161 results about "Database schema" patented technology

The present application discloses a database query method and apparatus, an electronic device, and a non-volatile storage medium. The method comprises: determining a knowledge graph corresponding to a database to be queried, wherein the knowledge graph is used for representing a logical structure and an association relationship of data in said database; determining similarity scores between user question text and graph nodes in the knowledge graph, and determining a target node from among the graph nodes of the knowledge graph on the basis of the similarity scores, wherein the similarity scores are used for representing the degree of association between the graph nodes and the user question text; and on the basis of the target node, generating database schema information corresponding to said database, and using a large language model to generate, on the basis of the database schema information, a structured query language statement corresponding to the user question text.

The invention provides a Text2SQL generation method and system driven by a large language model, and the method comprises the steps: recognizing key entities and relationships in a natural language question through the large language model, and carrying out the construction to obtain a query graph; constructing a hierarchical graph representation model based on the database mode of the target database; searching candidate sub-image sets which have the same structure as the query graph and are in semantic association with the query graph from the constructed graph representation model; and constructing a structured Prompt template based on the candidate sub-image set and the user question, generating an initial SQL statement, and outputting a final query result after grammar and semantic detection correction. According to the method, a traditional database is converted into the graph knowledge base, so that the SQL generation accuracy and performance are improved; guiding the large model to generate an SQL (Structured Query Language) meeting business requirements through knowledge graph storage and retrieval table and column information; and the accuracy of the SQL is continuously optimized by utilizing a self-adaptive feedback mechanism, so that the method is particularly suitable for a large-scale complex query scene, and self-improvement and customization of the model are realized.

The invention relates to the technical field of computers, in particular to a dynamic configuration initialization and version synchronization method and system for multi-agent service, and the method comprises the following steps: obtaining a database update lock, constructing a database migration script dependency relationship, determining a current version of a database mode, upgrading and deploying the database mode, and verifying agent versions. The method has the beneficial effects that a rigorous database updating lock control mechanism, perfect migration script dependency chain verification, agent version management, port availability detection, idempotence and transaction safeguard measures are introduced, so that the processes of database structure upgrading and agent version initialization in a distributed deployment environment can be effectively supported; and the data consistency, the operation stability and the service continuity of the system are ensured.

The invention discloses a method and a system for generating an SQL (Structured Query Language) query based on a natural language question. The method comprises the following steps: converting mode information of a target database and the natural language question into semantic vector representation; based on the semantic vector representation, a simplified database mode set related to SQL query is screened out through an attention mechanism; based on the natural language problem, the simplified database mode set and preset database constraint information, generating an SQL structural skeleton, and filling specific elements of the SQL structural skeleton to form a preliminary SQL query; and performing dynamic correction and verification on the initial SQL query by utilizing a large language model, and outputting a final SQL query. According to the method, association mining between user query and a database mode is effectively enhanced by utilizing a context-aware cross-encoder mechanism, and an implicit corresponding relation between a natural language problem and a database table / column can be more accurately identified and utilized.

A computer-implemented method for improved schema matching of two databases is disclosed. The method can receive a schema of a source table from a first database and a schema of a plurality of target tables from a second database, identify one or more matching tables among the plurality of target tables based on comparison of the schema of the source table and the schema of the plurality of target tables using a large language model, obtain first sample attribute data from the source table and second sample attribute data from a selected matching table, and identify one or more pairs of matching attributes between the source table and the selected matching table based on comparison of the first sample attribute data and the second sample attribute data using the large language model. Related systems and software for implementing the method are also disclosed.

The invention relates to a dynamic adaptive Text2SQL (Structured Query Language) generation method and system for the power field. The method comprises the following steps: S1, analyzing a power database Schema and automatically generating a diversified natural language-SQL pair data set; s2, performing cleaning, optimization and format standardization on the data, and constructing a high-quality training set and a high-quality test set; s3, querying a prediction database Schema based on a natural language, and generating an SQL statement with accurate pattern matching; s4, a logic disassembly optimization mechanism is introduced, and it is ensured that SQL query conforms to database rules and meets the requirements of the power industry; s5, finely adjusting the large language model to improve the accuracy and the performability of SQL generation; s6, performing compression optimization on the trained model to improve reasoning performance, stability and query accuracy; and S7, efficiently deploying the optimized electric power large language model, and guaranteeing the stability, response speed and model performance of the system through real-time monitoring. According to the method, the accuracy and applicability of converting the natural language query into the SQL statement in the power industry can be effectively improved.

The present disclosure relates to manufacturing training and testing data by leveraging data augmentation techniques to generate examples of long context database schemas. Aspects are directed towards accessing a training dataset comprising training examples where each training example may include i) a prompt including a natural language utterance and a database schema having one or more tables, and ii) a gold logical form corresponding to the natural language utterance, combining the tables from the database schemas in the training examples may generate a combined database schema set, generating a set of long context training examples based on the training dataset and the combined database schema set, and incorporating the long context database schema into the selected training example to generate a long context training example to train a generative artificial intelligence model with at least the set of long context training examples to generate a trained generative artificial intelligence model.

The invention relates to the technical field of data processing, and particularly provides a double-engine-based SQL (Structured Query Language) generation method, system and device and a medium, and the method comprises the following steps: receiving a natural language query input by a user; analyzing the natural language query by using an intention understanding engine in combination with a dialogue context and a business knowledge graph to generate a structured query intention representation; utilizing an SQL generation and optimization engine to generate at least one candidate SQL statement based on the structured query intention representation and a vectorization database Schema library; performing execution plan analysis on the at least one candidate SQL statement, and performing performance evaluation and optimization rewriting based on an analysis result to generate a final high-performance SQL statement; and executing the final high-performance SQL statement and returning a query result. According to the method, the query success rate and the user experience in a complex scene are greatly improved.

The invention provides a text-to-structured query statement method based on a large language model and reinforcement learning, and relates to the technical field of information, the method comprises the following steps: integrating a natural language problem and a database mode into a unified prompt template to obtain a candidate structured query, and generating a reference structured query by a reference model; constructing a multi-dimensional reward framework, and performing weighted aggregation on multi-dimensional rewards to obtain corresponding final reward scores; calculating a KL penalty value between the output of the strategy model and the output of the reference model based on the candidate structured query and the reference structured query to obtain a constraint of stable strategy update; updating the strategy model parameters through back propagation iteration to obtain an updated strategy model; and analyzing the prompt template by using the updated strategy model to obtain a text-to-structured query statement processing result, and completing the process from the text to the structured query statement. The problems that existing structured query generation is low in accuracy and semantic consistency is difficult to guarantee are solved.

Here is an innovative way to generate a finetuning corpus that maximizes the accuracy of a target large language model (LLM) that generates a database statement. From a natural language request, the target LLM infers an incorrect database statement that, based on a first database schema, could not satisfy a technical requirement. Based on the natural language request, a correct database statement is generated that, based on a second database schema, could satisfy the technical requirement. For the second database schema, a restatement of the natural language request is generated. In inputs during finetuning, the target LLM accepts: the correct database statement, the incorrect database statement, and the restatement of the natural language request.

Embodiments of the present disclosure provide large language models (LLMs) and large multimodal models (LMMs) for managing imaging data. An example method includes extracting a database schema and a data dictionary associated with a medical imaging database that maintains medical images and non-image data associated with a plurality of clinical studies; extracting object attributes associated with the medical images that are descriptive of the medical images and corresponding studies; and training an LLM to generate search queries from natural language requests based on a training dataset that includes: the database schema, the data dictionary, the object attributes, a prompt template including partial instructions for the LLM, and a plurality of example natural language requests and corresponding expected search queries. The trained LLM can then be used to generate a first search query from a first natural language request.

A healthcare shopping and management system is disclosed herein which assists users in searching for medical providers based on insurer-negotiated pricing, directly scheduling appointments, making payments, aggregating personal health records, and utilizing analytics to suggest preventative health screenings. The system architecture includes interfaces for searching, scheduling, paying, health records access, geolocation of providers, cost ratings, and an analytics engine leveraging clinical guidelines. Database schema and data models as well as example user interfaces are also disclosed.

The invention discloses a Text-to-SQL (Structured Query Language) method based on a large language model. The method comprises a database mode linking module, a context information enriching module, a dynamic decomposition SQL (Structured Query Language) generation module, an execution feedback optimization module and a selection module based on self-consistency. Performing multi-path recall screening on related tables, columns and value fields in a database through a mode link; the mode information of the database is expressed through context information enrichment, key information is extracted, and a question is re-described; aiming at the problem of different complexity, different SQL generation schemes are designed, and a plurality of candidate SQL statements are generated; checking and correcting grammar and semantic errors existing in the SQL statement by executing feedback optimization; and through voting and a binary selection model, selecting the most accurate SQL query as a final SQL. The method can effectively solve the problems of enterprise data increase, difficulty in complex SQL generation and the like, improves the conversion accuracy and efficiency, enhances the adaptability to different database modes, and has important practical value.

The invention discloses a ChatBI plug-in construction method and system based on multiple models. The method comprises the steps that a data source is established, and configuration connection is carried out; selecting successfully connected data sources and databases and tables under the data sources to form a database mode; selecting a pre-integrated natural language processing model type and inputting an API-key to complete information registration of the natural language processing model; the pre-integrated natural language processing model comprises a plurality of models; aiming at different SQL dialogue scenes, configuring corresponding prompt word templates; aiming at different SQL dialogue scenes, configuring corresponding dialogue interaction strategies; configuring application basic information, and selecting a corresponding database mode, a model, a cue word template and a dialogue interaction strategy according to the application basic information to form a query scene application; and generating a ChatBI plug-in package according to the query scene application. The problems that a traditional BI system lacks intelligence and is inconvenient to integrate with an original system are solved.

Systems, computer program products, and methods are described herein for improving network and data security by automatically preventing rogue database queries. The present disclosure is configured to identify a query request, wherein the query request comprises query request metadata; apply the query request to a generative adversarial network (GAN) model, wherein the GAN model is protected by a security gateway; generate, by the GAN model, a result metadata based on the query request, wherein the GAN model is trained on a database schema; and determine, based on a comparison of the query request metadata and the result metadata, whether to allow the query request by the security gateway.

Systems and methods are provided for processing a natural language question using structured query language (SQL). A computing system receives natural language questions, obtains access levels and database schema, generates prompts, identifies relevant tables, and generates SQL code using a SQL large language model (LLM). It executes the code on the database and generates a result message with obfuscated data values based on access level. The result message is provided in response to the natural language question.

Systems and methods are provided for processing a natural language question using structured query language (SQL). A computing system receives a natural language question; generates a prompt that comprises the natural language question and a database schema corresponding to a database; processes, using a retrieval system, the prompt to identify one or more tables in the database, the one or more tables relevant to the natural language question; generates using the retrieval system, an augmented prompt that comprises the natural language question, the database schema, and one or more identities of the one or more tables; generates, using a SQL large language model (LLM), a set of SQL code based on the augmented prompt; initiates executing the SQL code on the database and receiving a result; generates a result message using the result; and provides the result message responsive to the natural language question.

Methods and systems for retrieving data from at least one database based on a query input related to a clinical trial. A system processes a received query input related to a clinical trial using a pre-trained language model neural network. The neural network generates a structured representation of the query input. The system maps a first data field of the structured representation to a first column name and maps a second data field of the structured representation to a second column name. The system generates a database query based on (i) a database schema, (ii) the first column name, (iii) the second column name, (iv) data values associated with the first data field, and (v) data values associated with the second data field. The database query specifies an operation for joining data associated with the first column name with data associated with the second column name.

Techniques are disclosed for augmenting training data for training a machine learning model to generate database queries. Training data comprising a first training example comprising a first natural language utterance, a logical form for the first natural language utterance, and associated first metadata is obtained. From the first training example, a template utterance is generated. A second natural language utterance is generated by filling slots in the template utterance based on a database schema and database values. Updated metadata is produced based on the first metadata and the second natural language utterance. A second training example is generated, comprising the second natural language utterance, the logical form for the first natural language utterance, and the updated metadata. The training data is augmented by adding the second training example. A machine learning model is trained to generate a database query comprising the database operation using the augmented training data set.

Systems and methods are provided for processing a natural language question using structured query language (SQL). A computing system receives a natural language question, generates a prompt with a database schema corresponding to a database, identifies relevant tables, and generates an augmented prompt with table identities. A SQL large language model (LLM) generates SQL code, which is executed on the database, and a result from the database is obtained. Additional data sources and additional metadata are retrieved to obtain an additional result. The additional result and the result are used to generate a result message, which is provided in response to the natural language question.

The invention relates to internet data services such as databases and cloud database services, more particularly belongs to the field of internet and cloud computing and big data services, and particularly relates to a method and a system for realizing intelligent sorting of data assets based on a self-research algorithm, which comprises the following steps of: constructing an intelligent sorting link of a target database; generating an initial field description set of the target database through a field description generation module of the intelligent carding link; according to the semantic consistency consensus level and the field description accuracy of the initial field description set, establishing a quality evaluation matrix corresponding to the intelligent carding link; according to the quality evaluation matrix, reconstructing a dynamic increment Schema processing mechanism corresponding to the intelligent carding link; and performing vectorization storage on the data assets finally confirmed by the dynamic increment Schema processing mechanism to complete data asset sorting of the business system to be sorted. According to the method, the storage result of the data assets can be ensured, and database Schema dynamic change in a complex business scene can be accurately adapted.

A computer-implemented system for performing updates to a multi-client application without downtime, wherein the system includes: a client policy store configured to manage client-specific rollout and compliance policies; an update orchestrator configured to generate a client-specific rollout plan from a versioned dependency graph of application components; a version compatibility layer configured to enforce dual-mode compatibility between simultaneously active service and data versions; a data evolution engine configured to perform database schema changes using an expand-migrate contract procedure with versioned read views and a transactional outbox for dual-write with checkpoint-based backfill; a traffic shaper configured to perform programmable L4 / L7 routing, including session pinning, shadow mirroring, stepwise canary operation, and atomic client-specific cutover; and a telemetry and health manager configured to calculate client-specific service level indicators and to control phase progress and rollback decisions; where the coordinated cooperation of the update orchestrator, the version compatibility layer, the data evolution engine, the traffic shaper, and the telemetry and health manager updates one or more clients without interrupting ongoing sessions or violating data consistency requirements.

The invention discloses a large model illusion suppression intelligent question answering system and method based on deep learning, and the method comprises the following steps: constructing a database Schema, and configuring an entity mapping relation; configuring cue word information reasoned by the large model to form a task cue word sequence; complete system input is formed; executing structured query language conversion by adopting a large model of an improved NASNet network architecture to generate an SQL query statement; generating natural language description information; performing consistency verification to obtain a consistency verification result; generating corresponding calculation prompt information; according to the method, the hallusion and semantic offset generated by a large model in a structured question and answer scene are effectively inhibited, and the accuracy of SQL generation and the reliability of the question and answer result are improved.

Methods and systems for retrieving data from at least one database based on a query input related to a clinical trial. A system processes a received query input related to a clinical trial using a pre-trained language model neural network. The neural network generates a structured representation of the query input. The system maps a first data field of the structured representation to a first column name and maps a second data field of the structured representation to a second column name. The system generates a database query based on (i) a database schema, (ii) the first column name, (iii) the second column name, (iv) data values associated with the first data field, and (v) data values associated with the second data field. The database query specifies an operation for joining data associated with the first column name with data associated with the second column name.

The invention discloses a method for questioning and answering. The method comprises the steps of receiving a natural language question input by a user; determining a target node set according to the natural language question and correlation between nodes in a question and answer database relation graph generated on the basis of a question and answer database in advance; mapping the target node set into a database Schema; taking the database Schema and the natural language question as input of a target model to generate an SQL query statement; and determining a natural language answer of the natural language question according to a query result of the SQL query statement in the question and answer database. By dynamically constructing the database Schema related to the natural language question input by the current user, the target model directly understands the question and answer database structure based on the database Schema and generates the accurate SQL query statement, end-to-end training does not need to be performed on the target model, the structural change of the question and answer database can be automatically adapted, and the calculation burden is effectively reduced.

The application discloses a cloud-native database change rollback and version management method, belongs to the technical field of database maintenance, and solves the technical problem that the same code program maintained by the current developer has no unified processing logic, so that the database change becomes chaotic and bloated, and the human time cost is increased, the method comprising the following steps: encapsulating an application program and a database management program migration-job based on a k8s platform in a container and deploying the container in a k8s cluster; when the database migration is normally operated, the database management program migration-job is operated in a non-parallel task type Job mode, a container task Pod that undertakes the Job task is constructed, and the container task Pod is used for executing a corresponding database version migration task; the database schema change and rollback sql file of the latest version of a specified database are mounted in the container encapsulating the database management program migration-job; and the database schema change and rollback sql file of the latest version of the specified database are mounted in a host path or a persistent storage volume.

Generating structured query language queries, by extracting database schema from a target database, inputting the database schema into a generative model, training the generative model using a Monte-Carlo method and input data, training a discriminator model to distinguish generated and real input data, determining a loss value for generated input data, further training the generative model using the loss value, receiving, over a network, input text from a user, automatically generating, using the generative model, an output using the input text and providing, over the network, the output to the user.

This invention discloses a database schema-aware SQL query performance prediction method and apparatus. The method includes: acquiring SQL query statements, execution plans, and database schema information; extracting features and uniformly encoding various query-related elements; generating a novel query execution graph structure using the query statement and execution plan, learning the explicit correlations and execution processing logic between the two through an attention mechanism, and obtaining query execution graph features that integrate both information; using a relational graph convolutional neural network to learn features from the database schema relational graph, capturing rich connections between table columns and underlying data distribution information; and fusing the query execution graph and database schema information through a database schema-aware fusion method to generate query features for predicting query performance. This invention fully utilizes various query-related information and mines the correlations between features to improve query performance prediction, and can be applied to the field of database technology.

A system for decentralized database management includes a graphical user interface (GUI) layer configured to receive user actions for database operations. The GUI layer includes an input mapping module to translate user actions into backend commands, a real-time monitoring module to display metrics related to node status, database health, and blockchain transactions, and an error handling module to categorize errors for troubleshooting. The system includes a hybrid database system integrated within a decentralized blockchain network supporting both relational and non-relational data models, with value contracts defining database schemas and rules, and an indexing mechanism for optimizing data retrieval. A Self-Delegated Proof of Equal Stake (SDPoES) consensus mechanism is provided to validate and synchronize database operations across nodes, including a dynamic scheduling engine to coordinate block production among master nodes and a stake validation system to manage master node qualification through memory staking requirements.

A data structure embodied on a computer-readable medium is disclosed. The data structure may include database schema such as a structured query language (SQL) database. The database schema may include a registration schema that cooperates with a competition schema to award contestants engaged in a game of skill. The competition schema may encourage contestants to participate in games of skill related to songs, artists, and / or albums.