51 results about "Schema mapping" patented technology

The invention discloses a dialogue type data analysis method and device, a storage medium and electronic equipment, and relates to the technical field of data analysis. The method comprises the steps that a natural language text input by a user is acquired; obtaining corresponding semantic pre-analysis information based on the natural language text; matching a corresponding mode mapper based on the semantic pre-analysis information; obtaining corresponding semantic information based on a mode mapper; converting the semantic information into corresponding semantic information conversion parameters; analyzing the semantic information conversion parameter to obtain an index value corresponding to the to-be-queried index; performing anomaly detection on the index value corresponding to the to-be-queried index; and performing analysis result display on the index value corresponding to the to-be-queried index passing the anomaly detection. According to the method, information arrangement and information analysis are carried out on the natural language text input by the user, the intention of the user is known, the index needing to be queried by the user is fed back, the operation complexity is reduced, the interactivity and the real-time performance are improved, and convenience is provided for data analysis work.

The invention discloses a data integration and fusion method and system based on a large model, and the method comprises the steps: taking the metadata information of initial multi-source data and representative sample data as the input context of a pre-trained LLM large language model, and carrying out the training through a Prompt prompt project guide model, thereby obtaining a plurality of enhanced data source mode descriptions; inputting a plurality of enhanced data source mode descriptions into an LLM large-scale language model, enabling the model to be a data integration engine through a Prompt prompt project, guiding the model to recognize semantic equivalence fields among different data source modes, and outputting a dynamic mode mapping scheme; analyzing a conflict reason and reasoning a conflict resolution strategy, and outputting a data cleaning suggestion and a data analysis result; and loading the fused data into the target data storage. The method can adapt to complex and changeable data structures, and the flexibility and efficiency of data integration are improved.

A system (100) for adaptive data blending across heterogeneous BI platforms for real-time decision making, comprising: (a) a data source abstraction module configured to establish standardised and secure connections with several different Business Intelligence (BI) platforms and convert their native data structures into a unified intermediate schema; (b) a real-time data ingestion and synchronisation module operable to continuously monitor and retrieve data updates from the BI platforms using event-driven mechanisms and configurable synchronisation policies; c) an adaptive data mapping and transformation module designed to intelligently harmonize, adapt, and semantically translate disparate data formats using rule-based logic and AI-assisted schema mapping; (d) a semantic context and metadata management module configured to maintain a unified data dictionary, track data sequence and maintain semantic consistency across platforms; (e) a cross-platform query orchestration engine designed to decompose common user queries, translate them into platform-specific query languages, execute them across the relevant BI platforms, and aggregate the results into a coherent output; (f) a decision intelligence and recommendation module that analyses the mixed data to generate real-time insights, alerts and contextual recommendations for decision support; and (g) a security, governance and compliance module configured to enforce access controls, maintain audit trails and ensure compliance with data protection regulations; h) the system provides seamless, real-time and secure data blending to enable consistent analytics and decision-making across heterogeneous BI environments.

Systems, computer program products, and methods are described herein for generating and mapping unicode for cross-distributed network data transmissions. The present disclosure is configured to identify a cross-distributed network data transmission comprising source ledger code; generate a core schema for a destination ledger, wherein the destination ledger is based on data of the cross-distributed network data transmission; generate, by a ledger transformation module, a data dictionary, wherein the ledger transformation module receives ledger code from a large language model (LLM); generate code mapping instructions for the cross-distributed network data transmission based on the core schema for the destination ledger; convert the source ledger code to a unicode; receive, by a receiver schema mapping module, the unicode and the code mapping instructions; and generate, by the receiver schema mapping module, an intermediary ledger comprising the unicode and based on the code mapping instructions.

A computer-aided design metadata extraction system for automated uploads to a document management system, wherein the system includes the following: a central processing unit configured to control all metadata extraction operations and coordinate the interaction between multiple hardware subsystems; a metadata interpretation controller connected to the central processing unit, which includes a dedicated analysis circuit configured to read file headers, attribute tables, revision identifiers and embedded descriptors from a CAD file; a structured data formatter coupled with the metadata interpretation controller, wherein the structured data formatter is configured to convert the extracted metadata into a normalized schema compatible with predefined fields of a document management system; a volatile memory module and a non-volatile memory module, each coupled to the central processing unit, wherein the volatile memory module is configured to store runtime processing states and the non-volatile memory module is configured to retain persistent logs of extracted metadata and completed upload transactions; a secure communication module comprising an encryption controller and an authentication controller, wherein the secure communication module is configured to transmit the structured metadata and the associated CAD file to the document management system via an authenticated and encrypted connection; and an operator interface comprising a graphical display unit and physical input controls, wherein the operator interface is configured to allow an operator to preview the extracted metadata, configure schema mapping rules, and initiate or approve metadata upload operations.

This invention provides a high-end scientific instrument reliability analysis and design method based on SysML, belonging to the field of instrument reliability analysis technology. The method includes: constructing a system model of the target instrument using SysML, the system model including a requirements diagram, internal block diagram, and attribute library; extracting all failure modes from all failure mode libraries and assigning them global numbers to establish a failure mode mapping matrix; performing failure mode and impact analysis (FMEA) for the target instrument based on the system model to obtain a failure mode and impact analysis table; establishing a parameterized reliability model for each functional path; calculating the reliability constraints of each functional path based on the system model and the impact analysis table; and performing reliability analysis based on the measured attributes and reliability constraints of sub-components to generate an optimization scheme. The advantages of this invention are improved accuracy, consistency, and automation of reliability analysis.

A system and method of use resolves the frustration of repeated manual work during schema mapping. The system utilizes a transformation graph—a collection of nodes (unified attributes) and edges (transformations) in which source attributes are mapped and transformed. The system further leverages existing mappings and transformations for the purpose of suggesting to a user the optimal paths (i.e., the lowest cost paths) for mapping new sources, which is particularly useful when new sources share similarity with previously mapped sources and require the same transformations. As such, the system also promotes an evolving schema by allowing users to select which unified attributes they want to include in a target schema at any time. The system addresses the technical challenge of finding optimal transformation paths and how to present these to the user for evaluation.

A system and method for evicting a resource from a unified network topology (UNT) of a computing environment, is disclosed. The method includes: generating a representation of a UNT for a network topology of a computing environment, the representation based on a unified data schema, wherein the unified data schema is mapped to a plurality of data schemas, each corresponding to a unique data source; receiving an event record at a first time, the event record including a resource identifier; updating in the UNT a representation corresponding to the resource based on extracted data from the event record at the first time; determining that a time period has lapsed from the first time which is greater than a threshold; detecting that a number of event records which include the resource identifier is less than a threshold value; and removing the representation of the resource from the unified network topology.

Embodiments of systems and methods for defining data ingest pipelines for processing data received from data sources to obtain and store that extracted data according to a schema mapped to concepts defined in a semantic model are disclosed. Raw data can be received from these data sources and semantically contextualized by processing the determined raw data associated with each of the multiple data sources to represent the determined raw data from each data source according to the schema generated for that data source such that the determined raw data associated with that data source is mapped to the one or more concepts of the semantic model. This semantically contextualized data can be made available for uses for various systems or applications.

Automatically mapping unlabeled input data onto a target schema via semantic type detection is described. The input data includes data elements that are structured as 2D table rows and columns forming cells. Each data element is included in a cell. The target schema includes a set of fields. Schema mapping includes mapping each column to one or more fields. More particularly, the fields are clustered into field clusters, where each field cluster includes one or more of the fields. Each column is automatically mapped to one of the field clusters of the set of field clusters. The mapping between schema fields and data columns is automatically performed based on appropriate pairings of the detected semantic types, where the semantic types are encoded in vector representations of the fields, the field clusters, and the data elements.

The present invention relates to an intra prediction mode mapping method and a device using the method. The intra prediction mode includes: decoding flag information providing information regarding whether an intra prediction mode of a plurality of candidate intra prediction modes for the current block is the same as the intra prediction mode for the current block, and decoding a syntax component including information regarding the intra prediction mode for the current block in order to induce the intra prediction mode for the current block if the intra prediction mode from among the plurality of candidate intra prediction modes for the current block is not the same as the intra prediction mode for the current block. Thus, it is possible to increase the efficiency with which are images are decoded.

The invention provides a log management method and device based on an ORC format of Logstash, equipment and a medium. The method comprises the steps that a Logstash service is started, and an ORC conversion Ruby script and multi-pipeline configuration are loaded; real-time data receiving and Schema dynamic mapping are carried out; according to the method, the problems of long link, poor real-time performance, large resource consumption and the like of traditional log processing are fundamentally solved, the architecture is simplified, the delay is reduced, the resource utilization efficiency is improved, and the real-time data processing capability is enhanced.

The present invention discloses a method and a system for detecting and fixing reentrancy vulnerabilities. The detection method of the present invention is as follows: perform single reentrancy path detection and combined reentrancy path detection on the smart contract source code to be processed; among them, the single reentrancy path detection method is as follows: first, compile the smart contract source code to generate Ethereum Virtual Machine bytecode, Application Binary Interface, source code mapping, and Abstract Syntax Tree, and then traverse the Ethereum Virtual Machine bytecode to obtain the execution path of the smart contract source code and generate a control flow graph, and identify the reentrancy pattern of the path according to the reentrancy pattern mapping table during the traversal process; among them, the combined reentrancy path detection method is as follows: traverse the main reentrancy path, combine each main reentrancy path with each reentered path in pairs, and determine whether the path after each combination meets the connectivity and the read-write pattern of the reentrancy vulnerability. If it meets, it is considered that there is a reentrancy vulnerability. The present invention can fix vulnerabilities on the premise of ensuring the contract semantics remain unchanged.

A system and method for configuration-driven continuous training of large language models in autonomous medical coding normalize multi-source electronic health data into provenance-preserving, specialty-aware schemas and provide schema-mapped context to a configurable multi-stage reasoning pipeline to generate candidate code bundles including evaluation and management levels, procedure codes, modifiers, diagnosis codes, and linkages. A ruleset-based validation layer enforces bundling, modifier applicability, and payer policies, and emits machine-readable explanations with rule identifiers, evidence references, and pass / fail indicators. A telemetry-enabled user interface captures approvals, rejections, edits, and rationales, which are consolidated with validation results into provenance-tracked training examples via a trust- or quality-weighted consensus. Signals from reasoning outputs, validation checks, user interactions, and downstream outcomes are fused to produce continuous learning signals that update prompt templates and output ranking parameters through configuration rollouts-without redeploying base models. The approach improves accuracy, robustness, explainability, and auditability for scalable EHR integration.

An adaptive answer-generating agent implemented on a computer system, communicatively connected to a user device, and configured to: receive a user query from the user device; map the user query to one of a plurality of reconfigurable answer-generation schemas stored in first data storage, each of the answer-generation schema specify a sequence of steps to generate an answer output to the query, said agent including an answer generation engine configured to: receive the answer-generation schema mapped to the user query; implement the sequence of steps to generate an answer output, and communicate the answer output to the user device, wherein the agent further includes a schema modification function configured to: receive schema modification data from a further computing device relating to one or more of the answer-generation schemas, and modify the sequence of steps of the one or more of the answer-generation schemas in accordance with the schema modification data.

The invention discloses an intelligent memory scheduling method and device, electronic equipment and a storage medium, and relates to the technical field of memory scheduling and task management. The method comprises the following steps: acquiring data information of a current task, constructing an access mode database, and mapping an access mode into a preset task semantic category by adopting a task semantic classifier; according to the state information of the candidate memory channel, calculating an affinity score of the task semantic category and the candidate memory channel; calculating the priority of the current task through the multi-dimensional features according to the task semantic category of the current task and the user configuration information; according to the load conditions of the candidate memory channels, the target memory channel is allocated for the current task, and memory scheduling of the current task is completed. The method achieves precision and differentiation of memory scheduling, can adapt to service characteristics and resource requirements of different tasks, flexibly adjusts a scheduling strategy according to the system state, and improves the scheduling efficiency. And the utilization efficiency of memory resources is effectively improved.

The invention provides a voucher processing method which can be applied to the technical field of big data and artificial intelligence. The method comprises the following steps: based on a field mapping rule, carrying out mode mapping on preprocessed historical certificates from different service systems according to a standard field unified mode; performing multi-dimensional association processing on the historical vouchers after mode mapping and the multi-source real-time information associated with the historical vouchers to obtain a unified voucher data model; in response to the service request, preprocessing target service associated information associated with the service request, and generating a target service voucher by using the preprocessed target service associated information and the unified voucher data model; and carrying out anomaly detection on the service request by using the preprocessed feature clustering result of the historical voucher, and carrying out compliance check on the generation process of the target service voucher by using a compliance rule determined by the unified voucher data model. The invention further provides a voucher processing system, voucher processing equipment, a storage medium and a program product.

An adaptive answer-generating agent implemented on a computer system, communicatively connected to a user device, and configured to: receive a user query from the user device; map the user query to one of a plurality of reconfigurable answer-generation schemas stored in first data storage, each of the answer-generation schema specify a sequence of steps to generate an answer output to the query, said agent comprising an answer generation engine configured to: receive the answer-generation schema mapped to the user query; implement the sequence of steps to generate an answer output, and communicate the answer output to the user device, wherein the agent further comprises a schema modification function configured to: receive schema modification data from a further computing device relating to one or more of the answer-generation schemas, and modify the sequence of steps of the one or more of the answer-generation schemas in accordance with the schema modification data.

Disclosed is a method for an intermediary mapping an de-identification comprising steps of retrieving datasets and meta data from a data source; selecting a target standard; mapping the retrieved datasets and the metadata to the target standard, wherein the datasets and the metadata are mapped to the target standard using one of, a schema mapping, a variable mapping, or a combination thereof; infer one or more of, variable classifications, variable connections, groupings, disclosure risk settings, and de-identification settings using the dataset mapping and metadata; perform a de-identification propagation using the mapped datasets, the mapped metadata, the inferred variable classifications, the inferred variable connections, the inferred groupings, the inferred disclosure risk settings, the inferred de-identification settings, or a combination thereof.

Various embodiments provide methods, apparatus, systems, computing entities, and / or the like, for recommending and / or defining cross-schema mappings, or a mapping from a data element type of a first schema to one of a second schema. In one example embodiment, a method is provided. The method includes identifying a plurality of canonical data element types belonging to a canonical schema and an external data element type belonging to an external schema. The method includes obtaining a multi-dimensional representation for each data element type and determining a predicted similarity score for each canonical data element type using comparison results from a mapper machine learning model. The method further includes generating a recommendation data object indicating one or more mappings between the external data element type and a selected subset of the plurality of canonical data element types, the subset selected according to predicted similarity score.

A system and method of tax regulatory change management is provided, comprising: receiving tax filing instructions and e-file schemas of tax forms; converting the filing instructions into output filing instructions and the e-file schemas into output e- file schemas; executing a mapping operation to generate an instruction-to-schema mapping; executing a tax form descriptions operation that generates form descriptions and per-line descriptions of each of the tax forms from the instruction-to-schema mapping using a first generative model; receiving a tax article identifying a tax regulation change; executing an impacted forms operation that uses the form descriptions and the identified tax regulation change as inputs to a second generative model that identifies an impacted tax form; executing an impacted lines operation that uses a third generative model to identify an impacted line from the impacted form; and generating a notification including the impacted line in the impacted form.

A system and method of tax regulatory change management is provided, comprising: receiving tax filing instructions and e-file schemas of tax forms; converting the filing instructions into output filing instructions and the e-file schemas into output e-file schemas; executing a mapping operation to generate an instruction-to-schema mapping; executing a tax form descriptions operation that generates form descriptions and per-line descriptions of each of the tax forms from the instruction-to-schema mapping using a first generative model; receiving a tax article identifying a tax regulation change; executing an impacted forms operation that uses the form descriptions and the identified tax regulation change as inputs to a second generative model that identifies an impacted tax form; executing an impacted lines operation that uses a third generative model to identify an impacted line from the impacted form; and generating a notification including the impacted line in the impacted form.

The invention provides a voice interaction control method, an application terminal and a storage medium, the voice interaction control method is used for the application terminal, the application terminal is provided with an application program for controlling a clothes care machine, the application terminal is provided with a server, and the voice interaction control method comprises the following steps: S1, acquiring voice information input by a user and uploading the voice information to the server; s2, the server performs recognition by using a voice database and determines a nursing strategy; and S3, the clothes nursing machine carries out nursing operation according to the nursing strategy. According to the voice interaction control method, the clothes care machine can be more intelligent through voice interaction; the nursing strategy is quickly determined by utilizing the preset information mode mapping table stored on the server, meanwhile, mass data of the user parameters are uploaded to the server, and the mapping table is updated after screening, so that the user can better conform to the use habits of the user, and the user experience is good.

A system for monitoring data representative of a user's general wellbeing and for delivering digital content to the user, the system comprising a pre-defined module comprising a plurality of reference patterns mapped to digital content for delivery to the user, the system further comprising at least a processing means, wherein the processing means is configured to: identify user activity and select a user based on the identified user activity; for the selected user, interrogate user data representative of the user's general wellbeing and identify a pattern in said user data; and compare the identified pattern to the reference patterns in the pre-defined module to identify digital content mapped with the identified pattern.

The invention discloses a Flume-based time sequence database dynamic adaptation method and device and a medium, belongs to the technical field of databases, and aims to solve the technical problems of how to realize dynamic adaptation of a time sequence database and how to improve the access efficiency of the time sequence database. Comprising the following steps: dynamically loading a driving module of a target time sequence database through a dynamic pluggable protocol plug-in pool; the method comprises the following steps: sampling an input data stream, automatically deducing a timestamp field, an index field and a tag field based on a predefined rule set, and generating a Schema mapping relation of a target time sequence database; dynamically adjusting data batch processing parameters through a reinforcement learning model according to a real-time load index of the time sequence database; and when the time sequence data fails to be written in, a hierarchical retry mechanism is triggered, and the hierarchical retry mechanism comprises local caching, exponential backoff and standby cluster switching.

Systems, computer program products, and methods are described herein for generating and mapping unicode for cross-distributed network data transmissions. The present disclosure is configured to identify a cross-distributed network data transmission comprising source ledger code; generate a core schema for a destination ledger, wherein the destination ledger is based on data of the cross-distributed network data transmission; generate, by a ledger transformation module, a data dictionary, wherein the ledger transformation module receives ledger code from a large language model (LLM); generate code mapping instructions for the cross-distributed network data transmission based on the core schema for the destination ledger; convert the source ledger code to a unicode; receive, by a receiver schema mapping module, the unicode and the code mapping instructions; and generate, by the receiver schema mapping module, an intermediary ledger comprising the unicode and based on the code mapping instructions.

Embodiments of the present application provide a data migration method and device across heterogeneous databases, a storage medium and a program product, relating to the technical field of databases. The method comprises: in response to a migration task, obtaining source data information from at least one source database through a source connector; mapping the source data information into standardized data according to a preset unified data model; inputting the standardized data into a schema mapping engine, and generating target mapping configuration information corresponding to a target database according to a preset mapping rule by the schema mapping engine; converting the standardized data into data in the format of the target database based on the target mapping configuration information to obtain target-ready data; and writing the target-ready data into the target database through a target connector. The method of the present application increases the compatibility of heterogeneous databases and improves the automation degree and processing efficiency of data migration.

A system (100) for schema-aware automatic transformation of heterogeneous data into data lakes, comprising (a) a schema detection and inference module configured to automatically extract and infer the structure and schema of incoming data from different formats and sources; b) a schema matching and alignment module configured to semantically map and align the derived source schema with a predefined target schema using contextual analysis; c) a transformation rules module configured to automatically create transformation rules to convert data to conform to the target schema; d) an execution and orchestration module configured to apply the transformation rules to data in batch or streaming mode; (e) a metadata and lineage management module configured to track, log, and manage schema versions, mappings, and data transformations; and f) a user interface and API layer configured to enable the configuration, visualisation and control of schema mappings and transformation processes.

A method, system, and computer-readable medium are disclosed for processing and comparing documents in varying formats, incorporating advanced data extraction, normalization, and comparison techniques. The method includes receiving documents containing structured and unstructured data, processing them with optical character recognition and layout analysis to identify structural elements, and extracting key data fields using natural language processing, including named entity recognition and contextual analysis. Extracted data is normalized through schema mapping and synonym resolution, ensuring consistency in terminology and structure. The system employs fuzzy matching algorithms to identify discrepancies between documents and provides an interactive user interface with graphical visualizations, such as heatmaps and comparison charts, for reviewing and resolving differences. Machine learning techniques refine the system over time by integrating user feedback to improve data extraction, synonym resolution, and comparison accuracy.