Abstract: A method or system for monitoring data quality issues in non-native data over disparate computer networks is described. The system may monitor incoming data entries of non-native data over a disparate computer network. The system may generate for display, on a user interface, a recommendation based on a difference between a number of the first plurality of data entries and a dynamic threshold.

Abstract: Systems and methods are provided for managing and accessing data using one or more data cloud servers. An exemplary method includes: receiving from one or more data sources, a first data set; stratifying the first data set into first samples; receiving from second one or more data sources, a second data set; stratifying the second data set into second samples; computing a projection factor for each of the second samples using the first samples; computing projected samples using the projection factor for each of the second samples; receiving from third one or more data sources, a third data set; computing a parameter using the third data set; selecting one or more of the projected samples to form a fourth data set; and performing a computer operation for estimating the data using the fourth data set and the parameter.

Abstract: Data storage system conflict management techniques are described. In one example, data is sent by client devices to both a repository system and a search index system of a data storage system. Each of the client devices maintains, locally, records of changes to data of the repository system, e.g., through use of an invertible Bloom lookup table. The records are used as a basis by the client devices to send reports to an arbiter system and used to detect conflicts When detected, a conflict request is communicated to client devices involved in the conflict as well as to the repository system and responses used to generate a correction that is communicated to update the repository system and search index system.

Abstract: Example embodiments relate to creating, managing, and delivering feed formatted content. One method includes receiving a multimedia playlist request including at least one multimedia location indicator, determining at least one multimedia file location based on the at least one multimedia location indicator, and providing a multimedia playlist including at least one of the determined multimedia file locations. A further method includes receiving a resize input, resizing a feed reader from a first display including a first number of feed items to a second display including a second number of feed items based on the resize input, the first number different from the second number, receiving a scroll request, determining a scroll number based on the second number, and scrolling based on the determination.

Abstract: A method for flight data object realization includes storing a graph database that defines nodes populated with properties, receiving relationships among data items stored in data stores, transforming the relationships into edges in the graph database, receiving a query to create the flight data object, accessing the graph database, extracting particular properties from the graph database in response to the query, fetching from the data stores particular data items in response to the particular properties, generating the flight data object with the particular data items, creating a visual presentation of the flight data object, and presenting the flight data object.

Abstract: Systems and methods for embodiments of artificial intelligence systems for identity management are disclosed. Embodiments of the identity management systems disclosed herein may support the creation, association, searching, or visualization of any relevant context to identity management assets for a variety of purposes, including for informing the identity management systems' manual or automated decisions, processes or workflows.

Abstract: Systems and methods of the present disclosure enable a processor to automatically detect duplicate data entries by receiving data entries associated with a user, where each data entry includes a value, a time, an entity identifier, and a location. Pairs of similar data entries are determined by matching the entity identifier and the location pairs data entries. Candidate duplicate data entries are determined based on a proximity in time between data entries of the similar data entries. For each candidate duplicate data entry, a feature vector is generated including the entity identifier, location, value and time, and each feature vector is submitted to a duplicate classification model to automatically determine duplicate data entries from the candidate duplicate data entries, the duplicate classification model being trained according to a historical dispute entries.

Abstract: One example method includes identifying a cloud object as a potential candidate for defragmentation, evaluating the cloud object to determine what portion of segments of the cloud object are expired, when the portion of expired segments meets or exceeds a threshold, segregating the expired segments and unexpired segments of the cloud object, creating a first new cloud object that includes only unexpired segments, creating a second new cloud object that includes only expired segments, and deleting the cloud object from storage.

Abstract: The present invention discloses a medical clinical data quality analysis system based on big data, which relates to the field of medical big data technologies. The present invention is used to solve the technical problems that multi-link classified acquisition cannot be carried out on medical behaviors, and the quality of clinical data cannot be analyzed and reflected at the front end, middle link, and tail end of management. In the present invention, authenticity data, compliance data, process data and conclusive data are comprehensively acquired in admission, hospitalization, and discharge links of medical clinical data quality management, which is conducive to implementing multi-link classified acquisition of clinical medical behaviors, thereby facilitating subsequent dynamic and orderly quality analysis and control of clinical data.

Abstract: An event processing system may identify duplicate events and prevent one or more of the duplicate events from being processed. The event processing system may determine the duplicate events, which are not permitted to be processed more than once, and allow these duplicate events to be processed exactly once. The event processing system may allow events, which are permitted to be processed more than once, to be processed without determining whether the events are duplicate events. If a main data center of the event processing system fails, a backup data center may take over instantly event processing from the main data center and may prevent events that have been processed by the main data center from being processed. Multiple data centers may work together and process events in an active-active mode by offering the exactly once processing as well as removing any duplicate events from upstream systems.

Abstract: Systems and methods are described for merging customer profiles, such as may be implemented by a computer-implemented contact center service. In some aspects, a subset of profiles may be determined that satisfy merging criteria, where individual profiles include a plurality of data fields. At least one value in a first data field that conflicts between at least two profiles may be identified. Next a merged value may be selected for the first data field based on data deduplication criteria, where the data deduplication criteria includes at least one indicator of accuracy of values of the plurality of data fields. As a result of a determination that at least the subset of profiles of the group of profiles meet the merging criteria, at least the subset of profiles may be combined into a combined profile using the merged value.

Abstract: Systems, methods, and other techniques for genealogical entity resolution. The systems obtain first tree data and second tree data, the first tree data corresponding to a first tree person and the second tree data corresponding to a second tree person. The systems extract a set of features from the first tree data and the second tree data. The systems further generate an individual-level similarity score for each possible pairing of tree persons based on the set of features to identify a set of most-similar tree persons based on the individual-level similarity score for each possible pairing. The systems also provide a plurality of individual-level similarity scores for the set of most-similar tree persons as input to a family-level ML model to determine that the first tree person and the second tree person correspond to a same individual.

Abstract: A distributed, transactional database uses timestamps, such as logical clock values, for entry versioning and transaction management in the database. To write to the database, a service requests a timestamp to be inserted into the database with a new version of data. During a backup procedure, a cleanup process is paused, issuing new timestamps is paused, and a backup timestamp is generated, which results in an effective backup copy. During a restore of a backup, a snapshot of the database is loaded and any entries older than the backup timestamp are deleted, which ensures that a consistent restore has occurred.

Abstract: Methods and systems for managing, storing, and serving data within a virtualized environment are described. In some embodiments, a data management system may manage the extraction and storage of virtual machine snapshots, provide near instantaneous restoration of a virtual machine or one or more files located on the virtual machine, and enable secondary workloads to directly use the data management system as a primary storage target to read or modify past versions of data. The data management system may allow a virtual machine snapshot of a virtual machine stored within the system to be directly mounted to enable substantially instantaneous virtual machine recovery of the virtual machine.

Abstract: A system and method of autonomous data hub processing that uses semantic metadata, machine learning models, and a permissioned blockchain to autonomously standardize, identify and correct errors in supply chain data is disclosed. Embodiments input supply chain data stored in a supply chain database, train with the machine learning model trainer, one or more machine learning models to identify one or more data errors in the supply chain data, clean the one or more identified data errors from the supply chain data, and store cleaned supply chain data. Embodiments also update one or more machine learning models to identify one or more data errors in cleaned supply chain data, and join and aggregate one or more sets of cleaned supply chain data.

Abstract: One example method includes, in a data buffer that includes one or more words and whitespaces, calculating a hash value of data in a window that is movable within the data buffer, comparing the hash value to a mask, and when the hash value matches the mask, identifying a position of the window in the data buffer as a chunk anchor position, searching for a whitespace nearest the chunk anchor position, and designating an offset of the whitespace as a segment boundary.

Abstract: Various examples of the disclosure generally pertain to storing, in a distributed ledger and in association with one or more transactions, one or more identifiers of owners of respective smart contracts. Various examples of the disclosure relate to deleting transactions associated with a given party as owner upon receiving a withdrawal request of the given party.

Abstract: Embodiments include a method of storing digital data in a blockchain by receiving the digital data from a user, deduplicating the data in a deduplication system of the user to form deduplicated data, and encoding the deduplicated data into an array format for storage on a blockchain. A smart contract is deployed for deduplication on the destination side of the data, and the deduplicated data is encoded into a Binary Aligned Map (BAM) format for storage on the blockchain. A process on the destination side stores the deduplicated array data as a next block in the blockchain only if the next block agrees with the smart contract, and the blockchain is stored in system storage comprising hard disk, solid state, tape, or optical devices.

Abstract: Techniques for managing data patterns involve: acquiring multiple sets of data patterns respectively associated with multiple collection devices, wherein a set of data patterns in the multiple sets of data patterns represent patterns of duplicate data in data from one of the multiple collection devices; dividing the multiple collection devices into multiple groups based on clusters of the multiple sets of data patterns; and determining, based on sets of data patterns associated with collection devices in a group in the multiple groups, a set of shared data patterns for sharing among the collection devices in the group. Accordingly, data patterns that can be shared among multiple collection devices can be determined in a more accurate and effective manner, thereby facilitating the removal of duplicate data from the multiple collection devices.

Abstract: Systems and methods are provided to aggregate and analyze data from a plurality of data sources. The system may obtain data from a plurality of data sources. The system may also transform data from each of the plurality of data sources into a format that is compatible for combining the data from the plurality of data sources. The system uses a data harmonization module to organize, classify, analyze and thus relate previously unrelated data stored in multiple databases and/or associated with different organizations. The system can generate and publish master data of a plurality of business entities.