SLEGO: A Collaborative Data Analytics System with LLM Recommender for Diverse Users
This work addresses the problem of democratizing data analytics for diverse user groups, though it appears incremental by combining existing concepts like microservices and LLMs into a new system.
The paper tackles the challenge of enabling both experienced developers and novice users to collaborate on data analytics by introducing SLEGO, a cloud-based platform with modular microservices and an LLM-powered recommender, which improves resource reusability and team collaboration as shown in finance and ML case studies.
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.