In this paper, we propose a replication-aware framework named LILAC, sparqL query decomposItion against federations of repLicAted data sourCes, that relies on replicated fragment descriptions to accurately identify sources that provide replicated data.
The TRAPP team is investigating techniques for trading off precision for performance in replicated data environments.
Fortunately, these conditions are met in many systems providing weakly consistent replicated data.
Session guarantees could be utilized in many existing systems that provide weakly consistent replicated data semantics.
The above guarantees can easily be layered on top of a weakly-consistent replicated data system.
There are fundamental limits on the performance of replicated data environments when exact consistency and exact pr ecision are necessary.
All have evolved from sequential codes, and have typically been parallelised using a replicated data approach.
The benchmarks compare favorably to those obtained via a parallel replicated data algorithm.
We analyze reconfigurable algorithms for managing replicated data to determine how often one should detect and react to failure conditions so that reorganization operations can be performed at the appropriate time to improve the availability of replicated data.
In the scientific literature, there is a lack of variable selection and classification methods considering replicated data.
More recently, the use of weakly consistent replicated data has been driven by the needs of mobile computing applications [11,13,22].
