# GenAI-Assisted Database Deployment for Heterogeneous Indigenous-Native Ethnographic Research Data
## 6 Discussion and Conclusions
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Relational** & **Key-Value** & **Document** & **Graph** & **Vector** \\  & **Database** & **NoSQL** & **Database** & **Database** & **Database** \\ \hline Cost & High & Low & Low & Low & High \\ \hline Configuration & Easy & Easy & Medium & Medium & Hard \\ \hline Performance & High & High & Low & Medium & Low \\ \hline Schema-defined & Hard & No need & No need & Hard & Difficult \\ \hline Data Insert/Modify/Delete & Easy & Easy & Easy & Hard & TBD \\ \hline Semantic Query & No & No & Keywords & Keywords. & Keywords, \\  & & & & Relations & Semantic \\ \hline GenAI-assisted & Not evaluated & Not evaluated & Metadata & Metadata, & Metadata, & Vector \\  & & & & Relations & embedding \\ \hline Overall & Not good & Not good & Good & Good & Incomplete \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of different databases for ERD storage. necessary changes may require extensive adjustments as they tend to have cascading effects throughout the system. It is demonstrated in this research how GenAI can be leveraged to assist in the construction process. This technology facilitates the automation of tasks such as metadata generation and relationship connection, enhancing the efficiency and accuracy of building complex data systems. By optimizing prompt engineering and providing support, importing individual data records has shown promising results. However, for future applications involving overlapping relationships between multiple data entries, it will be crucial to design a knowledge base for GenAI to use as a benchmark. This is necessary to prevent the generated relations from diverging too broadly, thereby maintaining the advantages of semantic topologies. The challenges for Vector databases, renowned for their advanced search power, lie in complex modeling and algorithmic configuration, as well as in storage demands. Despite these issues, their promise to process multilingual data and integrate with cutting-edge AI technology makes them unbeatable future options. Although vector databases theoretically offer the most ideal form of semantic querying, not just for keywords but also for semantic similarity, perfecting a vector database construction that fits local and regional applications is still exceedingly challenging. The study attempts to assist in the construction process using two different generative AI methods, and is observed as the following:  
1. Generating vector embeddings through self-trained models shows better results for data containing indigenous languages and scripts, such as the Paiwan language written in Roman script. However, developing these models requires considerable computational resources and requires high hardware construction costs;
2. Using commercial services like the OpenAI API can quickly produce results, but the accuracy for indigenous linguistic data is not ideal. This is because current commercial platforms primarily use English training data, making it nearly impossible to meet the needs of languages like Paiwan in our research field in the short term. For example, the paragraph in Figure 17 is an oral conversation of the South-Paiwan language recorded in the Romanized script. The paragraph states that \"when I was young, my father and uncle would go out at night with hunting knives and rifles. By dawn, they would return with their catches. Sometimes it was wild boar, sometimes flying squirrels and occasionally pheasants. They would process the catch at home and share it with the members of our community\". However, if a commercial service like ChatGPT 4o is used to analyze and understand the meaning, the results will be displayed as shown in Figure 18. Responses from ChatGPT 4o exhibit the lack of recognition of indigenous weapons and the understanding of indigenous prey. It is also not understood that hunting takes place at night and the concept of sharing after hunting is a common practice. The model is only capable of summarily identifying the discourse as a familial activity based on kinship terminology.