9 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.

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.

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.

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.

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.