  Cloud computing has recently emerged as a key technology to provide
individuals and companies with access to remote computing and storage
infrastructures. In order to achieve highly-available yet high-performing
services, cloud data stores rely on data replication. However, providing
replication brings with it the issue of consistency. Given that data are
replicated in multiple geographically distributed data centers, and to meet the
increasing requirements of distributed applications, many cloud data stores
adopt eventual consistency and therefore allow to run data intensive operations
under low latency. This comes at the cost of data staleness. In this paper, we
prioritize data replication based on a set of flexible data semantics that can
best suit all types of Big Data applications, avoiding overloading both network
and systems during large periods of disconnection or partitions in the network.
Therefore we integrated these data semantics into the core architecture of a
well-known NoSQL data store (e.g., HBase), which leverages a three-dimensional
vector-field model (regarding timeliness, number of pending updates and
divergence bounds) to provision data selectively in an on-demand fashion to
applications. This enhances the former consistency model by providing a number
of required levels of consistency to different applications such as, social
networks or e-commerce sites, where priority of updates also differ. In
addition, our implementation of the model into HBase allows updates to be
tagged and grouped atomically in logical batches, akin to transactions,
ensuring atomic changes and correctness of updates as they are propagated.
