search results.
Systems and methods for query generation based on a logical data model with one-to-one joins are described. For example, methods may include accessing a join graph representing tables in a database; receiving a first query; selecting a connected subgraph of the join graph that includes the two or more tables referenced in the first query; accessing an indication that a directed edge of the connected subgraph corresponds to a one-to-one join; modifying the connected subgraph based on the indication to obtain a modified subgraph; generating one or more leaf queries based on the modified subgraph; generating a query graph that specifies joining of results from queries based on the one or more leaf queries; invoking a transformed query on the database that is based on the query graph and the queries based on the one or more leaf queries.
G06F 15/16 - Associations de plusieurs calculateurs numériques comportant chacun au moins une unité arithmétique, une unité programme et un registre, p. ex. pour le traitement simultané de plusieurs programmes
G06F 16/21 - Conception, administration ou maintenance des bases de données
G06F 16/22 - IndexationStructures de données à cet effetStructures de stockage
Index sharding in a low-latency database analysis system includes obtaining index configuration data for indexing constituent data, the constituent data including a plurality of logical tables, and indexing, by an indexing unit, the constituent data by partitioning the constituent data based on a characteristic of the constituent data into at least a first partition and a second partition, segmenting the first partition into a first segment of the first partition, sharding the first segment into a first shard of the first segment of the first partition, segmenting, using hash-partitioning, the second partition into one or more segments of the second partition, and for each segment of the second partition, sharding the segment into one or more respective shards.
G06F 16/22 - IndexationStructures de données à cet effetStructures de stockage
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
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Computer software systems for providing integrated business
management intelligence by combining information from
various databases and presenting it in an easy-to-understand
user interface; computer software that provides integrated
business management intelligence by combining information
from various databases and presenting it in an
easy-to-understand user interface. Providing online, non-downloadable software that provides
integrated business management intelligence by combining
information from various databases and presenting it in a
user interface; providing online, non-downloadable software
in the field of artificial intelligence, namely, software
for use in searching, analyzing, integrating, managing, and
reporting business data, and software for use in natural
language processing; providing online, non-downloadable
software that uses artificial intelligence to analyze
business data; providing online, non-downloadable software
for machine learning, namely, software for use in searching,
analyzing, integrating, managing, and reporting business
data; providing online, non-downloadable software for
analytics, namely, software for use in searching, analyzing,
integrating, managing, and reporting business data;
providing online, non-downloadable software for business
intelligence, namely, software for use in searching,
analyzing, integrating, managing, and reporting business
data.
90.
Data integration for distributed and massively parallel processing environments
Methods and systems for large scale data integration in distributed or massively parallel environments comprises a development phase wherein the results of a proposed jobflow can be viewed by the user during development, including the results of upstream units where the data sources and data targets can be any of a variety of different platforms, and further comprises the use of remote agents proximate to those data sources and data targets with direct communication between the associated agents under the direction of a topologically central controller to provide, among other things, improved security, reduced latency, reduced bandwidth requirements, and faster throughput.
Systems and methods for conversational user experiences and conversational database analysis disclosed herein improve the efficiency and accessibility of low-latency database analytics. The method may include obtaining data expressing a usage intent with respect to the low-latency database analysis system, wherein the data expressing the usage intent includes a current request string expressed in a natural language, a current context associated with the current request string, and a previously generated context associated with a previously generated resolved-request, identifying, from the current request string, a conversational phrase corresponding to a conversational phrase pattern from a defined set of conversational phrase patterns, generating a resolved-request based on the identified conversational phrase, including the resolved-request in the current context, obtaining results data responsive to the resolved-request from a distributed in-memory database, generating a response including the results data and the current context, and outputting the response.
A method and system may be implemented for automatically analyzing data in a database. A method for use in a low-latency database analysis system may include generating a schema. The schema may be based on a portion of an external database. The method may include storing the schema in an in-memory database. The method may include receiving a data-query. The method may include generating a resolved-request. The resolved-request may be based on the data-query and the stored schema. The stored schema may be used for executing the query on the external database. The method may include receiving results data responsive to the data-query from the external database. The method may include outputting the results data for display on a user interface.
G06F 16/21 - Conception, administration ou maintenance des bases de données
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
G06F 16/248 - Présentation des résultats de requêtes
G06F 16/25 - Systèmes d’intégration ou d’interfaçage impliquant les systèmes de gestion de bases de données
Low-latency autonomous-analysis includes obtaining data expressing a usage intent with respect to a low-latency database analysis system that intent omits data corresponding to user input expressly requesting low-latency autonomous-analysis, obtaining requested results data based on the data expressing the usage intent, outputting requested visualization data representing at least a portion of the requested results data for presentation to a user, and, in response to outputting the requested visualization data, obtaining low-latency autonomous-analysis data by performing low-latency autonomous-analysis based on the data expressing the usage intent by identifying an autonomous-analysis predicate based on the requested visualization data, obtaining a defined autonomous-analysis latency constraint, obtaining the low-latency autonomous-analysis data based on the autonomous-analysis predicate in accordance with the defined autonomous-analysis latency constraint, such that the low-latency autonomous-analysis data differs from the requested results data, and outputting at least a portion of the low-latency autonomous-analysis data for presentation to a user.
Improved systems and methods for database analysis are described herein. A method includes generating a graph-based ontological data structure including nodes connected by edges in a low-latency database analysis system, wherein each node represents a respective analytical-object in the low-latency database analysis system, maintaining versions for each of the nodes in the graph-based ontological data structure, maintaining versions for each of the edges in the graph-based ontological data structure, maintaining a transaction log for each transaction with respect to the graph-based ontological data structure, reverting to an earlier version of at least a portion of the graph-based ontological data structure using the transaction log, versioned nodes, and versioned edges in response to an event, and outputting a version of the graph-based ontological data structure in a defined form for presentation to a user or for use by a client.
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable computer search engine software; downloadable data analytics engine software Software as a service (SaaS) services featuring computer search engine software; software as a service (SaaS) services featuring data analytics engine software
Systems and methods for query generation based on a logical data model are described. For example, methods may include accessing a first join graph representing tables in a database; receiving a first query that references data in two or more of the tables of the database; selecting a connected subgraph of the first join graph that includes the two or more tables referenced in the first query; generating multiple leaf queries that reference respective subject tables that are each a root table of the connected subgraph or a table including a measure referenced in the first query; generating a query graph that specifies joining of results from queries based on the multiple leaf queries to obtain a transformed query result; and invoking a transformed query on the database that is based on the query graph and the queries based on the multiple leaf queries to obtain the transformed query result.
Systems and methods for natural language question answering are described. For example, methods may include determining a set of candidate database queries, including respective sequences of tokens of a database syntax, based on a string; determining a first score for a first candidate database query from the set of candidate database queries, wherein the first score is based on a match between one or more words of the string and a token of the respective sequence of tokens of the first candidate database query; determining a second score for the first candidate database query, wherein the second score is based on natural language syntax data determined for words of the string; selecting, based on the first score and the second score, the first candidate database query from the set of candidate database queries; and invoking a search of the database using the first candidate database query to obtain search results.
09 - Appareils et instruments scientifiques et électriques
41 - Éducation, divertissements, activités sportives et culturelles
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Computer hardware; computer hardware and downloadable
computer software systems for providing integrated business
management intelligence by combining information from
various databases and presenting it in an easy-to-understand
user interface; database management software for use in
integrated business management intelligence; downloadable
artificial intelligence software for use in searching,
analyzing, integrating, managing, and reporting business
data; downloadable software that uses artificial
intelligence to analyze business data; downloadable software
for machine learning, namely software for use in searching,
analyzing, integrating, managing, and reporting business
data; downloadable software for analytics, namely, software
for use in searching, analyzing, integrating, managing, and
reporting business data; downloadable software business
intelligence namely, software for use in searching,
analyzing, integrating, managing, and reporting business
data; downloadable software for use in natural language
processing; and downloadable instruction manuals distributed
therewith. Educational services, namely, conducting seminars,
conferences, workshops, and computer application training in
the fields of business intelligence and distributing course
materials in connection therewith. Providing online, non-downloadable software that provides
integrated business management intelligence by combining
information from various databases and presenting it in a
user interface; providing online, non-downloadable software
in the field of artificial intelligence, namely, computer
software for use in searching, analyzing, integrating,
managing, and reporting business data; providing online,
non-downloadable software that uses artificial intelligence
to analyze business data; providing online, non-downloadable
software for machine learning, namely, software for use in
searching, analyzing, integrating, managing, and reporting
business data; providing online, non-downloadable software
for analytics, namely software for use in searching,
analyzing, integrating, managing, and reporting business
data; providing online, non-downloadable software for
business intelligence, namely, software for use in
searching, analyzing, integrating, managing, and reporting
business data; providing online, non-downloadable software
for use in natural language processing.
A method and system may be implemented for automatically analyzing data in a database. The method and system may receive a current context of the database. The method and system may identify one or more columns of utility based on the current context and generate a current context based on the one or more columns of utility. The method and system may generate one or more exploration queries. The method and system may explore the one or more exploration queries to generate an exploration result set. The method and system may generate one or more insights. The one or more insights may be based on the current context, the exploration result set, or both. The method and system may rank the insights. The method and system may display, transmit, or store the one or more insights based on the rank.
Automatic database analysis includes identifying a current context for accessing data from a low-latency database and generating an exploration query based on the current context, which includes identifying a column from the low-latency database as a column of utility in response to determining that a probabilistic utility for the column satisfies a defined utility criterion. The current context includes a requested result set satisfying a requested search criterion, and the probabilistic utility is based on the current context. The analysis includes generating an exploration result set based on the exploration query, generating insights based on the exploration result set, ranking the insights, and outputting at least one insight based on the ranking.
