24 results about "Schema transformation" patented technology

A system for converting a source data feed schema into a canonical data product including a memory for storing computer-executable instructions and a processor for executing the instructions stored on the memory. Execution of the instructions programs the processor to perform operations that include receiving a source data feed having a source schema, identifying a plurality of data fields of the source schema, assigning a data category from a plurality of predefined data categories to each data field of the plurality of data fields, modifying the source schema based on predefined parameters, wherein the source schema is modified to match a target schema, comparing the modified source schema to the target schema, and in response to a determination that the modified source schema matches the target schema, converting the source data feed to a canonical data product having the target schema based on the assigned data categories.

The embodiment of the invention provides a model training method, an information recommendation method, equipment, a storage medium and a program product. According to the embodiment of the invention, a multi-encoder-multi-sub-decoder-total decoder hybrid model architecture is provided, sample data of different information modes correspond to different encoders-sub-decoders, and a mode of processing all training sample data by a single model is converted into a divide-and-conquer mode. The internal complexity of each encoder-sub-decoder is relatively low, the complexity of model training can be reduced, resource consumption can be saved, and different encoder-sub-decoders can be trained in parallel, so that the model training time can be shortened; and furthermore, by utilizing a dual decoding mechanism of the sub-decoder and the global decoder, parameters of the encoder can be continuously adjusted through local optimization and global optimization, the performance of the model is optimized, the accuracy of a reasoning result is improved, the convergence speed of the model is accelerated, the model training efficiency is further improved, and the model training time is saved.

Systems, methods, and computer-readable storage media. A system includes one or more processing circuits to identify a modification or update of unstructured data stored in an upstream source, determine a new schema of the unstructured data using a function, the function based on at least one of a pattern, a transformation, an inference, or a correspondence between the unstructured data and a previous schema, determine a difference between the new schema and the previous schema, generate structured data corresponding to the unstructured data, and store the structured data as a representation within the database of the modification or update of the unstructured data.

A computer implemented method includes receiving, by the processor, a request to initiate a migration of data. The method also includes receiving, by the processor, data identified in the request to initiate a migration. The method also includes generating, by the processor, a file-out of the data by converting the data from a proprietary schema into a commonly understood schema. The method also includes migrating, by the processor, at least a portion of the file-out to a cloud provider. The method also includes validating, by the processor, the migration of the file-out to determine whether the migration is successful, and in response to validating the migration, generating, by the processor, a migration response indicating whether the migration was successful.

The application discloses an edge network traffic prediction method based on redundancy reserve pool calculation, comprising the following steps: collecting traffic data of an edge base station, and constructing a training set and a test set according to the traffic data; inputting the constructed training set into an ESN model and training, and collecting a reserve pool state matrix; performing high-dimensional mapping on the reserve pool state matrix by using a ordinal pattern conversion network modeling method, calculating the ordinal complexity of reserve pool neurons according to a complex network measurement index, and determining key and non-key neurons of the reserve pool; balancing migrating the input weight and the internal weight of the key neurons of the reserve pool to the non-key neurons of the reserve pool in a redundant manner to form a new ESN model, retraining the new ESN model, and obtaining an optimal output weight matrix; deploying the trained new ESN model on an edge side network traffic management device, collecting traffic data of the edge base station in real time as input of the model, and obtaining a prediction value of future network traffic as output of the model.

Methods, systems, and computer-readable storage media for receiving a first source data schema (SDS) representing a first format for recording data in a computer-readable document, providing a first prompt including at least a portion of the first SDS, generating description data by processing the first prompt using a first LLM, providing a second prompt including of at least a portion of the first SDS, at least a portion of a first target data schema (TDS), and the description data, generating first schema conversion code (SCC) by processing the second prompt using a second LLM, converting a first document from the first SDS to the first TDS using the first SCC, processing the first document in the first TDS using a ML model to provide inference results, and automatically executing one or more tasks responsive to the inference results.

The application provides a data processing method of a row-column mixed storage database and related products. The data processing method comprises the following steps: obtaining an instruction for performing storage mode conversion on an initial partition, wherein the initial partition adopts a row storage mode; constructing a target partition, wherein the target partition adopts a column storage mode; performing columnar reorganization on data of the initial partition and migrating the data to the target partition, wherein the columnar reorganization is used for converting data based on the row storage mode into data based on the column storage mode; and atomically updating a metadata pointer of the initial partition to direct a corresponding query operation to the target partition. The data processing method overall realizes effective compression of storage space, acceleration of analysis query, and comprehensive optimization of system resource utilization, and is particularly suitable for mixed transaction and analysis processing scenarios.

The invention relates to the technical field of databases, and discloses a centralized database and distributed database conversion method, which comprises the following steps of: detecting whether all computing nodes support a cluster mode conversion function or not through a management node of a centralized cluster; if all the computing nodes support the cluster conversion function, converting the centralized cluster into a pre-conversion mode, and verifying the runnability of stock services after the centralized cluster is switched to a multi-fragment mode in the pre-conversion mode; if the verification of the operability of the stock service is passed after the centralized cluster is switched to the multi-fragment mode, SQL compatibility and data object verification is carried out; when the SQL compatibility and data object verification is passed, a system built-in library table and system metadata are reconstructed, a distributed built-in data column is added for a service library table, and all DDL operations and backup tasks are forbidden; and after the built-in library table of the system and the system metadata are reconstructed, the pre-switching mode of the cluster is switched into a formal multi-fragment mode, and the centralized database is switched into the distributed database.

The invention relates to the technical field of industrial control system data storage, in particular to a self-adaptive block storage method based on data steady-state feature recognition, which comprises a block management module, a mode conversion module, an index module and a data IO (input / output) module. And performing data organization by adaptively using the time sequence main module A or the hybrid main module B based on the data steady-state characteristics. The time sequence main module A is suitable for a high-steady-state data sequence and compresses continuous steady-state data by adopting run length coding; the mixing main module B is suitable for a steady-state and discrete data mixing sequence, and the two types of data are distinguished and processed in a bitmap marking mode. Through the innovative architecture of the A / B main module, the discrete module, the high-speed index and the mode conversion, the technical problem that high compression ratio and high query performance in industrial time series data storage are difficult to consider at the same time is effectively solved, and the method is particularly suitable for efficient storage and retrieval of mass time series data in industrial 4.0 and edge computing scenes.

This invention discloses a method, apparatus, system, electronic device, and storage medium for compiling web pages. The method includes: acquiring metadata of a data source storing business data; generating a schema describing the page structure and / or page layout based on the metadata; converting the schema into a code file; editing code associated with customized functions in the code file in response to editing processing; and compiling a page containing the business data based on the code file. By converting the schema into a code file and editing the code associated with customized functions within the code file, a page with customized functions can be provided, enriching the page's functionality. Furthermore, performing minimal editing work within the code file reduces workload. Additionally, direct CRUD operations on the data source are also implemented based on the metadata.

Techniques are disclosed relating to restarting a database node. A database node may allocate memory segments that include a restart segment for storing data records. The database node may spawn processes to read a log and replay log records of the log to update the restart segment to store data records. The database node may determine to perform a restart operation to transition from a first mode to a second mode. Performing the restart operation may include ceasing reading the log at a stop position and storing, based on the stop position, database state information that enables the processes to resume reading the log from the stop position. The database node may further deallocate the memory segments except for the restart segment and terminate the processes. After performing the restart operation, the database node may spawn the processes, which may resume reading the log based on the database state information.

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 discloses an electromechanical coupling system fault diagnosis method and system based on a multi-level vector library, and belongs to the technical field of fault diagnosis. The electromechanical coupling system is sequentially divided into a plurality of hierarchies according to functional composition of the electromechanical coupling system, each hierarchy comprises a plurality of units with different functions, and the method comprises the following steps: converting a fault mode into a fault mode vector x through an embedded model; the adjacent agent An evaluates and awards the fault reason of the corresponding unit retrieved by the agent An-1 from the PHM multi-level vector library, and retrieves the fault reason of the unit in the PHM multi-level vector library according to the fault mode vector x and the fault reason; and fusing the N fault causes of the intelligent body search through a fusion device. According to the invention, the fault part of the electromechanical coupling system can be accurately positioned.

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 database storage link performance optimization method which can be applied to the technical field of computer system software performance optimization. The method comprises the steps of performing compression and reconstruction on an original instruction sequence of a structured query language parser in a database storage link based on instruction compression and expansion provided by a reduced instruction set processor platform, and generating an optimized instruction sequence with higher code density; based on a memory management attribute of a reduced instruction set processor platform and a layout control function of a compiling linker, performing aggregation layout optimization on a physical storage position of an instruction code of a core storage management function in a database storage link in a processor instruction cache; based on single-instruction multi-data stream extension provided by a reduced instruction set processor platform, key operations related to batch data access and processing in a database storage link execution process are converted from a scalar execution mode to a vectorization execution mode.

This invention provides a method and apparatus for pattern conversion in calling tools from object models to language models. The method includes: receiving and parsing input object model description data, and extracting action fields, attribute fields, and supplementary configuration fields from the object model description data; mapping the action fields to first-class tool function definitions recognizable by the large language model; splitting and reconstructing the attribute fields into independent attribute acquisition functions and attribute setting functions, and adding constraints to the parameters of the attribute acquisition functions and attribute setting functions; converting the supplementary configuration fields into synchronous interaction functions and tool-level annotation information adapted to the large language model; aggregating the first-class tool function definitions, second-class tool function definitions, synchronous interaction functions, and tool-level annotation information, and outputting a parameter pattern file conforming to the large language model tool calling specification. This achieves standardized automatic bridging between IoT devices and the large language model interface, reducing the cost of connecting massive numbers of devices to the large model.

The application provides a relational database to document database mode conversion method and system, and relates to the technical field of computer, which comprises the following steps: extracting data mode in the relational database; completing the conversion from the relational model to the document mode based on the interactive genetic algorithm; showing the document mode obtained by the mode conversion module and providing the user with feedback, and then continuously iterating to generate a new document mode. The method provided by the application can guarantee that all relational tables and foreign key relationships in the relational mode are converted, and the document mode evaluation algorithm is used to measure the goodness of the converted document mode, which is beneficial to simplifying the mode conversion process. For the relational mode, a series of candidate document modes are converted in an iterative manner, the user can evaluate the candidate document modes according to the domain knowledge, the method re-generates the document mode by referring to the feedback, and the document mode meeting the user's expectation is obtained.

A system and method to generate a website for a user system. A set of static assets associated with the website are generated. A first document of a first data record is transformed from a first schema to a second schema to generate a first transformed document. A data graph associated with the user system is generated, where the data graph includes the first transformed document and a portion of HTML content of a data record received via a first data stream of a set of data streams. A web page including the portion of HTML content is rendered. The set of static assets and the web page of the website are provisioned to the user system.

The invention discloses a semantic distributed lock implementation method and system based on a database, belongs to the technical field of database kernel extension, and is used for solving the technical problem that an existing distributed lock scheme cannot provide more comprehensive and efficient distributed lock processing capacity for a PostgreSQL ecosystem on the premise that third-party middleware is not introduced. The method comprises the following steps of: creating a lock element data table in a PostgreSQL (Structured Query Language) database; developing a C extension module, and packaging a request lock function and a release lock function in the PostgreSQL kernel into a C function interface; constructing parameters of a request lock function and a release lock function according to the lock element data table; constructing a lock mode conversion interface, and configuring a lock mode conversion rule in the lock mode conversion interface to realize conversion between an original lock mode and a new lock mode; and configuring an overtime timer for a request lock interface and a lock mode conversion interface of the PostgreSQL database, and setting the maximum waiting time of the overtime timer.

A kind of data import method based on relational large-scale multi-mode data, belong to computer technology field;Original data file, storage format and data model are obtained;Through data conversion module, original data file data information is obtained by parsing, reading and structuring;Data information is generated uniform relational data model file by block parallel writing method, and is stored as the same file storage format CSV;Pretreatment is carried out by data association module;Mode information is extracted, and mode information is sent to mode conversion module, and mode conversion module automatically generates relational table and establishes DDL statement;DDL statement and CSV file are sent to SQL processing terminal, and multi-mode data is uniformly converted into relational data by the COPY statement and DDL statement of database, and is stored in the bottom storage structure of relational database.The application stores multi-mode data in the same storage layer, so that query can be directly called from database storage layer, and data conversion efficiency is improved.

A system and method to generate a website for a user system. A set of static assets associated with the website are generated. A first document of a first data record is transformed from a first schema to a second schema to generate a first transformed document. A data graph associated with the user system is generated, where the data graph includes the first transformed document and a portion of HTML, content of a data record received via a first data stream of a set of data streams. A web page including the portion of HTML content is rendered. The set of static assets and the web page of the website are provisioned to the user system.

A computing system and method that receives design input from a source system, processes the design input using artificial intelligence to identify distinct components, generates standardized representations of the components, performs similarity analysis to match the components with existing templates from a database, extracts relevant data from the components and maps the data to appropriate schema structures, and converts the populated schemas into a format compatible with a target system. The automated process leverages machine learning models and multimodal analysis techniques to transform visual designs into functional content without manual intervention, improving efficiency and consistency in content creation workflows.

A computer implemented method includes receiving, by the processor, a request to initiate a migration of data. The method also includes receiving, by the processor, data identified in the request to initiate a migration. The method also includes generating, by the processor, a file-out of the data by converting the data from a proprietary schema into a commonly understood schema. The method also includes migrating, by the processor, at least a portion of the file-out to a cloud provider. The method also includes validating, by the processor, the migration of the file-out to determine whether the migration is successful, and in response to validating the migration, generating, by the processor, a migration response indicating whether the migration was successful.

The present application relates to warehouse management regulation and control technical field, especially a kind of digital supply chain capacity state self-adapting dynamic regulation and control method, comprising: obtaining the initial state data of supply chain capacity unit, initial state data includes geographical position and initial capacity level time series;Based on initial state data and preset period's flow data.The present application constructs recursive partition tree by fusing geographical, traffic and instruction response multidimensional logistics coupling characteristics, realizes hierarchical accurate representation of capacity state by combining dynamic mode conversion and flow clustering;Establish the bottom-up asynchronous update and local reconstruction mechanism triggered by deviation, while significantly reducing the global computing overhead, ensure the agile feedback of system to real-time fluctuation;Finally, the state evolution is synchronously predicted by using partition tree as the skeleton of graph neural network, and the fine regulation and control instruction is generated by driving decision model, which realizes the early warning and efficient collaborative scheduling of supply chain capacity risk.