Suggestion on Incorporating Stream Data Processing Systems in Your Research

[Pygone]

I am currently reading your article on the integration of large language models (LLMs) and data management, which provides profound insights into the bidirectional relationships between DATA4LLM and LLM4DATA.
While studying the comprehensive review, I noticed that the aspect of stream data processing systems wasn't covered. In the field of data management, stream data processing is crucial, and systems like Vectraflow are making significant strides.
Considering the dynamic nature of data in modern applications, incorporating such stream data processing systems could further enrich the discussion, especially in scenarios where real - time data feeding, processing for LLMs (such as in pre-training or RAG with continuous data streams) and LLM-powered real - time data management tasks (like real-time data cleaning and analysis in data manipulation and analysis for LLM4DATA) are involved.
I believe that adding considerations of stream data processing systems like Vectraflow could enhance the completeness of your research in the context of evolving data management paradigms combined with LLMs.

[UNAOUN]

Thank you for your thoughtful suggestion regarding the role of stream data processing systems in the context of LLM and data integration. We fully agree that the ability to process and serve data in real time is increasingly important for modern LLM applications, particularly in scenarios involving continuous data ingestion, online retrieval, and low-latency semantic filtering.
This is a valuable direction that complements the existing components of our survey. If you know of additional relevant works, we would appreciate your suggestions—and you're very welcome to submit a pull request to help expand the survey. We are also interested in your opinion on where such data flow systems (e.g., VectraFlow) would be best positioned within our survey: under data processing, data serving, or as a separate subsection.