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Data synchronization is the process of establishing consistency between source and target data stores, and the continuous harmonization of the data over time. It is fundamental to a wide variety of applications, including file synchronization and mobile device synchronization.
A special case of replication is synchronization, where there are only two replicas. For example, personal digital assistants (PDAs) allow users to edit data either on the PDA or a computer, and then to merge these two datasets together. Note, however, that replication is a broader problem than synchronization, since there may be more than two ...
Rendezvous is a data synchronization mechanism in Plan 9 from Bell Labs. It is a system call that allows two processes to exchange a single datum while synchronizing. [1] The rendezvous call takes a tag and a value as its arguments. The tag is typically an address in memory shared by both processes.
Weak ordering classifies memory operations into two categories: data operations and synchronization operations. To enforce program order, a programmer needs to find at least one synchronisation operation in a program. Synchronization operations signal the processor to make sure it has completed and seen all previous operations done by all ...
Synchronization overheads can significantly impact performance in parallel computing environments, where merging data from multiple processes can incur costs substantially higher—often by two or more orders of magnitude—than processing the same data on a single thread, primarily due to the additional overhead of inter-process communication ...
Data integration refers to the process of combining, sharing, or synchronizing data from multiple sources to provide users with a unified view. [1] There are a wide range of possible applications for data integration, from commercial (such as when a business merges multiple databases) to scientific (combining research data from different bioinformatics repositories).
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The PACELC theorem, introduced in 2010, [8] builds on CAP by stating that even in the absence of partitioning, there is another trade-off between latency and consistency. PACELC means, if partition (P) happens, the trade-off is between availability (A) and consistency (C); Else (E), the trade-off is between latency (L) and consistency (C).