103 results about "Data discovery" patented technology

A multimodal content management system having a block-based data structure can include a query engine configured to perform automatic data discovery for natural language queries. The system can generate and render, at a computing device, a page comprising a graphical user interface (GUI) with a displayable item from a first block of a block-based data structure. The system can generate and bind, to the page, a schema definition comprising a first reference to the first block and a second reference to a set of blocks, wherein the first block is relationally linked to the set of blocks via the second reference. The system can use at least a portion of a natural language prompt, received at the GUI, to generate an input feature for a large language model, the input feature having a schema-question pair that includes at least a portion of the schema definition. The large language model can generate a query configured to operate on the block-based data structure.

The invention relates to the technical field of data management, and discloses a business and financial data governance optimization method for main data dynamic management, which comprises the following steps: step 1, carrying out multi-source data acquisition, including business system data acquisition, financial system data acquisition and other channel data acquisition, step 2, pre-processing the acquired data, numbering and storing, and step 3, carrying out data management on the acquired data. 3, calculating a data quality index, a data change condition, a data value and a data comprehensive coefficient based on the stored data set, and 4, performing feedback according to the data comprehensive coefficient, so that various data sources inside and outside an enterprise can be comprehensively covered, a data island phenomenon is reduced, and meanwhile, the quality and credibility of data are improved; the data comprehensive coefficient is comprehensively evaluated by combining the data quality index, the data change condition and the data value, the data level is comprehensively reflected, a comprehensive perspective is provided for enterprises to see the data, and the enterprises can better understand and manage the data and find potential problems and opportunities.

Disclosed are some techniques for implementing a data discovery agent in a network associated with a customer organization of a data privacy management system. Some implementations relate to one or more connections with one or more data sources storing private data of the customer organization. The one or more data sources can be scanned to obtain scanned data from the private data. The scanned data can be processed, including anonymizing the scanned data, to obtain preprocessed data for use by one or more classification operations. The preprocessed data can be shared with the data privacy management system. One or more classification promotion operations can be performed on classified data elements.

The invention provides a sensitive data discovery method based on complex semantic analysis, which innovatively introduces high-dimensional sparse Fourier transform (HSFT), maps semantic characteristics to a frequency domain space, captures semantic change frequency characteristics (such as high-frequency mutation characteristics of sensitive information) which are difficult to recognize by traditional deep learning, and improves the recognition accuracy of the sensitive data. Sensitive information and non-sensitive information in similar contexts are effectively distinguished, and the problems of high misjudgment rate, lack of dynamic adaptability and unreliable risk assessment in complex contexts in the prior art are solved.

The invention discloses a campus safety risk monitoring method and system, relates to the technical field of campus safety, and solves the problem that existing campus safety monitoring cannot be combined with other data to find potential risks. Timely early warning cannot be carried out, and a normative processing flow is prompted; according to the scheme, the method comprises the steps that monitoring data and psychological evaluation data are acquired; determining important attention personnel according to the psychological evaluation data; the risk level of the personnel behavior is identified according to the monitoring data, an early warning signal is generated based on a graded early warning rule, and the graded early warning rule comprises: early warning signals are generated for medium-risk behaviors or all high-risk behaviors of the key concerned personnel; the early warning signal is pushed to the corresponding responsibility management personnel and the nearby patrol personnel; according to the accident type fed back by the nearby patrol personnel, an associated guidance plan is pushed, and the associated guidance plan is used for guiding standard handling of the accident; generating a keyword according to the accident type, and carrying out public opinion monitoring on a website; the problems are solved.

Secure distributed document discovery via vector similarity is performed by a data discovery service. The service receives a request to perform a search using input data. The service sends the input data and an embedding protocol to different data repositories that comprise vector databases. The service receives a results from the different data repositories; each result identifies documents and corresponding document similarity scores. The service generates an overall ranking of the documents according to the document similarity scores. The service returns a query result that identifies the at least a portion of the documents and indicates the ranking of the identified documents.

The invention discloses a business operation and maintenance big data value mining method based on multiple fields, and belongs to the technical field of data analysis, and the method specifically comprises the steps: obtaining enterprise multi-source heterogeneous data streams, carrying out real-time classification and recognition, and automatically dividing business data types and security levels; implementing dynamic security protection processing according to a classification identification result, establishing a hierarchical access control system, and constructing a cross-service association analysis model in a security boundary; the method comprises the following steps: identifying a data combination with a synergistic effect by analyzing service data, and automatically generating a cross-department data sharing request when the synergistic effect reaches a preset standard; a controlled temporary analysis environment is established, and potential operation optimization points and business opportunities are found through combinatorial analysis of data of different business systems; according to the data association features and the use mode identified in the analysis result, an optimization security protection strategy and analysis model parameters are fed back; according to the invention, the depth, breadth and security of enterprise big data value mining are improved.

Systems and methods are provided for in-situ sensing and data analysis for industrial processes (such as additive manufacturing processes). An eddy current sensor array may be repeatedly scanned across the part during its fabrication to collect in-situ data to assess the quality of the part. defects discovered by processing the scan data may be eliminated or mitigated by subsequent fabrication steps. A processing technique is described that can identify defects by filtering across the multiple scans. For example, during a laser powder bed fusion (LPBF) process, the sensor is scanned after each build layer; the resultant in-situ data is processed in the build or "z" direction using the disclosed filtering methods. The results are used to assess the quality of the part or adjust the fabrication process. Also disclosed are system configurations illustrating how an industrial process may be instrumented for such data collection.

The invention discloses an electronic price tag management system and a data processing method, and the system comprises an electronic price tag terminal, a data collection and transmission module, an edge calculation unit, a cloud server, and an intelligent analysis platform. The data processing method of the electronic price tag management system comprises the following steps: S1, data acquisition and preprocessing; S2, behavior feature extraction and analysis; S3, data fusion and deep analysis; and S4, decision support and application. According to the invention, through multi-modal data acquisition, customer demands are accurately captured. The action capture and voice recognition module recognizes actions and voices of customers, and shopping convenience and satisfaction are improved; by analyzing customer behaviors and feedback data, new market opportunities are found, innovative products or services are launched, more customers are attracted, and sales and profit are increased.

The invention discloses an industrial sensitive data security management platform and method, and relates to the field of industrial data security management.The industrial sensitive data security management platform collects multi-dimensional industrial data source information through a data discovery and classification grading module, and analyzes data sensitive information by fusing AI deep learning and a traditional matching algorithm; obtaining a data sensitivity judgment result and business value evaluation, and constructing a multi-dimensional classification and grading matrix and a visual data asset map; an access control strategy is generated through a differential protection module; global protection measures are deployed based on an access control strategy, and authority control, data encryption, storage and transmission and differential desensitization are implemented; the protection effect is verified through the continuous monitoring and auditing module, abnormal behaviors are monitored and traced, and the response and recovery module carries out response and service recovery on industrial sensitive data security events; the problems that industrial sensitive data is difficult to manage and control in the whole life cycle and the protection pertinence is insufficient are effectively solved, and the efficiency and stability of data security protection are improved.

Artificial-intelligence computer-implemented processes and machines predict whether personal data may be present in structured software based on metadata field(s) contained therein. Natural language processing preprocesses input strings corresponding to the metadata field(s) into normalized input sequence(s). Individual characters in the sequence(s) are embedded into fixed-dimension vectors of real numbers. Bidirectional LSTM(s) or other machine-learning algorithm(s) are utilized to generate forward and backward contextualization(s). Neural network output(s) are provided based on element-wise averaging or feed forwarding based on the contextualization(s) in order to predict whether one or more value fields corresponding to the metadata field(s) may contain personal data.

The invention discloses a driving reference information providing method, electronic equipment and a computer readable medium, and relates to the technical field of big data processing and mining. The method comprises the following steps: acquiring the accident occurrence frequency of each road section in a first time period closest to the current time; if the target road section with the accident occurrence frequency larger than or equal to the first frequency threshold value is detected, historical data of one or more adjacent road sections in a second time period closest to the current time are obtained, and driving reference information is sent to vehicles about to drive into the adjacent road sections according to the historical data of the adjacent road sections. According to the embodiment, the road section with large potential danger can be found according to the historical data of the predetermined driving operation and / or the predetermined vehicle state, and the practical driving reference information is effectively provided for the user in time.

Systems and methods for generalized structured data discovery utilizing contextual metadata disambiguation via machine learning are disclosed. A method may include receiving physical application metadata for an attribute, a database object, or a database; receiving reference data including tokens and their associated abbreviations and acronyms; parsing the physical application metadata into application tokens including known application tokens and unknown application tokens; identifying unknown application tokens by comparing the parsed application tokens to a corpus; performing probabilistic parsing on the unknown application tokens using the reference data resulting in token phrases; identifying monosemous tokens in the token phrases using a look-up dictionary; replacing the monosemous tokens with their expansions from the look-up dictionary; and outputting a mapping of the physical application metadata to enhanced physical application metadata that includes an expression for the physical application metadata comprising the expansions for monosemous tokens in a supported language.

A system and method of autonomous multi-dimensional segmentation for a supply chain network. Embodiments include a supply chain network of one or more supply chain entities, a segmentation planner having a computer and memory, the segmentation planner configured to access input data relating to one or more supply chain entities, discover one or more features related to the input data, pre-process the input data and features, perform multi-dimension segmentation on the input data, generate one or more segment output visualizations, assign policy parameters to the multi-dimension segmentation performed on the input data.

A method for diagnosing and remediating system anomalies in a managed network using artificial intelligence (AI). The method includes receiving data from managed devices, discovering anomalies indicative of events in the network, and using an AI engine to analyze the anomalies and related data to determine additional relevant information. A data gathering mechanism is generated based on the AI engine to collect this additional information, which is then distributed to managed devices. The collected data is used to determine alterations to the network to resolve the anomalies, and these alterations are implemented in the managed network.

A data platform for providing large scale data discovery with near real time data cataloguing and detailed lineage. Data is received from one or more sources, and the received data is written to a data storage systems. The received data is processed at the data storage systems using sensors at the data storage systems. Metadata and data definitions associated with the data are sent to a data service in near real-time. A data catalog is generated at the data service based on the metadata and data definitions associated with the data. Data lineage for the data is created at the data service. The data catalog and data lineage are presented at a data discovery interface.

Method and system for communicating with user equipment, UE, the method comprising the steps of a reader device receiving a request to interrogate one or more UE. In response to the request, the reader device transmitting an initial message to the one or more UE. The one or more UE responding to the initial message by transmitting a first message. The reader device responding to the first message by sending a second message to the one or more UE unless the request received by the reader device to interrogate the one or more UE comprises data indicating that the second message is not required. When a second message is sent by the reader device to the one or more UE, the one or more UE responding to the second message with a third message unless data is received from the reader device by the one or more UE indicating that a response to a second message is not required.

Techniques for database management are described. A database management system may transmit a request for a data management system of a database to provide a set of metadata attributes for structured data within the database, and may receive a set of metadata attributes for the structured data within the database. The data management system may perform a pattern matching procedure to evaluate the set of metadata attributes for the structured data within the database against one or more patterns associated with a data type to determine one or more locations within the database that include structured data of the data type. Based on the pattern matching procedure, the data management system may output an indication that the one or more locations within the database include structured data of the data type.

The invention relates to an equipment network distribution method and device, electronic equipment and a storage medium, and the method comprises the steps: determining a target house type template which comprises the layout information of virtual room nodes and equipment configuration data corresponding to the virtual room nodes; equipment in a to-be-distributed network state is found, and physical position information of the equipment is acquired; determining a virtual room node to which the equipment belongs in the target house type template according to the physical position information of the equipment and the equipment configuration data; and performing network distribution processing on the equipment to enable the equipment to access a network control domain corresponding to the virtual room node to which the equipment belongs. Therefore, batch processing and an automatic network distribution process can be realized, and in a large-scale deployment scene of the smart home system, the equipment network distribution efficiency can be greatly improved, manual intervention and operation errors can be reduced, and the labor cost can be reduced.

The application provides a sensitive data discovery method based on complex semantic analysis, which innovatively introduces high-dimensional sparse Fourier transform (HSFT), maps semantic features to a frequency domain space, captures semantic change frequency characteristics (such as high-frequency mutation characteristics of sensitive information) that are difficult to identify by traditional deep learning, effectively distinguishes sensitive and non-sensitive information in similar contexts, and solves the problems of high misjudgment rate, lack of dynamic adaptability and unreliable risk assessment in the prior art in complex contexts.

A method and system for communicating with a user equipment, UE, the method comprising the step of a reader device receiving a request to interrogate one or more UEs. In response to the request, the reader device sends an initial message to the one or more UEs. The one or more UEs respond to the initial message by sending the first message. The reader device responds to the first message by sending a second message to the one or more UEs unless the request received by the reader device to interrogate the one or more UEs includes data indicating that the second message is not required. When a second message is sent by the reader device to the one or more UEs, the one or more UEs respond to the second message with a third message unless the one or more UEs receive data from the reader device indicating that there is no need to respond to the second message.

The application discloses a tokamak discharge modeling system based on a bidirectional long short-term memory neural network, which is composed of multiple modules, and the modules are low-coupling functional units.The modules at least include a data transfer module, a Batch data access input module, a training self-defined parameter module, a bidirectional long short-term memory neural network modeling module and a data visualization module.The whole system architecture is mainly divided into two paths, namely data training and data modeling.Using the visualization technology, the tokamak discharge experiment personnel can refer to the discharge modeling results and visualize the model training process.The application can one-key type in the experiment proposal stage to model the whole process of the tokamak discharge curve in advance, so that the experiment personnel and the proposal design personnel can check the validity and rationality of the proposal.The application can also be used for assisting data discovery after the experiment.

The application discloses an industrial sensitive data security management platform and method, relates to the field of industrial data security management, and collects multi-dimensional industrial data source information through a data discovery and classification grading module, fuses AI deep learning and traditional matching algorithm to analyze data sensitive information, obtains data sensitivity judgment results and business value evaluation, and constructs a multi-dimensional classification grading matrix and a visual data asset map; an access control strategy is generated through a differentiated protection module; global protection measures are deployed based on the access control strategy, permission control, data encryption storage transmission and differentiated desensitization are implemented; the protection effect is verified through a continuous monitoring and auditing module, abnormal behavior is monitored and traced, and a response and recovery module responds to industrial sensitive data security events and restores business; the problems of difficulty in controlling industrial sensitive data in the whole life cycle and insufficient protection pertinence are effectively solved, and the efficiency and stability of data security protection are improved.

The application discloses a kind of dimension self-adapting human behavior prediction method and equipment, the method first acquires the D-dimensional time series containing human behavior, carries out modal decomposition processing, obtains each dimension mode.Secondly, for each mode, the internal relationship between the implied state, activity contribution and observation event is constructed based on the dynamics equation.Then estimate each parameter of the dynamics equation, and construct total parameter set.Finally, the prediction sequence of each mode is inferred based on the total parameter set, and is reconstructed as D-dimensional time series, realize the prediction of human behavior.In the equipment, the central processing module processes the human behavior data collected by the perception module, the abnormal behavior control interaction module alarms, the communication module is used to send the abnormal data back to the personal computer end, and the data is transmitted to the storage module for saving when power failure or network interruption.The application does not need prior knowledge when carrying out human behavior prediction, low cost and high accuracy.

The invention provides an orchestrable CMDB data discovery method and system based on RuleGo. The method comprises the following steps: creating a user-defined RuleGo node; compiling a rule script by using a domain specific language DSL provided by RuleGo, and defining an operation executed every time a specific condition is met; and a mapping mechanism is created and set, and configuration data structure fields in the Nacos are mapped and converted into formats suitable for being stored in the CMDB. According to the method, data synchronization between the customized RuleGo node processing component and the CMDB is created, all logics are concentrated in one RuleGo node, and when the Nacos is upgraded or the API of the Nacos is changed, only codes in the node need to be modified, so that the maintenance cost is reduced; even if incompatible changes are introduced into Nacos upgrading, the Nacos upgrading is limited in the self-defined RuleGo node, the CMDB or other system components are not affected, and the stability and reliability of the system are enhanced.

This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that utilize a repository of metadata-based recommendations to classify data sources using metadata from the data sources. For example, the disclosed systems can generate a repository of metadata-based recommendations that indicate recommended classifications for objects within data sources through metadata associated with a data source schema. In some instances, the disclosed systems identify metadata from a data source schema associated with the data source. Subsequently, the disclosed systems can match the identified metadata to a metadata-based recommendation via metadata mappings in the metadata-based recommendation repository to select a metadata-based recommendation. Furthermore, the disclosed systems can also utilize a classifier model to generate predicted labels for the data source and update the metadata-based recommendation repository with a mapping between the predicted labels and metadata corresponding to the data source schema of the data source.

Disclosed are systems and methods for discovering one or more computing assets associated with primary identification data. The systems and methods comprise a series of processes and steps to discover an organization's external attack surface. The processes and steps include building a unique external attack surface management catalog to be used as a configuration value as a first step of discovering unknown internet-facing assets of an organization. Then the processes and steps include using the unique external attack surface management catalog in combination with open-source reconnaissance and proprietary scanners to determine the external attack surface of the organization. The disclosed systems and methods then uniquely present the acquired relevant data to users using a single display screen. The disclosed systems and methods not only discover the external attack surface and internet-facing assets of an organization and its aliases, but also internet-facing assets of related subsidiary, affiliate, and partner entities.

Various embodiments provide methods, apparatus, systems, computing devices, computing entities, and / or the like for identifying targeted data for a data subject across a plurality of data objects in a data source. In accordance with one embodiment, a method is provided comprising: receiving a request to identify targeted data for a data subject; identifying a first data object using metadata for a data source that identifies the first data object as associated with a first targeted data type for a data portion from the request; identifying a first data field from a graph data structure of the first data object that identifies the first data field as used for storing data having the first targeted data type; and querying the first data object based on the first data field and the data for the first targeted data type to identify a first targeted data portion for the data subject.

In general, various aspects provide methods, apparatuses, systems, computing devices, computing entities, and / or the like for performing data discovery on a target computing system. In various aspects, a third party computing connects, via a public data network, to an edge node of the target computing system and instructs the target computing system to execute jobs to discover target data stored in data repositories in a private data network in the target computing system. In some aspects, the third party computing system may schedule the jobs on the target computing system based on computing resource availability on the target computing system.

The application discloses a Web table abnormal data discovery method based on a text semantic mapping relationship. The application aims at discovering abnormal data with fuzzy or even wrong semantic information in a Web table. The method mainly comprises three parts: a semantic representation module, a column type inference module and an error discovery module. First, the semantic representation module represents the meaning of cell text. For a cell in a table, the string text in the cell is represented as a semantic vector according to context information. Then, the column type inference module infers the type of the column where the cell is located, and obtains the mode information of the column. Finally, based on the mapping relationship between the column type and the semantic vector of the cell text of the main column cell and the target cell, abnormal data in the table is discovered and labeled.