Data Centric Discovery with a Data-Oriented Architecture

Abstract

Increasingly, scientific discovery is driven by the analysis, manipulation, organization, annotation, sharing, and reuse of high-value scientific data. While great attention has been given to the specifics of analyzing and mining data, we find that there are almost no tools nor systematic infrastructure to facilitate the process of discovery from data. We argue that a more systematic perspective is required, and in particular, propose a data-centric approach in which discovery stands on a foundation of data and data collections, rather than on fleeting transformations and operations. To address the challenges of data-centric discovery, we introduce a Data-Oriented Architecture and contrast it with the prevalent Service-Oriented Architecture. We describe an instance of the Data-Oriented Architecture and describe how it has been used in a variety of use cases.