SLEGO: A Collaborative Data Analytics System with LLM Recommender for Diverse Users
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917860461248512 |
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| author | Ng, Siu Lung Rezaei, Hirad Baradaran Rabhi, Fethi |
| author_facet | Ng, Siu Lung Rezaei, Hirad Baradaran Rabhi, Fethi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_11232 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SLEGO: A Collaborative Data Analytics System with LLM Recommender for Diverse Users Ng, Siu Lung Rezaei, Hirad Baradaran Rabhi, Fethi Software Engineering Artificial Intelligence D.2.11; I.2.1 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. |
| title | SLEGO: A Collaborative Data Analytics System with LLM Recommender for Diverse Users |
| topic | Software Engineering Artificial Intelligence D.2.11; I.2.1 |
| url | https://arxiv.org/abs/2406.11232 |