SVP { a Model Capturing Sets , Streams

We describe the SVP data model. The goal of SVP is to model both set and stream data, and to model parallelism in bulk data processing. SVP also shows promise for other parallel processing applications. SVP models collections, which include sets and streams as special cases. Collections are represented as ordered tree structures, and divide-and-conquer mappings are easily deened on these structures. We show that many useful database mappings (queries) have a divide-and-conquer format when speciied using collections, and that this speciication exposes parallelism. We formalize a class of divide-and-conquer mappings on collections called SVP-transducers. SVP-transducers generalize aggregates, set mappings, stream transductions, and scan computations. At the same time, they have a rigorous semantics based on continuity with respect to collection orderings, and permit implicit speciication of both independent and pipeline parallelism.