71 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.

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.

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.

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 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.

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.

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.

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.

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.

A method utilizes data discovery to support visual analysis of a dataset. A user selects a data source, and the method presents a natural language interface for analyzing the data source. The user specifies an incomplete natural language command directed to the data source, and the method correlates words in the incomplete natural language command to data fields in the data source. The method determines a data type of the data fields and a range of data values of the data fields. From the data type and the range of data values, the method presents one or more autocomplete options for the incomplete natural language command. Each option includes respective text and a respective corresponding visual graphic. The user selects one of the autocomplete options, and the method forms a complete natural language command. The method then displays a data visualization in accordance with the complete natural language command.

The invention discloses an integrated intelligent quality inspection system for a gating unit, which integrates various inspection functions through a hardware test platform, avoids the trouble of frequently replacing tools and setting scenes in a traditional mode, can quickly and continuously detect various performance indexes of the gating unit, shortens the inspection period, improves the efficiency and reduces the cost. The requirements of large-scale production and rapid delivery are met; meanwhile, the hardware test platform can accurately simulate actual working conditions, such as different door opening and closing speeds, accelerations and loads, and provides test conditions close to a real environment, so that a test result more truly reflects the performance of the door control unit; in addition, the intelligent autonomous inspection test module has an intelligent evaluation function, mass data can be deeply analyzed, potential problems can be found, and support is provided for quality improvement. In addition, the intelligent autonomous inspection test module automatically records data and an evaluation report and outputs a quality inspection table, data management is standardized, follow-up query, statistics and tracing are facilitated, and intelligent management of the inspection process is achieved.

This invention discloses a highly robust web crawler system, belonging to the fields of big data acquisition and artificial intelligence technology. The system is a closed-loop intelligent system with multiple modules working collaboratively. It includes a multi-data source database construction and pre-training module, a real-time monitoring module for crawler operation status, a data semantic analysis and association mining module, a data request-matching evaluation and parameter optimization module, and a dataset generation and delivery module. Through tight coupling between modules and effective transmission of data flow and control flow, the system achieves full-process automation and intelligence from data discovery, intelligent crawling, dynamic adaptation to high-quality delivery. The system features high robustness and anti-crawling capabilities, strong data semantic understanding and association accuracy, and low manual costs.

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 system and method of mixed-reality visualization and analysis of multi-dimensional segmentation for a supply chain network. Embodiments include a supply chain network having supply chain entities and a planner. The planner accesses input data relating to the supply chain entities, discovers features related to the input data, pre-processes the input data and features, performs multi-dimension segmentation on the input data, computes the importance of the features, generates a multi-dimension segmentation visualization, and displays the multi-dimension segmentation visualization. The planner further assigns policy parameters to the multi-dimension segmentation performed on the input data, detects outliers in the multi-dimension segmentation visualization, generates a mixed-reality visualization having clusters and segmentation data displayed on three-dimensional mixed-reality objects. Embodiments further including a mixed-reality visualization system that displays a quantity of the three-dimensional mixed-reality objects equal to the number of dimensions of the segmentation data.

The invention discloses a security authentication method and system based on multi-modal biological feature fusion, and relates to the technical field of biological feature recognition. Environmental factor influence performance index data sets are constructed and grouped by using a clustering algorithm; determining an environmental condition classification set containing groups with insufficient light and normal noise, analyzing a change trend by adopting a sliding window method aiming at performance fluctuation under the conditions of insufficient light, noise interference and the like, generating a core adaptation rule set, and verifying corresponding adaptation adjustment parameters through a simulation environment to obtain a core adaptation rule set; finally, a biological recognition authentication output rule set is formed, a comprehensive analysis framework is constructed, various expression data in the authentication process are systematically monitored and combed, a performance change rule is found through the data, the authentication stability and accuracy in a complex environment are ensured, and the authentication efficiency is improved. And the adaptability and the reliability of a biological recognition technology are remarkably improved.

Method for account-specific discovery by a discoverer device pre-provided with (i) a first encryption seed, (ii) a second encryption seed, (iii) a doubly encrypted metadata encryption key (MEK) and (iv) encrypted metadata relating to an account configured on an advertiser device.The discoverer device (a) detects a broadcast advertising message carrying an encrypted device encryption key (DEK) associated with the advertiser device, (b) uses the first encryption seed to decrypt the encrypted DEK and thereby reveal the DEK, (c) maps the first encryption seed to the second encryption seed, the doubly encrypted MEK, and the encrypted metadata, and (d) accordingly uses (i) the DEK and the second encryption seed cooperatively as a basis for decrypting the doubly encrypted MEK and thus revealing the MEK, and (ii) uses the revealed MEK as a basis for decrypting the encrypted metadata and thus revealing the metadata relating to the account configured on the advertiser device.