The CAP Theorem Versus Databases with Relaxed ACID properties

The CAP theorem combines the three desirable properties C (data consistency), A (data availability), and P (partition-tolerance: tolerance of inconsistencies between data stored in a distributed database where partitions are allowed). The CAP theorem asserts that any distributed system that uses data from different locations can have at most two of the three desirable CAP properties [5]. The NoSQL movement has applied the CAP theorem as an argument against traditional ACID (atomicity, consistency, isolation, and durability) databases, which prioritize consistency and partition-tolerance at the cost of a potentially low availability. Recently, Brewer [4] has modified the CAP theorem, pointing out that all the CAP properties are more or less continuous, and possible to optimize, weighing them against each other., in practice it is possible for an application area to have both relative high availability and sufficient data consistency, despite the presence of network partitions.
The overall objective of this paper is to improve the CAP optimization methods by using optimization techniques outside of those preferred in CAP optimization literature. The main contribution is to use relaxed ACID properties in the CAP optimization process. This may be viewed as a bridge between the CAP theorem and the traditional ACID theory. Traditional ACID properties are weakened, but not completely dropped, in order to optimize CAP properties. From a user point of view, systems should thus function as if both the traditional ACID properties and all the CAP properties were implemented.
This optimizing is especially important in mobile integrated databases, where disconnections are normal and frequent. It is also important in distributed databases like EHR (electronic Health Records) where many different hospital locations are involved, since the risk for disconnections increases with the number of participating locations. We use distributed integrated EHR databases as an example where our optimizing method may contribute.