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: 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: 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: 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: 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: 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: 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: 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: 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: 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.

Abstract: Apparatus and method for data discrepancy identification are disclosed. The apparatus includes a memory communicatively connected to at least a processor, wherein the memory contains instructions configuring the at least a processor to authenticate a provider using a provider identifier, receive at least a record from the provider, wherein the at least a record includes a plurality of record elements, obtain at least a proof, wherein the at least a proof includes a plurality of proof elements, analyze the at least a record to find a data discrepancy, wherein analyzing further includes retrieving a discrepancy template and finding the data discrepancy of the at least a record as a function of the at least a proof and the discrepancy template, determine a discrepancy process as a function of the data discrepancy and update the at least a record as a function of the discrepancy process.

Abstract: Systems and methods are disclosed for implementing a data processing workflow user interface for a streaming data processing system. The workflow is visually represented as a series of modules along with interconnections for the modules. Each module represents an operation on a streaming data object, such as a data transformation. The user interface enables selection of a workflow template based on a user-specified data source, and then allows the user to customize the workflow template by specifying additional operations to apply to data objects. The interface may show the user a preview of output data objects processed according to the customized workflow.

Abstract: Systems and methods can include a provider institution accounts database structured to retrievably store data corresponding to a plurality of entities including a primary entity related to a plurality of secondary entities. The data for each of the plurality of entities may include values for a plurality of fields associated with the respective entity. A data management circuit may be configured to detect that one or more fields corresponding to at least one of the secondary entities has a value which is to be updated. The data management circuit may generate a first user interface that causes the user to confirm authorization to provide the value for the one or more fields, and a second user interface including a field for receiving the value. The data management circuit may receive the value, and update the data corresponding to the at least one secondary entity in the provider institution accounts database.

Abstract: Disclosed are techniques for deduplicating files to be ingested by a database. A bloom filter may be built for each of a first set of files to be ingested into a data exchange to generate a set of bloom filters, wherein the data exchange includes a metadata storage where metadata including a list of files ingested is stored. The set of bloom filters may be stored in the metadata storage of the data exchange. In response to receiving a set of candidate files to be ingested into the data exchange, the set of bloom filters may be used to identify from within the set of candidate files, each candidate file that is duplicative of a file in the first set of files.

Abstract: The present disclosure comprises systems and methods to mine association rules from a dataset provided in the input. This comprises methodologies for optimizing the dataset for efficient computation of association rules, other than methodologies for handling partially true data. Moreover, it includes methodologies for evaluating the mining process automatically and removing uninteresting rules. In addition, it includes methodologies for integrating experts in the evaluation of the rules. Finally, methodologies to automatically detect outliers, correct outliers, update truth values and complete missing data in the original dataset. The overall methodology is completely automated and provides numerous tuning parameters to fit most of the use cases, including a default value for each of the tuning parameters to simplify its usage.

Abstract: A data processing system implements techniques for provisioning and operating databases including receiving a request to provision a new database that includes multiple replicas. The data processing system provisions the new database including the plurality of replicas. The plurality of replicas includes a primary replica, one or more secondary replicas, and one or more delta replicas. The primary replica is configured to handle read and write operations. The one or more secondary replicas include a copy of data and transactional log information of the primary replica and are unable to handle write operations. The one or more delta replicas are a copy of the transactional log information of the primary replica and are unable to handle read operations or write operations.

Abstract: Dynamic Random Access Memory (DRAM) and a logic layer having direct access to the DRAM which provides symbolic addressing services. These services are provided by sequencers in the logic layer. The sequencers maintain a DRAM row or row-pair in sorted order, find a location and insert a new data element into the row or row-pair, all while preserving the sorted order. The sequencer is a plurality sequencer groups, each a plurality of sequencer cells. The sequencer cells perform a highly parallel pipeline insertion of a new data element. The logic layer also defines a Self-Addressing Memory Central Processing Unit (SamPU) operatively coupled to, and configured to control, the sequencer. The logic layer provides program memory for SamPU and a memory cache to build an index database. The database is subject to mitosis to accommodate the overflow of any item in the index database.