🤖 AI Summary
This work addresses the limitations of existing visual data mining tools, which often operate as isolated applications and cannot be readily embedded into web environments, thereby hindering the sharing and interactive integration of analytical workflows. To overcome this, the paper introduces a “component exposition” paradigm and presents a web-based collaborative visual analytics environment that enables users to construct machine learning pipelines through modular components. The system supports real-time state propagation and dynamic exploration across components by integrating modular visual programming, reactive dataflow, and web-embedding technologies. Crucially, any component within a workflow can be seamlessly embedded into external web pages, abstracting away underlying complexity while enabling customizable views and narrative-driven data experiences. Deployment in data literacy education demonstrates that this approach significantly lowers the barrier for users to understand and apply machine learning techniques.
📝 Abstract
While visual programming of data analysis workflows has become an important vehicle for the democratization of data science, such systems remain largely confined to standalone applications and offer limited support for transitioning their visual analytics solutions into interactive web environments. As a result, data analysis pipelines are difficult to share, embed, and adapt into user-facing analytical tools. We present Orange Lab, a web-based collaborative environment for visual data analytics. At its core, Orange Lab enables users to visually construct machine learning workflows from modular components, where interactions in any component propagate seamlessly through the workflow, turning static pipelines into dynamic, reactive systems that support exploration and data-driven storytelling. Our key contribution is component exposition, a paradigm that allows authors to embed selected workflow components, or parts of their interfaces, into arbitrary web contexts, creating synchronized, interactive interfaces while hiding underlying workflow complexity. This enables the development of tailored analytical views and narrative-driven experiences that integrate data analysis directly into online materials. We demonstrate the approach through deployments in data literacy education, where embedded components guide students in hands-on exploration of machine learning concepts without requiring knowledge of the underlying system, showing that Orange Lab effectively lowers barriers to entry and supports the democratization of data science.