18 results about "Metadata repository" patented technology

A data catalog system is described that includes capabilities for automatically identifying and classifying sensitive information stored in data objects associated with various data sources. The data catalog system identifies a data object associated with a data asset stored in a data catalog metadata repository and computes a sensitivity score for the data object based on a set of one or more sensitive data identification techniques. The system determines a set of enrichment labels for the data object based on the sensitivity score computed for the data object. The enrichment labels are used to further qualify, enrich, or classify the data objects identified as containing sensitive information. For instance, the enrichment labels may identify a set of custom properties to be assigned to a data object, identify glossary terms to be applied to the data object or the enrichment labels may identify tags to be assigned to the data object.

Aspects map values of skills data for candidates to skills metadata representations stored within a metadata repository that includes skills metadata representation data dimensions for other candidates; filter via machine learning a top-trending subset of job classifications that have better career opportunity values from a universe of job classifications defined within the repository dimensional data values; determine via machine learning career path viability values for the top-trending subset job classifications as a function of strength of match to candidate dimensional values; project likely future values of mapped candidate values at the end of a future time period within a simulated work market scenario; and prioritize the top-trending subset job classifications as potential career paths for candidates as a function of the career path viability values and the projected future values of the dimensional data mapped for the candidates within the repository.

Disclosed herein is an agent-first application architecture that facilitates intelligent, metadata-driven interactions across distributed computing environments. The system comprises a Metadata Repository, which defines communication policies, interaction rules, and contextual parameters. An Agent Orchestration Layer governs autonomous software agents, dynamically managing their lifecycle, task delegation, and execution logic. A Self-Learning Engine continuously refines agent decision-making models through real-time metadata updates and adaptive learning mechanisms. The system further incorporates an Event-Driven Middleware, which enables real-time, event-triggered execution of agent actions, ensuring adaptive, scalable, and autonomous decision-making across complex multi-system environments. By eliminating rigid API dependencies and leveraging metadata-centric intelligence, the architecture enhances cross-system interoperability, resilience, and adaptability. The invention is particularly suited for dynamic, multi-agent ecosystems requiring real-time adaptability, self-optimization, and intelligent orchestration of system interactions.

A known-deployed file metadata repository (KDFMR) and analysis engine enumerates reference lists of files stored on a software delivery point (SDP) and compares the enumerated list of files and associated metadata to previously stored values in the KDFMR. If newly stored or modified files are identified, the analysis engine acquires the files from the SDP. Each file is analyzed to determine whether the file is an atomic file or a container file and metadata is generated or extracted. Each file stored in a container file is recursively extracted and analyzed, where metadata is generated for each extracted file and each container file. The KDFMR periodically analyzes the files stored on the SDP for differences to maintain the currency of the KDFMR data with respect to files stored on the SDP. Storage or modification of files on the SDP triggers analysis of the associated file. KDFMR data is updated with metadata determined based on sandbox detonation of files and / or identified artifacts of known-deployed files.

Techniques described herein relate to a method for performing generating backups of host data. The method may include obtaining, by a data protection agent of a host, a backup request associated with an asset of the host; generating a backup image of the asset; obtaining file system metadata associated with the asset from a file system metadata repository on a storage of the host; identifying snapshot metadata included in the file system metadata; identifying snapshots included in the backup image using the snapshot metadata; for each snapshot of the snapshots: identifying backup image sub-assets associated with the snapshot using the snapshot metadata; generating a snapshot backup by storing the backup image sub-assets in a snapshot backup container on a backup storage; and generating snapshot backup metadata associated with each snapshot backup; and performing snapshot level restoration operations using at least the snapshot backup and the snapshot backup metadata.

A database system stores a table as a set of column files in a columnar format in a manner that improves the write performance of the table and avoids use of separate metadata repository. In embodiments, each column file groups values into entity chunks indexed by an entity index. Each chunk includes a live value index that determines which rows in chunk has live values. New values are written to the column file by appending an updated copy of the entity chunk. The entity index to refer to the newly written chunk as the latest version. This approach avoids expensive in-place updating of individual column values and allows the update to be performed much more quickly. In embodiments, the database system encodes metadata such as table schema information using file naming and placement conventions in the file store, so that a centralized metadata repository is not required.

Systems and methods for providing automated data governance are disclosed. The system may include a plurality of data environments, a metadata repository storing data attributes and classification requirements, a policy repository, one or more processors, and a memory in communication with the one or more processors storing instructions to execute steps of a method. The system may receive a first dataset from a first data environment having a first dataset ID. The system may transmit the dataset ID to the metadata repository and the metadata repository may return an indication that the first dataset includes at least one data attribute and at least one associated classification requirement. The system may transmit the classification requirement to the policy repository and receive classification code associated with the classification requirement. The system may modify the first dataset by transmitting instructions to the first data environment to execute the classification code.

A known-deployed file metadata repository (KDFMR) and analysis engine enumerates reference lists of files stored on a software delivery point (SDP) and compares the enumerated list of files and associated metadata to previously stored values in the KDFMR. If newly stored or modified files are identified, the analysis engine acquires the files from the SDP. Each file is analyzed to determine whether the file is an atomic file or a container file and metadata is generated or extracted. Each file stored in a container file is recursively extracted and analyzed, where metadata is generated for each extracted file and each container file. The KDFMR periodically analyzes the files stored on the SDP for differences to maintain the currency of the KDFMR data with respect to files stored on the SDP. Storage or modification of files on the SDP triggers analysis of the associated file. KDFMR data is updated with metadata determined based on sandbox detonation of files and / or identified artifacts of known-deployed files.

The invention discloses a Kubernetes container log data processing method and device. The Kubernetes container log data processing method comprises the steps of obtaining a log file generated when a container in a Kubernetes cluster runs; extracting an identifier of the log file according to a fixed naming rule of the Kubernetes log file; attaching the identifier to the original log file, and sending the original log file to a log content storage library for storage through a log content transmission channel; the method comprises the following steps of: monitoring resource change in a Kubernetes API (Application Program Interface) Server, and collecting metadata; and sending the metadata to a log metadata storage library for storage through a metadata transmission channel by taking the tetrad as a main key. According to the method, the lightweight mark is generated through analysis of the log file name, so that separated transmission of the log and the metadata is realized, log data redundancy is reduced, computing resource overhead and transmission and storage cost are remarkably reduced, and the problem that a processing flow depends on cluster availability is avoided.

A data analytics system includes a data repository configured to store a plurality of pieces of data for multiple clients; a metadata repository, separate from the data repository, configured to store metadata corresponding to the data, each including technical metadata for creating a pipeline and usage metadata indicating sensitivity; a policy store configured to store access control policies for the clients; and at least one processor configured to: receive a request for a piece of data from a user associated with a client; verify access based on the client's policy, the user's role, and the usage metadata; create a pipeline using the technical metadata; and provide the data using the pipeline.

Systems and methods for providing automated data governance are disclosed. The system may include a plurality of data environments, a metadata repository storing data attributes and classification requirements, a policy repository, one or more processors, and a memory in communication with the one or more processors storing instructions to execute steps of a method. The system may receive a first dataset from a first data environment having a first dataset ID. The system may transmit the dataset ID to the metadata repository and the metadata repository may return an indication that the first dataset includes at least one data attribute and at least one associated classification requirement. The system may transmit the classification requirement to the policy repository and receive classification code associated with the classification requirement. The system may modify the first dataset by transmitting instructions to the first data environment to execute the classification code.

A natural language query is received from an operator of a telecommunications network. A metadata request is computed from the natural language query. The metadata request is sent to a repository of metadata, the metadata describing telemetry data of the telecommunications network, the telemetry data stored in a relational database. Metadata is received from the metadata repository in response to the metadata request. Using the received metadata and a language model a relational database query is computed and the relational database is queried. A response is received from the relational database, triggering an action.

The present invention provides a wearable signature jewellery bead, (Beads-C TM), with at least two physical markings denoting authenticity and connection to a unique metadata repository on Distributed Ledger Technology (DLT) which can contain information, which might be used to give genesis to a dedicated digital asset. Such asset can be constituted of any kind of blockchain token, including a non-fungible token (NFT), a soul bounded token (SBT) or any digital representations of tangible or intangible value. The token derived of the metadata information is freely exchangeable but the underlying information is immutable, only updatable upon the interaction with decentralised oracles.

The invention relates to a gateway plug-in deployment method and device and a medium, and belongs to the technical field of gateway plug-in management. The method comprises the following steps: detecting available plug-in resources of gateway equipment; plug-in attributes are obtained from a distributed metadata warehouse, and the plug-in attributes comprise plug-in demand resources; determining a matching degree score of available plug-in resources of the gateway equipment and the plug-in demand resources; adjusting the matching degree score by using a priority correction factor; selecting the gateway equipment with the highest matching degree score as a target deployment node; deploying a plug-in at the target deployment node; and acquiring a deployment state of the plug-in. According to the scheme, core elements such as resource requirements and dependency relationships of different language plug-ins are abstracted into standardized description, so that the machine readability of the heterogeneous plug-ins is enhanced; and during deployment, the resource utilization rate is high, and a reliable transactional guarantee mechanism is provided for clamping.

A known-deployed file metadata repository (KDFMR) and analysis engine enumerates reference lists of files stored on a software delivery point (SDP) and compares the enumerated list of files and associated metadata to previously stored values in the KDFMR. If newly stored or modified files are identified, the analysis engine acquires the files from the SDP. Each file is analyzed to determine whether the file is an atomic file or a container file and metadata is generated or extracted. Each file stored in a container file is recursively extracted and analyzed, where metadata is generated for each extracted file and each container file. The KDFMR periodically analyzes the files stored on the SDP for differences to maintain the currency of the KDFMR data with respect to files stored on the SDP. Storage or modification of files on the SDP triggers analysis of the associated file. KDFMR data is updated with metadata determined based on sandbox detonation of files and / or identified artifacts of known-deployed files.

This invention discloses a streaming tag computation method based on event-driven and metadata-driven approaches, addressing issues such as poor real-time performance in IPTV tag management, high rule modification costs, untraceable status, and difficulties in tag system expansion. This invention constructs a real-time data stream access platform and a tag rule metadata repository, decoupling rules from computation tasks; it generates real-time computation tasks based on metadata-driven approaches and completes incremental tag computation through event-driven mechanisms; it supports dynamic hot updates of rules, establishes a closed-loop management system for the entire tag lifecycle, and designs a unified data model and hybrid storage architecture for multiple tag types. This invention pioneers a collaborative mechanism between event-driven streaming incremental computation and metadata-driven hot updates, achieving tag synchronization latency ≤1 second, a 90% improvement in rule modification efficiency, and 100% traceability of status transitions, providing IPTV operations with real-time, flexible, and controllable tag capabilities.

A scalable integrated information system in a network environment, the system comprising: an agent instantiated as a virtual machine or virtual network function, the agent configured to communicate with the network environment, the network environment comprising a meta data inventory; a data store comprising a central metadata repository, the central metadata repository configured to communicate with the network environment and selectively retrieve the meta data inventory, wherein the central metadata repository stores an integrated context representation comprising at least one of a real-time temporal context, a historical context, and a meta context associated with the meta data inventory; a reasoning module instantiated as a virtual machine or virtual network function and including an input configured to receive a reasoning concept; a machine learning module, instantiated as a virtual machine or virtual network function and configured to communicate with the central metadata repository to selectively retrieve the integrated context representation, wherein the machine learning module communicates with the reasoning module to develop a reasoning model configured to associate the reasoning concept with the integrated context representation; and wherein the agent communicates with the data store to retrieve the integrated context representation and communicates with the reasoning module to retrieve the reasoning model to develop an action and wherein the agent implements the action within the environment.

Systems, methods, and other embodiments associated with a framework to configure and generate operational data signals for controls are described. In one embodiment, a method includes accepting input that defines a configuration for a functional sensor in a metadata repository of an enterprise data system. The configuration specifies condition(s) on data source(s) for triggering a signal associated with initiation of a task. The method monitors the data source(s) of the enterprise data system with the functional sensor for transaction changes that satisfy the condition(s) for triggering the signal. The method detects a transaction change that satisfies the condition(s) using the functional sensor. The method emits the signal in response to detection that the condition(s) for triggering the signal are satisfied. And, in response to receiving the signal, the method automatically executes the task in the enterprise data system.