SVP - a Model Capturing Sets, Streams, and Parallelism

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 defined on these structures. We show that many useful database mappings (queries) have a divide-and-conquer format when specified using collections, and that this specification 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 specification of both independent and pipeline pa.ra.llelism.