2983 results about "Data objects" patented technology

Systems, methods, and computer-readable media for modeling cyber resilience data using cyber resilience identities and associated metadata are disclosed. A system can include one or more processing circuits configured to receive or identify cyber resilience data, encrypt a portion of the cyber resilience data, and generate a metadata object including metadata of cyber resilience data. The processing circuits can generate a cyber resilience identity including at least a link with the metadata object, a unique identifier (UID), and a performance event dataset. The processing circuits can encapsulate the cyber resilience identity within a control structure restricting one or more updates and redemptions of the metadata object, determine at least one access data structure being compatible with the control structure, and broadcast, using the control structure, the cyber resilience identity to a ledger or distributed ledger.

Various embodiments of the present disclosure provide an interactive map-based visualization system related to multi-channel search for search domains to improve upon traditional search resolutions within such domains. The techniques may include receiving a user interface request that comprises (i) character-level text input related to a search query via a user interface of a user device and (ii) filter metadata for a user identifier associated with the user interface request, generating a set of query result data objects for the user interface request by correlating the character-level text input to at least one domain knowledge profile, and generating a set of filtered query result data objects for the user interface request by filtering the set of query result data objects using the filter metadata. In some examples, the techniques may include initiating a rendering of a set of selectable graphical element options that are correlated to a real-time map visualization.

The invention discloses a document information input method and system based on big data processing, and belongs to the technical field of document information processing. According to the method, deep risk feature screening is performed on document data by combining big data mining and a knowledge graph, potential sensitive elements can be accurately identified, risk categories and influence ranges are analyzed based on association rules, automatic early warning and real-time risk notification are realized, a dynamic rule knowledge base is adopted, multi-dimensional compliance verification is supported, and the risk assessment efficiency is improved. Service constraint conditions can be updated in real time, the flexibility and adaptability of rules are ensured, the accuracy and timeliness of compliance check are improved, pattern matching and multi-level check are performed on compliance data objects through an intelligent check process and a recheck rule set trained by big data, the preciseness and reliability of data check are ensured, and the data check efficiency is improved. Through comprehensive quantitative calculation of the business complexity, the risk coefficient and the timeliness priority, the task scheduling weight parameter is generated, the receipt auditing path is optimized, and the auditing efficiency is improved.

A system facilitates a process for automatically generating artificial intelligence (AI) models. The system receives a first natural language input from a user that includes a set of phrases and an instruction to analyze data associated with the set of phrases using an AI model. The system accesses a metadata graph to determine a node corresponding to the set of phrases, where nodes in the metadata graph indicate internal data objects stored in data silos. The system processes the internal data objects indicated by the determined node to generate a first set of application data. The AI model is applied to the first set of application data to generate one or more outputs. Additional user inputs can be received to modify the first set of application data and apply the AI model to the modified data, until a desired data pipeline for the model has been constructed.

The invention provides a cold and hot data object life cycle feature extraction method based on deep learning, and the method comprises the steps: carrying out the clustering of feature vectors through employing a clustering algorithm, dividing the data into different viscosity level data clusters according to a clustering result, and enabling the data in each viscosity level data cluster to have similar access modes and life cycle features; for each viscosity level data cluster, analyzing the access time distribution and life cycle stage of the data cluster, and judging the heat type to which the data cluster belongs, including continuous heat, periodic heat, temporary heat and cold; according to the current popularity type and the historical access mode of the viscosity level data cluster, predicting the popularity change trend of the viscosity level data cluster in a future period of time, and obtaining a popularity change curve of each cluster; by monitoring the access condition and the heat change of the data in real time, the migration and replication modes of the data between different storage layers are dynamically adjusted, and when the actual access mode of the data is deviated from the predicted mode, data re-clustering and storage optimization are triggered.

Embodiments are directed to a file system that include object stores. An object store for write requests may be provided. Write ahead log (WAL) entries that include data blocks may be generated. A WAL object may be generated based on the WAL entries and stored in the object store. An in-memory overlay may be updated to associate the data blocks with the WAL object. A checkpoint operation may be executed to: generate an index object that includes index entries that associate other data blocks with data objects stored in the object store; update the index object to include index entries that associate the data blocks with the WAL object; store the updated index object in the object store; update the in-memory overlay to remove the association of the data blocks and the WAL object and update the in memory WAL to remove records of successfully checkpointed WAL objects.

The invention relates to the technical field of cloud computing storage, in particular to a cloud storage dynamic optimization method and system, and the method comprises the following steps: based on a cloud storage data access record, recognizing an access time interval sequence, analyzing the variation amplitude of adjacent time intervals, and calculating the standard deviation of the access time intervals; and determining the access period stability according to the standard deviation and the change amplitude, and classifying the data objects to obtain a data access period stability classification value. According to the method, task scheduling is more accurate by identifying a data access time interval sequence and changes thereof and refining data classification and storage resource allocation, a high-throughput and low-delay storage path is matched, the health condition of a storage node is evaluated, a fault node is identified in advance, data stability is guaranteed, and data copy distribution is adjusted; the storage reliability and stability are improved, the storage path for accessing the intensive data is optimized, the dynamic load balance of the storage resources is realized, and the storage efficiency is improved.

Systems and methods are disclosed for condition identification. One or more processors may receive a member data object with indicators and dimensions, access a member-specific graph network with nodes representing attributes and weighted edges indicating associations, modify the nodes and edges based on the member data object, generate a multi-modal graph database by combining the modified member-specific graph network and a disease graph network, apply the multi-modal graph database to an attention-based graph neural network (GNN) that identifies associations between nodes by dynamically allocating attention weights to edges, generate an embedding data object with node identifiers and vectors representing features and relationships, select a target node associated with condition data, apply the embedding data object to a classification layer that outputs predicted conditions for the target node, and generate the probability of predicted conditions appearing in the target node.

According to the artificial intelligence-based data analysis system and method provided by the invention, a set of novel data analysis system architecture is designed, and key technical components such as natural language processing, a large language model, database query optimization, multi-modal visualization and a semantic knowledge graph are fused; data transmission between modules is realized through unified intermediate data objects such as a structured query intention, an analytic tree and a structured query language template object, and asynchronous collaboration is realized through event driving and a message queue mechanism, so that a non-professional user can input an analysis request through a natural language; semantic analysis, structured query language query statement generation, data query and result visualization presentation are automatically completed, the method can be widely applied to data analysis scenes in the industries of government affairs, traffic, finance, education and the like, the data use efficiency is improved, the technical threshold is reduced, and the digital decision-making ability is enhanced.

Embodiments are directed to a file system that include object stores. An object store for write requests may be provided. Write ahead log (WAL) entries that include data blocks may be generated. A WAL object may be generated based on the WAL entries and stored in the object store. An in-memory overlay may be updated to associate the data blocks with the WAL object. A checkpoint operation may be executed to: generate an index object that includes index entries that associate other data blocks with data objects stored in the object store; update the index object to include index entries that associate the data blocks with the WAL object; store the updated index object in the object store; update the in-memory overlay to remove the association of the data blocks and the WAL object and update the in memory WAL to remove records of successfully checkpointed WAL objects.

The invention relates to the technical field of cloud computing and distributed storage, and discloses a distributed metadata management method and storage system based on cloud computing, and the method comprises the following steps: S1, collecting the access frequency of a plurality of metadata objects in a set time period; s2, performing weighting processing on the access frequency, and constructing access heat distribution under multiple time scales; s3, obtaining the available cache capacity of a plurality of cache nodes, and constructing cache resource weight distribution; s4, assessing the access cost and migration cost between each metadata object and each cache node; and S5, generating a scheduling strategy between the metadata object and the cache node based on a preset optimization target, wherein the optimization target is access cost minimization. According to the method, a heat, resource and cost joint modeling mechanism is constructed, and optimal scheduling and feedback adaptive control are combined, so that accurate decision, dynamic adjustment and migration cost controllability of copy deployment are realized, and the performance and stability of distributed metadata management are effectively improved.

The present invention is related to data processing methods and systems thereof. According to an embodiment, the present invention provides a method of processing documents using a machine learning model. The process begins by accessing data files and extracting information from them, which is subsequently stored. This document information, along with the machine learning model trained on various document formats, is used to classify the data files and generate tabular data. From this tabular data, data objects are created and included in an output data file. The information from the output file is then used to update the data of the machine learning model, optimizing it for improved future document processing. There are other embodiments as well.

The invention relates to the technical field of data processing, and discloses a multi-protocol fusion Internet of Things equipment intelligent gateway data conversion method and system. The method comprises the following steps: collecting a multi-protocol equipment data packet, and extracting protocol features to construct a vector library; protocol types are identified based on the vector library, data are analyzed, and a data object set containing semantic tags is constructed; semantic correlation is analyzed through an adaptive learning algorithm, and a dynamic protocol semantic mapping matrix is established; converting the data into a standard format according to the mapping matrix and recording a matching degree to form a target data pool; and extracting fusion data from the data pool, recoding according to a target protocol format, and outputting a data frame. The problem that an existing multi-protocol fusion data conversion method lacks protocol semantic understanding and self-adaptive learning ability is solved, and semantic consistency and conversion quality of data conversion among multi-protocol equipment are improved.

The invention discloses a distributed data storage management system and method, and relates to the technical field of distributed storage, and the method comprises the steps: obtaining an access log, metadata and content features of each node; data object attributes are judged, cold data are marked, and cold data distribution nodes are positioned; the method comprises the following steps: dividing cold data into permanent, periodic and sudden basic access modes through first-layer classification; generating a composite classification label through second-layer classification; matching a predefined strategy matrix according to a classification result, dynamically selecting a compression algorithm and a storage medium, and allocating bandwidth; operation auditing, compliance verification and exception handling are realized through the smart contract; and collecting strategy execution indexes in real time, and adjusting classification model parameters and resource scheduling weights. Through an intelligent cold data judgment and classification mechanism, cold data is accurately identified and managed, occupation of storage resources is reduced, and the overall utilization rate of the storage resources is improved.

Various embodiments of the present disclosure provide automated encryption and data de-identification techniques for improving computer security. The techniques apply machine learning and encryption techniques to transform input data objects to tagged data objects that may be locally decrypted using encrypted element representation stored within the tagged data objects. The techniques may include determining a protected data element from an input data object based on privacy criteria and generating the tagged data object from the input data object by replacing the protected data element with an anonymized privacy tag that identifies a privacy type of the protected data element. The techniques may further include generating an encrypted element representation of the protected data element and inserting the encrypted element representation to a portion of the tagged data object to enable decryption of the tagged data object by authorized entities.

A computer implemented system is described in various embodiments herein, the system includes a processor, a memory coupled to the processor and storing processor-executable instructions that, when executed, configure the processor to: receive an initial video data object V comprising a plurality of image frame data objects I; receive a set of time-synchronized viseme parameters corresponding to target audio data object A′; and process the initial video data object V and the set of time-synchronized viseme parameters using a machine learning network to generate an output video data object V′, wherein initial mouth regions in the initial video data object V have been replaced with replacement mouth regions generated based on the set of time-synchronized viseme parameters.

The present invention is related to data processing methods and systems thereof. According to an embodiment, the present invention provides a method of processing claim deduction documents using a machine learning model. The process begins by accessing data files and extracting information from them, which is subsequently stored. This document information, along with the machine learning model trained on various document formats, is used to classify the data files and generate tabular data. From this tabular data, data objects are created and included in an output data file. The information from the output file is then used to update the data of the machine learning model, optimizing it for improved future document processing. There are other embodiments as well.

Disclosed herein are systems and method for detecting malicious activity. A method may receive a plurality of logs indicative of software behavior from an endpoint device and generate, based on the plurality of logs, a provenance graph that represents relationships between different types of data objects on the endpoint device. The method may detect a plurality of trigger actions in the provenance graph and generate, for each respective trigger action of the plurality of trigger actions, a sequence of events that contributed to an occurrence of the respective trigger action. The method may train, using sequences of events generated for the plurality of trigger actions, a foundational language model to predict resultant events for a sequence of lead up events and classify whether the resultant events indicate malicious activity. The method may detect the malicious activity by applying the foundational language model on an input sequence of events.

Embodiments are directed to a file system that include object stores. An object store for write requests may be provided. Write ahead log (WAL) entries that include data blocks may be generated. A WAL object may be generated based on the WAL entries and stored in the object store. An in-memory overlay may be updated to associate the data blocks with the WAL object. A checkpoint operation may be executed to: generate an index object that includes index entries that associate other data blocks with data objects stored in the object store; update the index object to include index entries that associate the data blocks with the WAL object; store the updated index object in the object store; update the in-memory overlay to remove the association of the data blocks and the WAL object and update the in memory WAL to remove records of successfully checkpointed WAL objects.

Storing and retrieving data using vector embeddings, including: receiving a request to store a data object in an object store; generating, based on the data object, a vector embedding for the data object; and storing, in the object store, the data object and the vector embedding for the data object.

Techniques for automatically generating reports based on organizational data. For example, a computer system can include a database of organizational data associated with the organization. The organizational data includes a plurality of employee data objects and a plurality of previously generated report configuration files. The system can include a machine-learned model configured to generate a report configuration file based on a prompt. The machine-learned model can be trained using the plurality of previously generated report configuration files. The system can receive a user query requesting a report associated with the organizational data. Additionally, the system can process, using a natural language processing model, the user query and the employee data objects to generate a prompt. Moreover, the system can process, by the machine-learned model, the prompt and the organizational data to generate a report configuration file. Furthermore, the system can generate the report based on the report configuration file.

A computer-implemented system, computer-implemented method, and computer-program product includes receiving, via a graphical user interface, a request to generate an initial design of experiment plan based on one or more experiment factors; generating a first plan settings container object that represents the initial design of experiment plan in computer memory; and instantiating, within the graphical user interface, a plan explorer section that includes a plan settings graphical entry that displays a graphical representation of a plan factors data object and a plan model data object associated with the first plan settings container object.

A method includes receiving a protection request for an exchange and generating a metadata object including a plurality of outputting attributes for outputting. The method further includes generating a protected NFT including a link with the metadata object. The protected NFT is encapsulated within a control structure that restricts an output of the metadata object. The method further includes signing the protected NFT using at least one of a first private key or a second public key. The method further includes transmitting the signed protected NFT to the remote device and continuously monitoring the exchange. The method further includes detecting, by the control structure, at least one outputting attribute of the plurality of outputting attributes is satisfied and, in response to detecting the at least one outputting attribute is satisfied, outputting at least a portion of the output.

The invention relates to the technical field of data processing, and discloses a data processing method and application based on multi-source data fusion. The method comprises the following steps: acquiring multi-source time sequence data (including sensor, geographic space, user behavior data and the like) and space vector base map data of a target area; de-noising and normalizing the multi-source time series data, extracting an associated feature map through a heterogeneous model, generating a regional data object set based on base map adaptive segmentation, and extracting multi-dimensional features; optimizing a data object set through spatial topology verification, feature matching screening and weighted fusion; and generating statistics, space-time coupling analysis and a dynamic prediction result by utilizing optimized data, or generating a visual map supporting interaction by mapping a base map. The method improves the multi-source data fusion precision and spatial analysis capability, is suitable for multi-field cross-modal decision support, and can be used for scenes of smart cities, environment monitoring and the like.

Various embodiments described herein support or provide operations including receiving input data that comprises a function definition; generating a schema that describes output data in an output format expected in a function output; and executing a function based on the function definition. The execution of the function includes generating a prompt that includes the schema and the input data; using a machine learning model to generate a response based on the prompt, the machine learning model comprising at least one large language model; and generating the function output by converting the response into a programming data object in the output format described by the schema.

The invention relates to a distributed data security storage and encryption system based on an AI technology. The system comprises a data acquisition and preprocessing layer used for acquiring original data and preprocessing the original data to obtain an identifiable data object; the AI identification and strategy decision layer is used for identifying data features of identifiable data objects based on an interpretable AI model to obtain an encrypted instruction packet; the encryption execution and distributed storage layer is used for performing encryption storage processing on the original data content according to an encryption instruction packet to obtain data access interface information; and the dynamic access control layer is used for obtaining user information in response to a data access request instruction of a user, recombining and decrypting the data by using the data distribution mapping index based on the access control strategy table, and returning a plaintext data fragment. By adopting the system, a high-strength, differentiated and multi-level security mechanism can be realized through the AI, intelligent encryption strategy decision is carried out, and the security requirements of multiple scenes and multiple types of data are met.

The invention provides a digital factory flexible production scheduling system based on MES, and relates to the technical field of digital management, and the method comprises the steps: capturing an order change instruction in real time through an integrated interface of the MES and ERP, and converting the order change instruction into a standard chemical order data stream; based on the structured data object, fusing the equipment real-time sensing data with a pre-stored process constraint matrix and dynamic material topological data, and generating a three-layer real-time resource model containing an equipment state vector, the process constraint matrix and the material topological data; layered optimization is executed based on a three-layer real-time resource model, in the first layer of optimization, an equipment state vector and a process constraint matrix are used as hard constraints, and an initial feasible solution set is generated; and the second-layer optimization executes multi-target evaluation on the initial feasible solution set to obtain a non-dominated solution set as a production scheduling plan. According to the invention, order-resource-execution full-process intelligent collaboration is realized.

The invention discloses an intelligent data storage method based on a cloud platform, relates to the technical field of data storage management, and is used for solving the problem that cold and hot data identification is lagged. According to the method, the access behavior graph is constructed, the concurrent access coupling weight is calculated, the data object with high access frequency and deep path hierarchy is identified, the structure adjustment instruction is dynamically generated, the access link complexity and index hierarchy redundancy are reduced, the response delay and the popularity prediction error before and after the structure adjustment are combined, and the access efficiency is improved. According to the method, the scheduling efficiency of the hotspot data is improved, the adaptability to the change of a storage access mode is enhanced, the dynamic adjustment capability and the structure response performance are good, and the method can be used for realizing the closed-loop linkage of the access behavior perception, the structure reconstruction and the model optimization. Therefore, the method is more suitable for a large-scale data intelligent management scene under a cloud platform.

Systems and methods for providing a data lineage are described. According to some examples, a method may include receiving a source dataset corresponding to a use case scenario. The method may further include classifying the source dataset based on the use case scenario to create a classified dataset and then applying a set of data processing rules to the classified dataset to create a curated dataset. The method may further include receiving a reporting data object indicating a relationship between the source dataset and the curated dataset. The relationship can describe a data lineage of the source dataset. The method may further include outputting display data associated with the reporting data object.

Systems and techniques are described for tracking and providing access reports for individual pieces of data managed by a data storage service. A service may generate and store a record of operations performed on a piece of data, such that may be classified as containing sensitive or important data, in a data store. The record may link representations of users and the operations performed by those users to instances of the piece of data, as it is found in one or more data objects within the data store. The data store may link other instances of the piece of data and other operations performed on the piece of data to the first instance of the piece of data. The service may access the data store to produce a history record of the various instances of the piece of data and operations performed on those instances of the piece of data.