Streaming Analytics and Workflow Automation for DFS

Researchers reuse data from past studies to avoid costly re-collection of experimental data. However, large-scale data reuse is challenging due to lack of consensus on metadata representations among research groups and disciplines. Dataset File System (DFS) is a semi-structured data description format that promotes such consensus by standardizing the semantics of data description, storage, and retrieval. In this paper, we present analytic-streams - a specification for streaming data analytics with DFS, and streaming-hub - a visual programming toolkit built on DFS to simplify data analysis workflows. Analytic-streams facilitate higher-order data analysis with less computational overhead, while streaming-hub enables storage, retrieval, manipulation, and visualization of data and analytics. We discuss how they simplify data pre-processing, aggregation, and visualization, and their implications on data analysis workflows.