StreamingHub: Interactive Stream Analysis Workflows

TL;DR

StreamingHub is a visual programming framework that enables interactive, metadata-propagating stream analysis workflows for time-series data, promoting reproducibility and rapid prototyping in interdisciplinary research.

Contribution

It introduces a novel framework for building metadata-aware, interactive stream analysis workflows with a visual programming interface, validated through multiple case studies.

Findings

Framework generalizes across multiple tasks

Minimal performance overhead observed

Supports reproducible and reusable analyses

Abstract

Reusable data/code and reproducible analyses are foundational to quality research. This aspect, however, is often overlooked when designing interactive stream analysis workflows for time-series data (e.g., eye-tracking data). A mechanism to transmit informative metadata alongside data may allow such workflows to intelligently consume data, propagate metadata to downstream tasks, and thereby auto-generate reusable, reproducible analytic outputs with zero supervision. Moreover, a visual programming interface to design, develop, and execute such workflows may allow rapid prototyping for interdisciplinary research. Capitalizing on these ideas, we propose StreamingHub, a framework to build metadata propagating, interactive stream analysis workflows using visual programming. We conduct two case studies to evaluate the generalizability of our framework. Simultaneously, we use two heuristics to evaluate their computational fluidity and data growth. Results show that our framework generalizes to multiple tasks with a minimal performance overhead.