Provenance-based quality assessment and inference in data-centric workflow executions

Abstract

In this article we present a rule-based quality model for data centric workflows. The goal is to build a tool assisting workflow designers and users in annotating, exploring and improving the quality of data produced by complex media mining workflow executions. Our approach combines an existing fine-grained provenance generation approach [3] with a new quality assessment model for annotating XML fragments with data/application-specific quality values and inferring new values from existing annotations and provenance dependencies. We define the formal semantics using an appropriate fixpoint operator and illustrate how it can be implemented using standard Jena inference rules provided by current semantic web infrastructures.

In this article we present a rule-based quality model for data centric workflows. The goal is to build a tool assisting workflow designers and users in annotating, exploring and improving the quality of data produced by complex media mining workflow execu