In recent years, NoSQL databases have emerged as an answer to Internet scale datasets. However, these solutions do not support out of the box the necessary backup, restore and disaster recovery capabilities required by many big data scenarios. In this paper we address this gap: we present a novel disaster recovery approach designed for big data NoSQL workloads. This approach can support low recovery point and time objectives at low costs. Our approach is highly scalable and elastic, based on open source components and suitable for any document-based NoSQL solution, which makes it highly attractive for diverse big data workloads.

We present a new algorithm for computing interprocedural dominators. The algorithm identifies a set of special nodes, which are the only ones that can have interprocedural dominance edges, and extends the intraprocedural dominator trees by deriving those edges. The computation of the dominators of each node is independent of the computation of any other node, and therefore can be done on demand for each node as required. For the same reason, the algorithm lends itself naturally to parallelization. The algorithm has been implemented, and is shown to be practical for large programs. Because of its cooperative caching behavior, the algorithm gains a large performance boost when running on parallel hardware. We also present an efficient way of extending the algorithm for computing interprocedural dominance frontiers and control dependence.

Program slicing is a useful technique in tools for program understanding and transformation. Computing correct and accurate slices for unstructured programs is particularly difficult. We present a new family of slicing algorithms that are proved correct and are more accurate than the best previous algorithms. An empirical study shows significant improvements of our algorithms on real code.