Data Analytics as Data: A Semantic Workflow Approach

Kristin Bennett, John Erickson, Hannah De Los Santos, Evan Patton, John Sheehan, Deborah McGuinness

By treating the end-to-end data science workflow as data itself and through the conceptual modeling of the goals and functional intent of the data analyst, the entire process of data analytics becomes open and accessible to the powerful tools of artificial intelligence, machine learning, statistics, and data mining. We examine the fundamental questions and capabilities that must be addressed to realize cap- turing and reasoning over workflows as well as interpreting and contextualizing their results. Our approach focuses on capturing key components of complete workflow processes, making explicit the “deep” semantics of the workflow plan; the analysis performed; the structure and sub-components of the workflow; and intermediate and final data products. Our goal is to provide sufficient detail to facilitate practical workflow and work product integration, interpretation, reuse, reproducibility, recommendation, and search. The structure for this workflow-as- data view is formalized by an extensible, reusable ontology that we are creating that applies to all aspects of the workflow representation and reasoning process. We report on our exploration and reuse of existing methods, tools and ontologies as well as our semantic analytics contributions to real world projects addressing childhood health challenges.