18 results about "Central repository" patented technology

Systems and methods for intelligent data sanitization employing PCM and AI / ML are provided. The idea uses AI / ML to detect specific facts that needs sanitization rather than full properties in incoming records. Data sanitization is optimized using this focused method, saving computational resources. To properly manage changing data volumes, PCM shifts between Logical 0 and Logical 1 states. Logical 0 processes smaller volumes with high resistance and low conductivity, while Logical 1 processes large volumes with low resistance and high conductivity. The AI / ML module organizes and directs data to maximize resource and processing efficiency. The PCM processes data in-memory and directly overwrites, eliminating erasure. AI / ML and PCM integrate to sanitize data quickly, efficiently, and securely, improving system performance and data integrity without a central repository. The system dynamically adjusts to changing data patterns, protecting and optimizing data.

Method for uploading data destined for a central repository, wherein the data is uploaded to an intermediary upload device before being uploaded to the central repository, the method comprising the following step: - Determining mobile / intermediary uploading devices on site; the procedure being characterized by the following steps: - Determining the probability that the mobile / intermediary upload devices will return to a connected upload point; - Identifying those mobile / intermediary upload devices with a higher probability of returning to the connected upload point; and - Uploading the data to those mobile / intermediary upload devices with the higher probability of returning to the connected upload point.

Systems and methods for intelligent data sanitization employing PCM and AI / ML are provided. The idea uses AI / ML to detect specific facts that needs sanitization rather than full properties in incoming records. Data sanitization is optimized using this focused method, saving computational resources. To properly manage changing data volumes, PCM shifts between Logical 0 and Logical 1 states. Logical 0 processes smaller volumes with high resistance and low conductivity, while Logical 1 processes large volumes with low resistance and high conductivity. The AI / ML module organizes and directs data to maximize resource and processing efficiency. The PCM processes data in-memory and directly overwrites, eliminating erasure. AI / ML and PCM integrate to sanitize data quickly, efficiently, and securely, improving system performance and data integrity without a central repository. The system dynamically adjusts to changing data patterns, protecting and optimizing data.

Methods and systems disclosed herein describe generating products using data objects and / or entities that comply with a canonical / governed model(s). The data objects and / or entities may be obtained from an enterprise model or a combination of an enterprise model and one or more local models within a central repository to generate the new product data structures. Once all the data objects and / or entities have been added to the new product, one or more simplification rules may be applied to the new product to flatten (optimize for consumption) the data structure of the product such that superfluous or extraneous code snippets may be removed, or reduced, in such a way that the product complies with the canonical model. The new product may then be exported to an executable data format, which can either be incorporated in another application or used as a standalone product.

A bond-log interpretation system, apparatus, and computer-implemented method are disclosed. The system ingests a cement-bond log (CBL) containing variable-density log (VDL) data and at least one secondary data track (SDT). A processing core, coupled to toggleable lithology, casing-tally, cementing-data, and prior-CBL libraries, first executes a VDL-only assessment at each depth to flag possible discontinuities. For every flagged depth, the core correlates the concurrent SDT data with selected library information to confirm or clear the discontinuity, delineate imperfect intervals, and identify probable causes. Results are synthesized into an annotated integrity report that includes audit-trail metadata listing which libraries were enabled. In a multi-well deployment, multiple field units synchronize their libraries with a central repository over a secure network, allowing real-time, parallel analysis and fleet-wide visualization. The modular architecture permits on-the-fly enabling, disabling, and versioning of libraries while maintaining a single interpretation engine adaptable to diverse tools and formations.

An example methodology includes, by a computing device, analyzing an application to identify one or more elements in the application and, for each element of the one or more elements, an attribution of the element. The method also includes, for each element of the one or more elements, by the computing device, determining an attribute of the element which is a non-duplicate attribute, creating a first accessibility to the element by the non-duplicate attribute, creating a second accessibility to the element by XPath, and storing the first accessibility and the second accessibility created for the element within a central repository. The method may also include, by the computing device, generating a handler class for an element of the one or more elements in the application, wherein the handler class contains code for accessing the element, and providing the generated handler class to another computing device.

This invention provides a code AI review method, system, device, and storage medium with a central repository architecture. The method includes: constructing a centralized code review service repository to provide unified services for multiple business projects; receiving code change trigger signals and loading the review configuration for the corresponding business project; splitting multi-dimensional review tasks into multiple independent execution units and executing them in parallel, with each unit running in an independent context; creating a multi-level versioned storage directory based on code version identifiers and storing review results by dimension; submitting the review results to the central repository to generate an audit trail and pushing the review report to the code hosting platform. This invention, through unified management of the central repository and a multi-agent parallel architecture, achieves efficient access for multiple projects, significantly improved review efficiency, versioned traceability of review results, and a significant reduction in review costs.

The invention discloses a planned transport capacity interruption freight flight disturbance cost optimization method, and relates to the technical field of air transportation operation management and intelligent scheduling, and the method comprises the steps: obtaining flight adjustment records, cargo transportation state updating and ground equipment availability data from a cross-department system; the information is synchronized to a central storage library through a real-time convergence channel, and a preliminarily integrated multi-dimensional dynamic data set is generated according to cross-department data timestamp comparison and data field integrity checking; according to the planned transport capacity interruption freight flight disturbance cost optimization method, the overall disturbance cost is gradually reduced in a business scene in which planned transport capacity interruption repeatedly occurs, and the stability, controllability and cross-department cooperation efficiency of air freight operation are improved.

The application discloses a method and system for executing SQL query in NoSQL database, the method extracts the unique semantics of NoSQL into standardized rule slot plug-ins and registers the rule slot plug-ins into a central repository, and binds all the rule slot plug-ins with a SQL parser, the SQL parser can automatically query the corresponding rule slot plug-in for semantic compensation in the central repository according to the current SQL query and the binding result, and the rule slot plug-in can be automatically instantiated during parsing, so that the standard SQL and NoSQL extension can be seamlessly written in the same statement, the seamless completion from the normal SQL to the complete SQL with the unique semantics of NoSQL is realized, any unique semantics of NoSQL can be expressed with zero loss, the migration cost and maintenance cost from NoSQL to SQL are significantly reduced, and analysts can directly use the standard SQL to query NoSQL data in the NoSQL database.

A computer-implemented method for managing code analysis tools and their configurations across multiple software projects is disclosed. The method comprises: providing a centralized configuration package that includes references to a plurality of code analysis tools and pre-defined configuration settings for the code analysis tools, wherein the configuration package is versioned and stored in a central repository; associating one or more software projects with the centralized configuration package by including a linking reference to the configuration package within a project file of each software project; automatically applying the referenced configuration settings and dependencies defined within the centralized configuration package to each associated software project upon build or analysis by the steps of: adding code analysis tool references and configuration settings to each associated software project; and resolving and managing dependency versions for the code analysis tools according to the version of the centralized configuration package referenced. Unlocking insights from Geo-Data, the present invention further relates to improvements in sustainability and environmental developments: together we create a safe and liveable world.

Disclosed is a flexible and scalable artificial intelligence and analytics platform with advanced content analytics and content ingestion. Disparate contents can be ingested into a content analytics system of the platform through a content ingestion pipeline operated by a sophisticated text mining engine. Prior to persistence, editorial metadata can be extracted and semantic metadata inferred to gain insights across the disparate contents. The editorial metadata and the semantic metadata can be dynamically mapped, as the disparate contents are crawled from disparate sources, to an internal ingestion pipeline document conforming to a uniform mapping schema that specifies master metadata of interest. For persistence, the semantic metadata in the internal ingestion pipeline document can be mapped to metadata tables conforming to a single common data model of a central repository. In this way, ingested metadata can be leveraged across the platform, for instance, for trend analysis, mood detection, model building, etc.

Embodiments of the present disclosure include techniques for decentralizing feature toggles. In certain embodiments, feature toggles are generated for features across a plurality of software systems. The feature toggles include annotations specifying dependencies across the plurality of software systems. The annotated feature toggles are stored in a central repository accessible by the plurality of software systems. When a feature toggle is enabled, feature toggles across the systems may be retrieved from the repository and analyzed by an LLM to determine which feature toggles may need to be updated to prevent a software error.

In a computer-implemented method for configuring a distributed computer system comprising a plurality of nodes of a plurality of node classes, configuration files for a plurality of nodes of each of the plurality of node classes are stored in a central repository. The configuration files include information representing a desired system state of the distributed computer system, and the distributed computer system operates to keep an actual system state of the distributed computer system consistent with the desired system state. The plurality of node classes includes forwarder nodes for receiving data from an input source, indexer nodes for indexing the data, and search head nodes for searching the data. Responsive to receiving changes to the configuration files, the changes are propagated to nodes of the plurality of nodes impacted by the changes based on a node class of the nodes impacted by the changes.

A computer-implemented method, according to one embodiment, includes using a first symmetric key to encrypt a first private key of a first asymmetric key pair, and storing the encrypted first private key on a predetermined near-field communication (NFC) tag. The method further includes storing a first public key of the first asymmetric key pair and the first symmetric key on a predetermined central repository, and obtaining an encrypted first message and an encrypted first group key from the central repository. The encrypted first message was encrypted using the first group key, and the encrypted first group key is the first group key encrypted using the first public key. The method further includes using the encrypted first private key stored on the predetermined NFC tag to decrypt the encrypted first message.

Apparatus and associated methods relate to training a neural network on a first host system, sending the neural network to a second host system, training the neural network by the second host system based on data private to the second host system, and employing the neural network to filter events sent to the first host system. In an illustrative example, the first host system may be a server having a central repository including trained neural networks and historical data, and the second host system may be a remote server having a data source private to the remote server. The private remote data source may be a camera. In some examples, events may be filtered as a function of a prediction of error in the neural network. Various examples may advantageously provide remote intelligent filtering. For example, remote data may remain private while adaptively filtering events to the central server.

We provide an adaptive robot evaluation system for multidimensional educational quality monitoring. [Solution] The system (100) comprises a mobile support structure (102), a sensing assembly (104), a data interface unit (106), a memory unit (108), a processing unit (110), and a control unit (112). The processing unit (110) performs techniques to generate multidimensional groupings that correlate physical evidence with institutional information, calculates quality indicators, and evaluates data sufficiency, and instructs the control unit (112) to autonomously reposition the equipment towards undervalued zones. As a result, the system (100) can transmit aggregated performance indicators and metadata to a central repository to support organizational analysis, governance, compliance, and decision-making.

A system (1100) and method (1200) for generating vehicle data and surrounding contextual data into a standardized form by combining disparate, non-standardized onboard vehicle data into a central repository which is updated in real-time, that normalizes that data from non-standard to pre-defined formats, and then makes the pre-defined data available for consumption to authorized applications and users in raw form, or as outputs to a data product comprising data from multiple onboard signal generating sources through APIs. The system (1100) comprises an assigning authority engine (1105), a mobile device (110) for a vehicle (1000), a connected vehicle device (135) comprising on-vehicle data for the vehicle (1000), and an off vehicle source selected from a database (1125), a cloud source (1180), or a physical structure (1140).