13 results about "Legacy data" patented technology

Embodiments described herein comprise an advanced Software as a Service (SaaS) platform addressing challenges including trust, governance, quality, data accuracy, and transparency, ensuring full visibility of enterprise data warehouse data pipelines. These embodiments seamlessly integrate with cloud-based and legacy data platforms, enhancing visibility through monitoring data quality, tracking metadata changes, and generating exceptions for review. Data quality management allows for custom script creation to validate pipelines and business rules. Cloud-native connectors facilitate integration with one or more cloud-based services streamlining data delivery processes. Personalized notifications aid administrators in responsive action, while data profiling capabilities identify quality issues and provide distribution insights. Actionable data intelligence for privacy, security, and governance is enabled through data classification. Automation features encompass data quality checks, metadata monitoring, profiling, and classification, orchestrated by a flexible scheduler. The architecture leverages a scalable serverless design with independent microservices, dynamically scaling based on demand.

Device for the intelligent encapsulation of legacy data information systems with the integration of artificial intelligence, comprising: a collection unit for legacy interfaces, configured to intercept communication data from existing platforms; a semantic interpretation unit designed to analyze and map data structures and process semantics; a contextual abstraction layer designed to convert interpreted information into standardized, interoperable formats; a policy-driven management module that ensures secure access, Data transformation and regulatory compliance are ensured; and a learning and inference unit for adaptive optimization of data mappings and operational logic.

A data organizer, optimizing, reconciliation system (DOORS) / method / program, provides capability to merge intuitively datasets created over users' digital lifetimes from multiple devices. Finds and gathers files from devices and may create master copies in Cloud or local storage. Enhanced functionality via artificial intelligence or machine learning provides more intuitive merging of datasets including file and folder name selection and may operate autonomously at times of low usage. Optimized by learning from training data, or legacy data, or learning in real time from user preferences, practices, and habits. Reducing loses of treasured photographs and important documents via preventing files getting marooned on inaccessible legacy devices. Because devices get lost, replaced, or may suffer ransomware attacks or may need to be re-set and suffer data loss for a variety of reasons. Compatible and useable across the proliferating number of data producing devices, including smart phones, tablets, cameras, and computers.

A process includes identifying a plurality of legacy data sources, wherein each legacy data source has a unique legacy data format, and identifying, for each of the legacy data sources, a data loader that is adapted to process data from the legacy data source to form training data having a training data format that is different than the legacy data format. The process further includes causing, for each of the legacy data sources, the identified data loader to process at least a portion of the data from the legacy data source to form a batch of training data having the training data format. Still further, the operations comprise training an artificial intelligence model using the batches of training data formed from each of the legacy data sources.

One example method includes [searching a legacy data backup system and identifying, in the legacy data backup system, a legacy backup asset, identifying a type of the legacy backup asset, creating a wrapper service instance that corresponds to the type of the legacy backup asset, detaching the legacy backup asset from the legacy data backup system, receiving, by the legacy backup asset, backup calls and restore calls from a new data backup system that is different from the legacy data backup system, and servicing, by the legacy backup asset the backup calls and restore calls based on a state of the legacy backup asset.

The present invention discloses a computer-implemented method for remote management of on-site equipment (10), comprising the steps of identifying, by an identification module (30), one or more on-site equipment (10) equipped with a protocol corresponding to said equipment (10), in which the identification is automatically or manually performed by said identification module (30); converting, by a conversion module (40), the protocol corresponding to the equipment (10) into a digital-ready protocol; and transmitting, by a communication module (50), the digital ready protocol over a wireless network to a storage module (70) for remote access and monitoring by a control center (90) thereafter; wherein the digital-ready protocol contains legacy data that are configurable in a manner that upon modification, said digital-ready protocol is incorporated with executable instructions and subsequently converted back to the protocol corresponding to the equipment (10), such that said equipment (10) may execute the instructions thereafter

A system includes data compression logic for compression of legacy data objects defining transactions that reference inactive rate indices. The data compression logic identifies a non-legacy participant data object that defines a participant flow that is matched in magnitude and opposite in polarity to a net legacy flow associated with one or more legacy data objects. The data compression logic applies data compression by cross-linking the participant data object to the one or more legacy data objects and removing the participant data object to the one or more legacy data objects responsive to the cross-linking.

A data search device according to the present invention comprises: a legacy data management unit configured to collect legacy data together with feature amounts calculated on the basis of feature information contained in the legacy data;and a search execution unit configured to include a receive processing unit for receiving a search query containing user input data and a search condition, a feature amount calculation processing unit for calculating a feature amount based on feature information contained in the user input data, a similarity calculation processing unit for calculating a similarity by comparing a feature amount relating to the user input data with a feature amount relating to the legacy data, and a search condition application processing unit for extracting data satisfying the search condition from the legacy data based on the calculated similarity, wherein the search condition is selectable from a plurality of conditions for which at least three types of relationships with the user input data are specified.

A hybrid cloud bridge system (100) for synchronizing legacy data between on-premises IBM i systems and AWS cloud instances, the system comprising: an on-premises legacy interface module configured to connect to one or more IBM i (AS / 400) systems via native data access protocols, including Distributed Relational Database Architecture and Distributed Data Management, the module further comprising a journal read component for capturing change data in real time from IBM i journal subsystems; a data transformation engine operatively coupled with the on-premises legacy interface module and configured to convert data between the EBCDIC encoding native to IBM i systems and the UTF-8 encoding required by cloud-native applications, the engine comprising an EBCDIC-to-UTF-8 converter, a schema mapping processor, a data validation unit, and a format serializer;a secure communication gateway that establishes encrypted communication channels between the on-premises network and the AWS cloud environment using mutual Transport Layer Security authentication with a pure outbound connection model; a cloud integration module deployed within the AWS cloud environment that includes service adapters for connecting to AWS services; a synchronization controller configured to coordinate bidirectional data synchronization operations via data change detection mechanisms; and a conflict resolution module that implements vector-clock-based versioning to detect and resolve conflicts during concurrent changes;characterized by the fact that the system acts as an intermediary bridge, performing bidirectional real-time synchronization between IBM i legacy systems and AWS cloud instances through integrated data format transformation, secure tunneled communication, and vector clock-based conflict resolution, without requiring any changes to the legacy application code or database schemas.

Various methods and processes, apparatuses or systems, and media for decommissioning of applications, related components, and data are disclosed. The method includes: receiving first information that relates to an application for decommissioning; retrieving second information that relates to components and data sources associated with the application; obtaining third information that relates to data usage associated with the application; identifying an alternate data source associated with a target platform; constructing a physical data model in the alternate data source; migrating a predetermined set of data associated with the application to the alternate data source; obtaining a confirmation for a decommissioning of the application and for an archival of legacy data; performing a lockdown operation and a physical decommissioning of the application, the components, and the data sources; and performing a validation that all traces of the application, the components, and the data sources have been removed as a result thereof.

A method for truth-based, synthetic, partial-entity knowledge sourcing and tradeable asset creation based thereon, is provided. The method may receive a request to transact a long position or a short position with respect to a performance of a selected aspect of a larger entity. The method determines, using artificial intelligence (“AI”) running on a processor, a set of issuance characteristics. The issuance characteristics support the request to transact the long position or short position. The method data-mines historic information to retrieve legacy data representative of the set of issuance characteristics. The data-mining determines whether the set of issuance characteristics supports the request to transact. The method tests the legacy data to determine whether the issuance characteristics support the request to transact. In response to a determination that the issuance characteristics support the request, the method prompts the user to transact the requested long position or the requested short position.

A system includes data compression logic for compression of legacy data objects defining transactions that reference inactive rate indices. The data compression logic identifies a non-legacy participant data object that defines a participant flow that is matched in magnitude and opposite in polarity to a net legacy flow associated with one or more legacy data objects. The data compression logic applies data compression by cross-linking the participant data object to the one or more legacy data objects and removing the participant data object to the one or more legacy data objects responsive to the cross-linking.

This invention relates to the field of information technology. To address the problem of overlooking time-sensitive anomaly signals when processing massive dynamic datasets, this invention provides a digital auditing method and system for the supply chain. The steps are as follows: First, collect real-time transaction and logistics information from each node of the supply chain to obtain a timestamped dynamic dataset and determine the initial distribution of anomaly signals. Second, process the time-series data using a sliding window algorithm to obtain the fusion result of the latest transactions and historical data. Third, if delivery delays exceed a threshold, identify potential risks and obtain quality fluctuation correlation indicators. Fourth, use an anomaly detection algorithm to analyze logistics anomalies and determine the location of evidence chain breaks. Fifth, automatically slide the audit window according to the break location to obtain a timeliness analysis framework. Sixth, if the framework shows risk deviations, use a clustering algorithm to obtain an optimized early warning sequence. Seventh, fuse the latest transactions and logistics anomalies to determine the complete evidence chain path. Finally, process legacy data according to the path to obtain real-time audit results. This method accurately captures the initial distribution of time-sensitive anomaly signals, avoiding obscuring them with historical data.