53results about "Semi-structured data retrieval" patented technology

The invention discloses a data quality monitoring method and system based on a knowledge graph. According to the method, data tables and fields in multi-source heterogeneous data are constructed into graph nodes, graph edges are constructed through main and foreign key constraint relationships and field value overlapping analysis, and a data blood relationship knowledge graph is formed; business rules are coded into the atlas to execute data constraint check, and an abnormal data influence range is traversed and tracked through a directed graph; and dynamic monitoring is realized based on the time change of the abnormal score sequence. The invention further provides system implementation including data acquisition and preprocessing, knowledge graph construction, quality monitoring, problem diagnosis and quality evaluation. The method solves the problems that a traditional method is difficult to track the influence range of abnormal data and lacks systematic evaluation, and improves the automation level and accuracy of data quality management.

Systems for providing multi-access edge computing (MEC) and content delivery. At least one cloud platform and at least one server node are provided. Hypertext Transfer Protocol (HTTP) requests are operable to be send between the at least one cloud platform and at least one edge device. The at least one server node is registered in a cloud platform global registry using an HTTP POST request to a global service address, and the cloud platform global registry is associated with the at least one cloud platform. The at least one server node is operable to create at least one cellular network and provide cellular network services to the at least one edge device. The present invention is also operable to collect and analyze data from an IoT sensor network and take actions based on sensor data using the same node infrastructure and edge computing platform.

In order to apply a text-based label inference approach to infer a label to be assigned to target data in a common data form, an inference apparatus (1) includes: a data converting section (11) for converting target data subject to label assignment into text; and a label inferring section (12) for inferring a label to be assigned to the target data, in accordance with a label inference model for inferring a label to be assigned to text and the text obtained by the converting carried out by the data converting means.

The invention discloses a network communication method and system based on an XML database, and relates to the technical field of data processing and information.The method comprises the steps that data requests from an external system are collected, the received data requests are analyzed, and key information of the data requests is extracted; the received data is converted into an XML format, multiple coding standards are supported, and a data stream is formed; adding metadata to the data stream, adding the metadata into the data stream, merging the metadata with the original data, and generating a final XML effective load; identifying each data item in the XML data, evaluating the importance of each data item, determining the priority of data processing, and storing a processing result in a database; and for a processing result in the database, a requester permission is set, a permission level is calculated, and finally the access permission of the data is determined.

Artificial intelligence (AI) systems may manipulate search results about an entity to inject a predetermined bias into the search results. A bias reduction artificial intelligence (AI) system and method may perform the same automated search about the entity on open source and closed source AI systems. The AI-generated search results for the open and closed source AI systems may be compared to determine differences in results. The differences may be analyzed to determine attempts by the AI systems to manipulate search results about the entity to inject predetermined bias. If an attempt at predetermined bias is identified, the bias reduction AI system may reduce the predetermined bias by sharing data about the entity between the open source AI system and the closed source AI system to cause the system to use machine learning to update the algorithms and the first and second data sets. Incentives may also be provided to the open and closed source AI systems to reduce biases.

The invention discloses a semi-structured data analysis method and device and a medium, and the method comprises the steps: collecting semi-structured data from a preset data source, recognizing the data type of the semi-structured data, and determining an analysis mode corresponding to the semi-structured data according to the data type; wherein the analysis mode is divided into a direct analysis mode and an indirect analysis mode according to whether the data content can be directly obtained or not; on the basis of a direct analysis mode, determining an organization relationship among content elements in the semi-structured data, and reading data contents corresponding to the semi-structured data according to the organization relationship; based on the indirect analysis mode, performing region division on the semi-structured data, and obtaining data content corresponding to the semi-structured data through each data component obtained after division; and generating an embedded vector corresponding to the data content, and storing the embedded vector into a vector database to realize analysis of the semi-structured data.

The invention discloses a tracking method of a semi-structured dialogue state, a medium and electronic equipment, and belongs to the technical field of computers, a dialogue state model based on UML is designed to represent and track the dialogue state, the dialogue state model comprises multiple rounds of decision making processes, priori knowledge of a service is abstracted, and the decision making efficiency is improved. According to the method, the information density of a single dialogue sample is increased in the state of each round of dialogue process, so that the search space of the dialogue state is reduced, the requirement of a DST model for the number of training corpus samples is reduced, the cold start problem of model training is solved, the samples are generated on the basis of the dialogue state model, and the efficiency is improved. The DST model can continuously learn and adjust in a dynamic environment within a certain range through reinforcement learning to adapt to new data or tasks, the content output by the model can be controllable, the precision of processing complex tasks is improved, and the problem that the model cannot track a long dialogue state is solved.

The technology provides a rich attention mechanism for structured information extraction of web pages and other electronic documents. An input layer of a model obtains system, information associated with the document, including field tokens representing respective fields to be extracted from the document, structured document type tokens associated, and text tokens from a text sequence in the document. An encoder connects the field tokens, the S type tokens and the text tokens according to a set of different attention patterns. The encoder generates an overall token representation based on the set of different attention patterns. An output layer of the model extracts a final text span for the each of the respective fields from the set of text tokens. The extracted final text span for each of the respective fields is stored in memory, and can be produced in response to a search query, analytics evaluation or other request.

The present invention relates to autonomous tuning of a data grid of documents in a database. Herein are techniques for storage cells to autonomously maintain local indices and other optimization metadata and algorithms to accelerate selective access into a distributed collection of documents. In an embodiment, each storage cell persists a respective subset of documents. Each storage cell stores, in memory, respective index(s) that map each item to location(s), in one or more documents of the respective subset of documents, where the item occurs. One or more computers execute, based on at least a subset of the indices of the storage cells, a data access request from a database management system. In an embodiment, a cloud of JSON document services provides an easy-to-use, fully autonomous JSON document database that horizontally and elastically scales to deliver fast execution of document transactions and queries without needing tuning by a database administrator.

The invention discloses an electric power system XML generation method and system based on mxGraph and G6, and relates to the technical field of computer platform load balancing, and the method comprises the steps: collecting first object parameter data, analyzing the parameter data to obtain influence parameters, converting the influence parameters to obtain graph model data, and storing the graph model data in a database; and establishing a format conversion model to analyze and adjust the graph model data to obtain XML format data. According to the power system XML generation method based on mxGraph and G6, through combination of mxGraph and G6 technologies, the system can provide more flexible graphical operation and real-time feedback, and the interactive experience of a user in the power system design process is enhanced. An efficient graph model data analysis and conversion mechanism is introduced, so that the power system data can be quickly and accurately converted into a standardized XML format.

An embodiment of the present invention discloses a unified description and shared discovery method for intelligent models, comprising the following steps: S10: using PMML to describe the relevant information of the intelligent model to form a PMML model description document, storing the PMML model description document in an XML database, and storing the intelligent model in an intelligent model library; S30: encapsulating the intelligent model to form an intelligent simulation model; S50: responding to an execution instruction to realize the shared discovery of the intelligent model.

The present disclosure relates, inter alia, to a computer-implemented read-only data structure for archiving snapshots of an RDF dataset into a file, the RDF dataset comprising one or more graphs, the read-only data structure being able to be queried directly using all possible triple patterns of a SPARQL query engine. The data structure includes: an indexed list of one or more graphs of an RDF data set; for each graph of the RDF dataset, an indexed list of predicates; mapping each RDF lexical item of the data set to a corresponding index and a dictionary mapped in reverse; and for each predicate indexed in the indexed list of predicates of each graph, representing an adjacency matrix of a set of tuples of the RDF dataset, thereby obtaining a complete representation of the RDF dataset that can be queried directly using all possible triple patterns of the SPARQL query engine.

A system and method of the disclosure relates to structuring at least one resource of an application programming interface (API) for a server. In the method, a plurality of field-level elements may be defined. Each of the plurality of field-level elements may be associated with a respective property. Also, first and second message-level elements may be defined. The first message-level element may be based on a first field-level element of the plurality of field-level elements, and the second message-level element may be based on the first field-level element. The API may be associated with a first resource having properties resolved based on at least the first and second message-level elements.

A method involves detecting primary entities in a document, involving determining that a subset of the primary entities are associated with a first primary entity type, and determining a second primary entity type of one of the primary entities. The method further involves processing the primary entity of the second primary entity type to determine a secondary entity type of the primary entity. The secondary entity type is a subcategory of the second primary entity type. The method also involves hierarchically organizing the primary entities into a document layout structure that includes a top level and a child level. The top level is established by the first subset of primary entities based on the first primary entity type identifying the first subset as headings, and the child level is established by the primary entity based on the second primary entity type, the child level identifying the secondary entity type.

A computer-implemented method for generating by a query engine a graph of operators for a SPARQL query over an RDF graph. The method includes obtaining a graph of operators executable by the query engine, the graph comprising a plurality of basic operators, at least two of said operators being of a first type each configured to find RDF triples of the RDF graph that match a respective basic graph pattern. The method further comprises identifying a group of operators among the at least two basic operators of the graph which are of the first type. The respective basic graph patterns of the group of operators have same subject and / or predicate and / or object and the identified group of operators is replaced in the graph by an equivalent operator configured to find RDF triples of the RDF graph that match the respective basic graph patterns of the group of operators.

The present invention discloses a data quality monitoring method and system based on a knowledge graph. The method constructs data tables and fields in multi-source heterogeneous data as graph nodes, constructs graph edges through primary and foreign key constraint relationships and field value overlap analysis, and forms a data kinship knowledge graph; encodes business rules into the graph to perform data constraint checks, and tracks the impact range of abnormal data through directed graph traversal; and implements dynamic monitoring based on the time changes of abnormal score sequences. The present invention also provides a system implementation including data collection preprocessing, knowledge graph construction, quality monitoring, problem diagnosis, and quality assessment. The present invention solves the problem that traditional methods are difficult to track the impact range of abnormal data and lack of systematic assessment, and improves the automation level and accuracy of data quality management.

Technology for creating and managing a geospatial network model (hereinafter "network model") for a distribution network in a client environment using geospatial information system (GIS) data is disclosed. In some embodiments, the disclosed technology enables access of a network model for a distribution network stored on a client device, detection of one or more changes to the distribution network associated with the network model, validation the one or more changes to the distribution network against one or more validation rules and transmission of the one or more changes to the distribution network to a GIS database to update the current state of the distribution network in the GIS database.

Data loss prevention may be applied to data that includes a plurality of records, with each record including a plurality of fields and with each field corresponding to a different one of a plurality of categories. Applying of data loss prevention may include selecting a subset of records, computing, for each category, a likelihood the category contains the sensitive information, selecting a subset of categories based on the computed likelihoods, searching the sensitive information in the selected subset of categories, and in response to detection of sensitive information in at least one of the subset of records, taking one or more data loss prevention related actions. The selected subset of records includes fewer records than the plurality of records, may include a number of non-consecutive records, and the subset of records may be selected such that the records are well distributed within the plurality of records of the data.

The present invention relates to context-aware feature embedding using deep recursive neural networks and anomaly detection of sequential log data. Techniques are provided herein for contextually embedding features of network traffic or operation logs for anomaly detection based on sequential prediction. In an embodiment, a computer has a predictive recursive neural network (RNN) for detecting anomalous network flows. In an embodiment, the RNN transcodes a sparse feature vector representing a log message into a dense feature vector in context, which may be predictive or used to generate a predictive vector. In an embodiment, graph embedding improves the feature embedding of log traces. In an embodiment, the computer detects and feature-encodes independent traces from related log messages. These techniques can detect malicious activities by performing anomaly analysis on context-aware feature embedding of network packet flows, log messages, and / or log traces.

To solve a problem in which a user of data created by a creator may have difficulty in extracting necessary information from the data, the creator being allowed to freely select a format of the spreadsheet data.SOLUTION: A data processing system 10 includes a first processor 22, a first storage device 24, a second input device 36, and a second output device 38. The first processor 22 is configured to: (a) acquire an input file including tabular data to be stored in a first storage device 24; (b) output an interface to the second output device 38, for inputting first information that defines element relationships between a plurality of elements in the tabular data; (c) generate semi-structured data including the element relationships defined by the input first information from the tabular data included in the input file stored in the first storage device 24; and (d) generate an output file including the generated semi-structured data.SELECTED DRAWING: Figure 10

The application provides a lock execution method and device based on a transactional key-value database, equipment and medium, which can be applied to the field of electric digital data processing. The method comprises the following steps: in response to a first transaction operation request for a target resource in a transactional key-value database, reading a first lock key and a first lock value of a distributed lock corresponding to the target resource, wherein the first lock value comprises an instance identifier holding the distributed lock and valid timestamp information of the distributed lock, and the first transaction operation request comprises a target instance identifier and a lease duration of the distributed lock; determining an occupation result of the distributed lock according to the first lock key and the first lock value; in the case that the occupation result represents that the distributed lock is not occupied, updating the instance identifier and the valid timestamp information in the first lock value according to the target instance identifier and the lease duration, so that the target instance executes a transaction operation indicated by the first transaction operation request.

The invention provides a lock execution method, device and equipment based on a transactional key value database and a medium, which can be applied to the field of electric digital data processing. The method comprises the steps that in response to a first transaction operation request for a target resource in a transactional key value database, a first lock key and a first lock value of a distributed lock corresponding to the target resource are read, the first lock value comprises an instance identifier with the distributed lock and effective timestamp information of the distributed lock, and the instance identifier and the effective timestamp information of the distributed lock correspond to the target resource; the first transaction operation request comprises a target instance identifier and lease duration of the distributed lock; determining an occupation result of the distributed lock according to the first lock key and the first lock value; and under the condition that the occupation result represents that the distributed lock is not occupied, according to the target instance identifier and the lease duration, updating the instance identifier and the effective timestamp information in the first lock value, so that the target instance executes a transaction operation indicated by the first transaction operation request.

Methods and systems are provided for enhanced data loss prevention. Data loss prevention may be applied to data that includes a plurality of records and a plurality of categories, with each record including a plurality of fields and each field corresponding to a different one of the categories. Applying of data loss prevention may include selecting a subset of records from the plurality of records; scanning fields of the selected subset of records for sensitive information; computing based on a result of the scanning, for each category, a likelihood the category contains the sensitive information; selecting a subset of categories based on the computed likelihoods of the categories to contain the sensitive information; searching the sensitive information in the selected subset of categories; and in response to detection of sensitive information in at least one of the subset of records, taking one or more data loss prevention related actions.

Systems and Methods for Presenting Search Results and Computer Readable Media. A system identifies a document related to a search term, in which the document includes a set of structural elements. The system determines a distribution of occurrences of the search term in the document, identifies one of the structural elements, based on the distribution of occurrences of the search term in the document, and presents information associated with the identified structural element.

Disclosed herein are system, method, and device embodiments for a data bridge adapter in a data integration platform that models any application programming interface as an entity-relationship model. This technique allows an individual using an integration platform to map and transform the entity-relationship model without having to create any custom code. The user may specify target parameters as part of an ETL process, and the entity relationship model may allow appropriate data and API calls to be generated to pass the data to the specified target. By creating an entity-relationship model from any API, the technique further supports versioning, schema change resolution, compatibility detection and other features. Furthermore, data visualization software may use the entity-relationship model to allow users to explore and analyze the data represented in the entity-relationship model.

This real-time data processing system, based on Web architecture Canvas technology, belongs to the fields of automated report logic engine technology and web page table processing technology. The invention includes a data extraction module, a data parsing module, a data storage module, a real-time display module, and an online modification module. The data extraction module dynamically extracts business data from the business system to be analyzed. The data parsing module parses the extracted business data using a built-in parsing model and a custom parsing model. The data storage module stores the business data and the results of the data parsing. The online modification module performs real-time online modification and statistical analysis on the data to be displayed, while synchronizing the modified data to the data storage module. The real-time display module provides an interface with the operator. This invention is compatible with historical data from traditional business personnel and allows business personnel to manually configure and display data results in real time.

Method for data loss prevention in data, wherein the data are structured and / or semi-structured data with a plurality of records and a plurality of categories, wherein each record comprises a plurality of fields each belonging to a different one of the categories, the method comprising the following steps: selecting, in a processor, a subset of records of the data, wherein the selected subset comprises at least two records between which the data comprise a plurality of records which are not included in the subset; scanning, in the processor, the fields of the selected subset of categories for sensitive information; computing, in the processor, for each category a likelihood to contain the sensitive information on the basis of the scanning result; selecting, in the processor, a subset of categories on the basis of the computed likelihoods of the categories to contain the sensitive information; and searching, in the processor, the sensitive information in the selected subset of categories.

Technologies are described herein for executing queries expressed with reference to a structured query language against unstructured data. A user issues a structured query through a traditional structured data management (“SDM”) application. Upon receiving the structured query, an SDM driver analyzes the structured query and extracts a data structure from the unstructured data, if necessary. The structured query is then converted to an unstructured query based on the extracted data structure. The converted unstructured query may then be executed against the unstructured data. Results from the query are reorganized into structured data utilizing the extracted data structure and are then presented to the user through the SDM application.

The technology provides a rich attention mechanism for structured information extraction of web pages and other electronic documents. An input layer of a model obtains system, information associated with the document, including field tokens representing respective fields to be extracted from the document, structured document type tokens associated, and text tokens from a text sequence in the document. An encoder connects the field tokens, the S type tokens and the text tokens according to a set of different attention patterns. The encoder generates an overall token representation based on the set of different attention patterns. An output layer of the model extracts a final text span for the each of the respective fields from the set of text tokens. The extracted final text span for each of the respective fields is stored in memory, and can be produced in response to a search query, analytics evaluation or other request.