Orange Lab: Lowering Barriers to Data Mining through Embedded Interactive Workflows

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.

Machine Learning (cs.LG); Human-Computer Interaction (cs.HC)

arXiv:2606.09239 [cs.LG]

(or arXiv:2606.09239v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.09239 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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