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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.
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).
Such data disappear from the database (upon the abort) and are parts of an incorrect database state. Reading such data violates the consistency rule of ACID. Unlike Serializability, Recoverability cannot be compromised, relaxed at any case, since any relaxation results in quick database integrity violation upon aborts. The major methods listed ...
Synchronization is designed to be cooperative, demanding that every thread follow the synchronization mechanism before accessing protected resources for consistent results. Locking, signaling, lightweight synchronization types, spinwait and interlocked operations are mechanisms related to synchronization in .NET." [11]
In the case that shared data is cached, there are two approaches in order to enforce the cache coherence. In the first approach, when a shared data is updated, the server forwards invalidation to all caches. In the second approach, an update is propagated. Most caching systems apply these two approaches or dynamically choose between them.
Microsoft Sync Framework is a data synchronization platform from Microsoft that can be used to synchronize data across multiple data stores. Sync Framework includes a transport-agnostic architecture, into which data store-specific synchronization providers, modelled on the ADO.NET data provider API, can be plugged in. Sync Framework can be used for offline access to data, by working against a ...
Additionally, two view-equivalent schedules must involve the same set of transactions such that each transaction has the same actions in the same order. In the example below, the schedules S1 and S2 are view-equivalent, but neither S1 nor S2 are view-equivalent to the schedule S3.
Optimistic concurrency control (OCC), also known as optimistic locking, is a non-locking concurrency control method applied to transactional systems such as relational database management systems and software transactional memory. OCC assumes that multiple transactions can frequently complete without interfering with each other.