This paper presents the SLEGO (Software-Lego) system, a collaborative analytics platform that bridges the gap between experienced developers and novice users using a cloud-based platform with modular, reusable microservices. These microservices enable developers to share their analytical tools and workflows, while a simple graphical user interface (GUI) allows novice users to build comprehensive analytics pipelines without programming skills. Supported by a knowledge base and a Large Language Model (LLM) powered recommendation system, SLEGO enhances the selection and integration of microservices, increasing the efficiency of analytics pipeline construction. Case studies in finance and machine learning illustrate how SLEGO promotes the sharing and assembly of modular microservices, significantly improving resource reusability and team collaboration. The results highlight SLEGO's role in democratizing data analytics by integrating modular design, knowledge bases, and recommendation systems, fostering a more inclusive and efficient analytical environment.

翻译：本文提出SLEGO（软件乐高）系统，这是一个基于云平台、采用模块化可复用微服务架构的协同分析平台，旨在弥合经验丰富的开发者与新手用户之间的鸿沟。该系统使开发者能够共享其分析工具与工作流，同时通过简洁的图形用户界面（GUI）让无需编程技能的新手用户构建完整的数据分析流程。在知识库与基于大语言模型（LLM）的推荐系统支持下，SLEGO优化了微服务的筛选与集成过程，显著提升了分析流程构建效率。通过金融与机器学习领域的案例研究，本文展示了SLEGO如何促进模块化微服务的共享与组装，从而大幅提升资源复用率与团队协作效能。研究结果凸显了SLEGO通过融合模块化设计、知识库与推荐系统，在推动数据分析民主化、构建更具包容性与高效性分析环境方面的重要作用。