A Collaborative Data Analytics System with Recommender for Diverse Users

Siu Lung Ng, Hirad Baradaran Rezaei, Fethi Rabhi·June 17, 2024

Summary

SLEGO is a collaborative data analytics platform that bridges the gap between developers and non-programmers by offering a cloud-based system with modular microservices. Key features include a simple GUI, a knowledge base, and an LLM-powered recommendation system. The platform democratizes analytics by allowing users with minimal technical expertise to create complex pipelines, enhancing efficiency, reusability, and team collaboration. SLEGO's architecture is designed for low-code, with Python-based microservices, standardized data processing, and a user-friendly interface. Case studies demonstrate its effectiveness in both experienced and novice users, with LLMs improving productivity. However, the system's reliance on LLMs calls for further research on prompt engineering and accuracy. Overall, SLEGO aims to simplify data analytics, promote accessibility, and foster a more inclusive and efficient analytical environment.

Key findings

6

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of bridging the gap between individuals proficient in programming and those who are not in the field of data analytics . It discusses the conventional approach of translating business users' requirements into specialized tools by software engineers, which can be inefficient and lacks flexibility . The paper also highlights the importance of creating user-friendly, generic Graphical User Interfaces (GUIs) that empower users of all types to efficiently build analytical pipelines . Additionally, it emphasizes the need for tools that offer complex and flexible data analytics features while requiring minimal technical knowledge . This problem is not entirely new but remains a significant issue in the field of data analytics, especially as tools become more powerful and require higher levels of programming skills .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that the SLEGO architecture, integrating Large Language Models (LLMs) for intelligent recommendations, enhances collaborative data analytics by streamlining the creation and modification of analytics pipelines, making advanced data analytics accessible to users at all skill levels, from novice to expert . The paper demonstrates how SLEGO's modular design facilitates the reuse and easy adaptation of developed tools for various tasks, ensuring efficient and effective analytics processes in dynamic business environments . Additionally, the paper highlights the practical implications of SLEGO in addressing critical issues in the usability and functionality of analytical tools, simplifying the process of building and managing analytics pipelines for rapid data-driven decision-making .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes a collaborative data analytics system called SLEGO that integrates Large Language Models (LLMs) for intelligent recommendations, enhancing the development and sharing of analytics components . This system aims to reduce the complexity associated with data analytics tasks by leveraging machine learning to transfer cognitive and operational tasks from human users to more capable machine systems . SLEGO's architecture fosters an environment where users can focus on strategic decision-making rather than the mechanics of data manipulation, aligning with theories of computational offloading .

One key aspect of the paper is the emphasis on reducing technical barriers for novice users in complex data analytics tasks through the LLM recommendation system, showcasing a substantial lowering of technical barriers . The system's modular design supports software sustainability principles by promoting reusability and adaptability, essential for maintaining effective analytics workflows over time . Additionally, SLEGO addresses critical issues in the usability and functionality of analytical tools, simplifying the process of building and managing analytics pipelines, which is particularly beneficial in environments where rapid data-driven decision-making is crucial .

The paper highlights the practical implications of SLEGO in addressing usability and functionality issues in analytical tools, making advanced data analytics accessible to users at all skill levels, from novice to expert . It showcases how SLEGO's intuitive user interface and seamless data integration between microservices democratize data science, making sophisticated tools broadly available . Furthermore, the paper discusses the theoretical implications of SLEGO's architecture, exemplifying the application of machine learning to enhance collaborative data analytics and promote efficient, inclusive data-driven decision-making .

In conclusion, the paper introduces SLEGO as a pioneering solution in collaborative analytics, effectively bridging the gap between technical and non-technical users, and promoting efficient, inclusive data-driven decision-making . The system's integration of LLMs for recommendation support showcases the feasibility of using machine learning to simplify complex analytics processes and make them more accessible to a wide range of users . The SLEGO collaborative data analytics system introduces several key characteristics and advantages compared to previous methods outlined in the paper .

  1. Modularity and Reusability: SLEGO's architecture emphasizes modularity and reusability through the use of microservices, enabling the development and sharing of analytics components . Users can easily select and integrate microservices to construct visualization pipelines, promoting a streamlined workflow where the output of one microservice automatically becomes the input for the next . This modular design supports software sustainability principles by facilitating the reuse and adaptation of tools for various tasks, ensuring efficient analytics processes in dynamic business environments .

  2. Flexibility and Adaptability: SLEGO demonstrates flexibility and adaptability by allowing users to adapt existing pipelines to new requirements or integrate pipelines from other teams . For instance, a new analyst member successfully repurposed an existing framework to analyze daily returns instead of moving averages, showcasing the system's adaptability to different analytical needs . This adaptability extends to integrating pipelines from other teams, as demonstrated by an analyst who adapted an AutoML pipeline for air quality predictions to predict stock returns, highlighting the system's versatility .

  3. User-Friendly Interface: SLEGO's intuitive user interface and seamless data integration between microservices cater to users at all skill levels, from novice to expert, democratizing data analytics . Novice users can leverage the system's graphical user interface to build comprehensive analytics pipelines without programming skills, significantly reducing technical barriers . The system's low-code platform with AI recommendation enhances the efficiency of analytics pipeline construction, making advanced data analytics accessible to a wide range of users .

  4. Collaborative Features: SLEGO promotes collaboration by enabling users to share and assemble modular microservices, improving resource reusability and team collaboration . The system's knowledge base and LLM-powered recommendation system enhance the selection and integration of microservices, boosting productivity by providing context-aware suggestions . This collaborative approach bridges the gap between technical and non-technical users, fostering an inclusive and efficient analytical environment .

In conclusion, SLEGO's characteristics of modularity, adaptability, user-friendliness, and collaborative features set it apart from previous methods, offering a comprehensive solution for collaborative data analytics that addresses usability, functionality, and accessibility across diverse user groups .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related researches exist in the field of collaborative data analytics systems. Noteworthy researchers in this area include Bhardwaj, A., Deshpande, A., Elmore, A.J., Karger, D., Madden, S., Parameswaran, A., Subramanyam, H., Wu, E., Zhang, R. , Wang, Z., Li, C. , Frank, U. , and Ng, S.L., Rabhi, F. . These researchers have contributed to various aspects of collaborative data analytics, including system architecture, user-friendly interfaces, and the integration of advanced analytics capabilities .

The key to the solution mentioned in the paper is the introduction of SLEGO (Software-Lego), a collaborative analytics platform that combines the simplicity of graphical interfaces with advanced analytics capabilities. SLEGO allows developers to distribute their analytical tools and workflows as microservices on a cloud-based platform, promoting a modular approach. This platform aims to make advanced data analytics accessible to users at all skill levels, from novice to expert, by offering a flexible, all-in-one solution for collaborative data analytics .


How were the experiments in the paper designed?

The experiments in the paper were designed to showcase the collaborative analytics platform, SLEGO, which integrates experienced developers and novice users through a cloud-based system with modular, reusable microservices . The experiments aimed to demonstrate how developers can share analytical tools and workflows, while novice users can build comprehensive analytics pipelines without programming skills using a simple graphical user interface (GUI) . The system leverages a knowledge base and a Large Language Model (LLM) powered recommendation system to enhance the selection and integration of microservices, thereby increasing the efficiency of constructing analytics pipelines . The experiments illustrated in the paper focused on promoting the sharing and assembly of modular microservices, improving resource reusability, and enhancing team collaboration within the data analytics domain .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the collaborative data analytics system is stored in the file "dataspace/prepared_dataset_return_ml.csv" . Regarding the code, the information provided does not specify whether the code is open source or not. It focuses on the functionality and processes within the system rather than the open-source nature of the code .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The paper introduces SLEGO, a collaborative analytics platform that combines graphical interfaces with advanced analytics capabilities, aiming to bridge the gap between technical and non-technical users in data analytics . The system's architecture, integrating Large Language Models (LLMs) for recommendations, demonstrates the application of machine learning to simplify complex data analytics tasks and reduce technical barriers . The case studies conducted with SLEGO showcase its effectiveness in enabling both simple visualizations and complex machine learning models, with the LLM recommender system significantly aiding novice users in engaging with complex data analytics tasks .

Moreover, the theoretical implications of SLEGO's architecture align with theories of computational offloading, where tasks are transferred from human users to machine systems, enabling users to focus more on strategic decision-making rather than the technical aspects of data manipulation . The practical implications of SLEGO address critical issues in the usability and functionality of analytical tools, simplifying the process of building and managing analytics pipelines, which is crucial for environments requiring rapid data-driven decision-making . The modular design of SLEGO promotes software sustainability principles by enhancing reusability and adaptability, essential for maintaining effective analytics workflows over time .

Overall, the experiments and results in the paper provide strong support for the scientific hypotheses by demonstrating the feasibility and effectiveness of SLEGO in collaborative data analytics, showcasing its ability to democratize data science, make sophisticated tools broadly accessible, and promote efficient, inclusive data-driven decision-making .


What are the contributions of this paper?

The paper makes several contributions in the field of collaborative data analytics systems with a recommender for diverse users:

  • It introduces the SLEGO architecture, which is a flexible platform enhancing collaborative data analytics by enabling the development and sharing of analytics components, integrating Large Language Models (LLMs) for intelligent recommendations .
  • The SLEGO system streamlines the creation and modification of analytics pipelines, making advanced data analytics accessible to users at all skill levels, from novice to expert, through its modular design that facilitates tool reuse and easy adaptation for various tasks .
  • The paper highlights how SLEGO's intuitive user interface and seamless data integration between microservices democratize data science by making sophisticated tools broadly available, showcasing the effectiveness of low-code platforms in promoting inclusive data-driven decision-making .
  • Case studies demonstrate SLEGO's effectiveness in enabling both simple visualizations and complex machine learning models, with an LLM recommender enhancing productivity by providing context-aware suggestions .
  • Despite its advancements, ongoing research is needed to address challenges such as maintaining recommendation system accuracy and scaling the knowledge base for long-term success .

What work can be continued in depth?

Further research can be conducted to enhance the robustness of the recommendation system in the SLEGO framework. The current system provides suggestions based on Large Language Models (LLMs) but requires more development to ensure stability and effectiveness . Additionally, exploring methods to measure the effectiveness of the recommendation system and expanding the knowledge base of microservices and analytics pipelines would contribute to the system's reliability and performance . This deeper investigation is crucial to develop more advanced and dependable approaches for prompt engineering in recommendations within the collaborative data analytics environment .


Introduction
Background
Evolution of data analytics platforms
Importance of accessibility in data analysis
Objective
To bridge the developer-novice user gap
Democratize analytics through low-code solutions
Enhance efficiency and collaboration
LLM-Powered Innovation
Integration of LLMs in the platform
Impact on productivity and user experience
Methodology
Platform Architecture
Cloud-based system
Modular microservices (Python-based)
Low-code design
Standardized data processing
User Interface
Simple GUI for non-programmers
Knowledge base for guidance
LLM-powered recommendations
Data Collection and Processing
Data acquisition methods
Integration with various data sources
Data Preprocessing Techniques
Automated data cleaning
Feature engineering with LLM assistance
Case Studies
Success stories with experienced and novice users
Impact of LLMs on user performance
Challenges and Future Research
Prompt Engineering
Improving LLM prompts for better accuracy
Customization for diverse use cases
LLM Accuracy and Reliability
Assessing LLM-generated solutions
Limitations and potential biases
Conclusion
SLEGO's contribution to the analytics landscape
Promoting accessibility and inclusivity
Vision for the platform's growth and development
Basic info
papers
software engineering
artificial intelligence
Advanced features
Insights
How does LLM integration in SLEGO impact productivity and collaboration in data analytics?
What are the key features that make SLEGO user-friendly for non-technical users?
What is SLEGO primarily designed for?
How does SLEGO bridge the gap between developers and non-programmers in data analytics?

A Collaborative Data Analytics System with Recommender for Diverse Users

Siu Lung Ng, Hirad Baradaran Rezaei, Fethi Rabhi·June 17, 2024

Summary

SLEGO is a collaborative data analytics platform that bridges the gap between developers and non-programmers by offering a cloud-based system with modular microservices. Key features include a simple GUI, a knowledge base, and an LLM-powered recommendation system. The platform democratizes analytics by allowing users with minimal technical expertise to create complex pipelines, enhancing efficiency, reusability, and team collaboration. SLEGO's architecture is designed for low-code, with Python-based microservices, standardized data processing, and a user-friendly interface. Case studies demonstrate its effectiveness in both experienced and novice users, with LLMs improving productivity. However, the system's reliance on LLMs calls for further research on prompt engineering and accuracy. Overall, SLEGO aims to simplify data analytics, promote accessibility, and foster a more inclusive and efficient analytical environment.
Mind map
Limitations and potential biases
Assessing LLM-generated solutions
Customization for diverse use cases
Improving LLM prompts for better accuracy
Impact of LLMs on user performance
Success stories with experienced and novice users
Feature engineering with LLM assistance
Automated data cleaning
Integration with various data sources
Data acquisition methods
LLM-powered recommendations
Knowledge base for guidance
Simple GUI for non-programmers
Standardized data processing
Low-code design
Modular microservices (Python-based)
Cloud-based system
Impact on productivity and user experience
Integration of LLMs in the platform
Enhance efficiency and collaboration
Democratize analytics through low-code solutions
To bridge the developer-novice user gap
Importance of accessibility in data analysis
Evolution of data analytics platforms
Vision for the platform's growth and development
Promoting accessibility and inclusivity
SLEGO's contribution to the analytics landscape
LLM Accuracy and Reliability
Prompt Engineering
Case Studies
Data Preprocessing Techniques
Data Collection and Processing
User Interface
Platform Architecture
LLM-Powered Innovation
Objective
Background
Conclusion
Challenges and Future Research
Methodology
Introduction
Outline
Introduction
Background
Evolution of data analytics platforms
Importance of accessibility in data analysis
Objective
To bridge the developer-novice user gap
Democratize analytics through low-code solutions
Enhance efficiency and collaboration
LLM-Powered Innovation
Integration of LLMs in the platform
Impact on productivity and user experience
Methodology
Platform Architecture
Cloud-based system
Modular microservices (Python-based)
Low-code design
Standardized data processing
User Interface
Simple GUI for non-programmers
Knowledge base for guidance
LLM-powered recommendations
Data Collection and Processing
Data acquisition methods
Integration with various data sources
Data Preprocessing Techniques
Automated data cleaning
Feature engineering with LLM assistance
Case Studies
Success stories with experienced and novice users
Impact of LLMs on user performance
Challenges and Future Research
Prompt Engineering
Improving LLM prompts for better accuracy
Customization for diverse use cases
LLM Accuracy and Reliability
Assessing LLM-generated solutions
Limitations and potential biases
Conclusion
SLEGO's contribution to the analytics landscape
Promoting accessibility and inclusivity
Vision for the platform's growth and development
Key findings
6

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of bridging the gap between individuals proficient in programming and those who are not in the field of data analytics . It discusses the conventional approach of translating business users' requirements into specialized tools by software engineers, which can be inefficient and lacks flexibility . The paper also highlights the importance of creating user-friendly, generic Graphical User Interfaces (GUIs) that empower users of all types to efficiently build analytical pipelines . Additionally, it emphasizes the need for tools that offer complex and flexible data analytics features while requiring minimal technical knowledge . This problem is not entirely new but remains a significant issue in the field of data analytics, especially as tools become more powerful and require higher levels of programming skills .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that the SLEGO architecture, integrating Large Language Models (LLMs) for intelligent recommendations, enhances collaborative data analytics by streamlining the creation and modification of analytics pipelines, making advanced data analytics accessible to users at all skill levels, from novice to expert . The paper demonstrates how SLEGO's modular design facilitates the reuse and easy adaptation of developed tools for various tasks, ensuring efficient and effective analytics processes in dynamic business environments . Additionally, the paper highlights the practical implications of SLEGO in addressing critical issues in the usability and functionality of analytical tools, simplifying the process of building and managing analytics pipelines for rapid data-driven decision-making .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes a collaborative data analytics system called SLEGO that integrates Large Language Models (LLMs) for intelligent recommendations, enhancing the development and sharing of analytics components . This system aims to reduce the complexity associated with data analytics tasks by leveraging machine learning to transfer cognitive and operational tasks from human users to more capable machine systems . SLEGO's architecture fosters an environment where users can focus on strategic decision-making rather than the mechanics of data manipulation, aligning with theories of computational offloading .

One key aspect of the paper is the emphasis on reducing technical barriers for novice users in complex data analytics tasks through the LLM recommendation system, showcasing a substantial lowering of technical barriers . The system's modular design supports software sustainability principles by promoting reusability and adaptability, essential for maintaining effective analytics workflows over time . Additionally, SLEGO addresses critical issues in the usability and functionality of analytical tools, simplifying the process of building and managing analytics pipelines, which is particularly beneficial in environments where rapid data-driven decision-making is crucial .

The paper highlights the practical implications of SLEGO in addressing usability and functionality issues in analytical tools, making advanced data analytics accessible to users at all skill levels, from novice to expert . It showcases how SLEGO's intuitive user interface and seamless data integration between microservices democratize data science, making sophisticated tools broadly available . Furthermore, the paper discusses the theoretical implications of SLEGO's architecture, exemplifying the application of machine learning to enhance collaborative data analytics and promote efficient, inclusive data-driven decision-making .

In conclusion, the paper introduces SLEGO as a pioneering solution in collaborative analytics, effectively bridging the gap between technical and non-technical users, and promoting efficient, inclusive data-driven decision-making . The system's integration of LLMs for recommendation support showcases the feasibility of using machine learning to simplify complex analytics processes and make them more accessible to a wide range of users . The SLEGO collaborative data analytics system introduces several key characteristics and advantages compared to previous methods outlined in the paper .

  1. Modularity and Reusability: SLEGO's architecture emphasizes modularity and reusability through the use of microservices, enabling the development and sharing of analytics components . Users can easily select and integrate microservices to construct visualization pipelines, promoting a streamlined workflow where the output of one microservice automatically becomes the input for the next . This modular design supports software sustainability principles by facilitating the reuse and adaptation of tools for various tasks, ensuring efficient analytics processes in dynamic business environments .

  2. Flexibility and Adaptability: SLEGO demonstrates flexibility and adaptability by allowing users to adapt existing pipelines to new requirements or integrate pipelines from other teams . For instance, a new analyst member successfully repurposed an existing framework to analyze daily returns instead of moving averages, showcasing the system's adaptability to different analytical needs . This adaptability extends to integrating pipelines from other teams, as demonstrated by an analyst who adapted an AutoML pipeline for air quality predictions to predict stock returns, highlighting the system's versatility .

  3. User-Friendly Interface: SLEGO's intuitive user interface and seamless data integration between microservices cater to users at all skill levels, from novice to expert, democratizing data analytics . Novice users can leverage the system's graphical user interface to build comprehensive analytics pipelines without programming skills, significantly reducing technical barriers . The system's low-code platform with AI recommendation enhances the efficiency of analytics pipeline construction, making advanced data analytics accessible to a wide range of users .

  4. Collaborative Features: SLEGO promotes collaboration by enabling users to share and assemble modular microservices, improving resource reusability and team collaboration . The system's knowledge base and LLM-powered recommendation system enhance the selection and integration of microservices, boosting productivity by providing context-aware suggestions . This collaborative approach bridges the gap between technical and non-technical users, fostering an inclusive and efficient analytical environment .

In conclusion, SLEGO's characteristics of modularity, adaptability, user-friendliness, and collaborative features set it apart from previous methods, offering a comprehensive solution for collaborative data analytics that addresses usability, functionality, and accessibility across diverse user groups .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related researches exist in the field of collaborative data analytics systems. Noteworthy researchers in this area include Bhardwaj, A., Deshpande, A., Elmore, A.J., Karger, D., Madden, S., Parameswaran, A., Subramanyam, H., Wu, E., Zhang, R. , Wang, Z., Li, C. , Frank, U. , and Ng, S.L., Rabhi, F. . These researchers have contributed to various aspects of collaborative data analytics, including system architecture, user-friendly interfaces, and the integration of advanced analytics capabilities .

The key to the solution mentioned in the paper is the introduction of SLEGO (Software-Lego), a collaborative analytics platform that combines the simplicity of graphical interfaces with advanced analytics capabilities. SLEGO allows developers to distribute their analytical tools and workflows as microservices on a cloud-based platform, promoting a modular approach. This platform aims to make advanced data analytics accessible to users at all skill levels, from novice to expert, by offering a flexible, all-in-one solution for collaborative data analytics .


How were the experiments in the paper designed?

The experiments in the paper were designed to showcase the collaborative analytics platform, SLEGO, which integrates experienced developers and novice users through a cloud-based system with modular, reusable microservices . The experiments aimed to demonstrate how developers can share analytical tools and workflows, while novice users can build comprehensive analytics pipelines without programming skills using a simple graphical user interface (GUI) . The system leverages a knowledge base and a Large Language Model (LLM) powered recommendation system to enhance the selection and integration of microservices, thereby increasing the efficiency of constructing analytics pipelines . The experiments illustrated in the paper focused on promoting the sharing and assembly of modular microservices, improving resource reusability, and enhancing team collaboration within the data analytics domain .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the collaborative data analytics system is stored in the file "dataspace/prepared_dataset_return_ml.csv" . Regarding the code, the information provided does not specify whether the code is open source or not. It focuses on the functionality and processes within the system rather than the open-source nature of the code .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The paper introduces SLEGO, a collaborative analytics platform that combines graphical interfaces with advanced analytics capabilities, aiming to bridge the gap between technical and non-technical users in data analytics . The system's architecture, integrating Large Language Models (LLMs) for recommendations, demonstrates the application of machine learning to simplify complex data analytics tasks and reduce technical barriers . The case studies conducted with SLEGO showcase its effectiveness in enabling both simple visualizations and complex machine learning models, with the LLM recommender system significantly aiding novice users in engaging with complex data analytics tasks .

Moreover, the theoretical implications of SLEGO's architecture align with theories of computational offloading, where tasks are transferred from human users to machine systems, enabling users to focus more on strategic decision-making rather than the technical aspects of data manipulation . The practical implications of SLEGO address critical issues in the usability and functionality of analytical tools, simplifying the process of building and managing analytics pipelines, which is crucial for environments requiring rapid data-driven decision-making . The modular design of SLEGO promotes software sustainability principles by enhancing reusability and adaptability, essential for maintaining effective analytics workflows over time .

Overall, the experiments and results in the paper provide strong support for the scientific hypotheses by demonstrating the feasibility and effectiveness of SLEGO in collaborative data analytics, showcasing its ability to democratize data science, make sophisticated tools broadly accessible, and promote efficient, inclusive data-driven decision-making .


What are the contributions of this paper?

The paper makes several contributions in the field of collaborative data analytics systems with a recommender for diverse users:

  • It introduces the SLEGO architecture, which is a flexible platform enhancing collaborative data analytics by enabling the development and sharing of analytics components, integrating Large Language Models (LLMs) for intelligent recommendations .
  • The SLEGO system streamlines the creation and modification of analytics pipelines, making advanced data analytics accessible to users at all skill levels, from novice to expert, through its modular design that facilitates tool reuse and easy adaptation for various tasks .
  • The paper highlights how SLEGO's intuitive user interface and seamless data integration between microservices democratize data science by making sophisticated tools broadly available, showcasing the effectiveness of low-code platforms in promoting inclusive data-driven decision-making .
  • Case studies demonstrate SLEGO's effectiveness in enabling both simple visualizations and complex machine learning models, with an LLM recommender enhancing productivity by providing context-aware suggestions .
  • Despite its advancements, ongoing research is needed to address challenges such as maintaining recommendation system accuracy and scaling the knowledge base for long-term success .

What work can be continued in depth?

Further research can be conducted to enhance the robustness of the recommendation system in the SLEGO framework. The current system provides suggestions based on Large Language Models (LLMs) but requires more development to ensure stability and effectiveness . Additionally, exploring methods to measure the effectiveness of the recommendation system and expanding the knowledge base of microservices and analytics pipelines would contribute to the system's reliability and performance . This deeper investigation is crucial to develop more advanced and dependable approaches for prompt engineering in recommendations within the collaborative data analytics environment .

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