Reconstructing unsound data provenance view in scientific workflow

Abstract

The view of data provenance provides an approach of data abstraction and encapsulation by partitioning tasks in the data provenance graph (DPG) of scientific workflow into a set of composite modules due to the data flow relations among them, so as to efficiently decrease the workload consumed by researchers making analysis on the data provenance and the time needed in doing data querying. However, unless a view is carefully designed, it may not preserve the dataflow between tasks in the workflow. Concentrating on this scenario, we propose a method for reconstructing unsound view. We also design a polynomial-time algorithm, and analyze its maximal time complexity. Finally, we give an example and conduct comprehensive experiments to show the feasibility and effectiveness of our method. © 2012 Springer-Verlag Berlin Heidelberg.