A shared-nothing database system is provided in which parallelism and workload balancing are increased by assigning the rows of each table to “slices”, and storing multiple copies (“duplicas”) of each slice across the persistent storage of multiple nodes of the shared-nothing database system. When the data for a table is distributed among the nodes of a shared-nothing system in this manner, requests to read data from a particular row of the table may be handled by any node that stores a duplica of the slice to which the row is assigned. For each slice, a single duplica of the slice is designated as the “primary duplica”. All DML operations (e.g. inserts, deletes, updates, etc.) that target a particular row of the table are performed by the node that has the primary duplica of the slice to which the particular row is assigned. The changes made by the DML operations are then propagated from the primary duplica to the other duplicas (“secondary duplicas”) of the same slice.

The instant disclosure illustrates how the privacy and security of activities occurring on distributed ledger-based networks (DLNs) can be enhanced with the use of zero-knowledge proofs (ZKPs) that can be used to verify the validity of at least some aspects of the activities without private information related to the activities necessarily being revealed publicly. Methods and systems that are directed at facilitating the tracking and recovery of assets stolen on ZKP-enabled DLNs while preserving the confidentiality of the tokens are presented herein.

The instant disclosure illustrates how the privacy and security of activities occurring on distributed ledger-based networks (DLNs) can be enhanced with the use of zero-knowledge proofs (ZKPs) that can be used to verify the validity of at least some aspects of the activities without private information related to the activities necessarily being revealed publicly. Methods and systems that are directed at facilitating the tracking and recovery of assets stolen on ZKP-enabled DLNs while preserving the confidentiality of the tokens are presented herein.

A method for a first set of processors and a second set of processors comprises, the first set of processors processing a set of queries, as a result of a change in utilization of the first set of processors, processing the set of queries using the second set of processors. The change in processors is independent of a change in storage resources, the storage resources shared by the first set of processors and the second set of processors.

A method for a first set of processors and a second set of processors comprises, the first set of processors processing a set of queries, as a result of a change in utilization of the first set of processors, processing the set of queries using the second set of processors. The change in processors is independent of a change in storage resources, the storage resources shared by the first set of processors and the second set of processors.

Provided herein are system, apparatus, device, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for generating an output indicating differences in the data stored in disparate data storage devices and/or for reconciling data stored in disparate data storage devices. In an embodiment, a server loads a first subset of a first set of data corresponding to one or more first columns and a second subset of a second set of data corresponding to one or more second columns into a data repository. The server identifies one or more differences between the first subset of data and the second subset of data in the data repository, and causes display of the one or more differences. The server may generate an output including the first and second sets of data, and a visual indicator indicating each of the one or more differences and causes display of the output.

Provided herein are system, apparatus, device, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for generating an output indicating differences in the data stored in disparate data storage devices and/or for reconciling data stored in disparate data storage devices. In an embodiment, a server loads a first subset of a first set of data corresponding to one or more first columns and a second subset of a second set of data corresponding to one or more second columns into a data repository. The server identifies one or more differences between the first subset of data and the second subset of data in the data repository, and causes display of the one or more differences. The server may generate an output including the first and second sets of data, and a visual indicator indicating each of the one or more differences and causes display of the output.

An asynchronous accounting method for a blockchain is provided. After a block reaches a consensus, different types of data contained in block information of the block are written into message queues corresponding to the respective types of data. Each of the message queues correspond to a different one of the types of data contained in the block information. A next round of block consensus processing is started after the different types of data are written into the message queues corresponding to the respective types of data. The different types of data are read from the message queues corresponding to the respective types of data to perform persistence processing of a blockchain ledger.

A network of networks enables seamless and effective aggregation of a complete electronic health record on a user's personal device and sharing of the complete medical history among health organizations as authorized by the user. A complete health record includes all diagnoses, testing, and treatment related to routine medical services, ambulatory medical services, emergency medical services, telemedicine, data collected from personal monitors, and/or data entered by user. Also provided is automatic updating of the electronic health record to include information from new medical services.

A network of networks enables seamless and effective aggregation of a complete electronic health record on a user's personal device and sharing of the complete medical history among health organizations as authorized by the user. A complete health record includes all diagnoses, testing, and treatment related to routine medical services, ambulatory medical services, emergency medical services, telemedicine, data collected from personal monitors, and/or data entered by user. Also provided is automatic updating of the electronic health record to include information from new medical services.