# GenAI-Assisted Database Deployment for Heterogeneous Indigenous-Native Ethnographic Research Data
###### Abstract
They suggested that this approach can yield valuable insights into the social and political implications of database use and design. To develop cross-cultural databases, [PERSON] et al. [4] discussed several methodologies focusing on best practices and principles that enhance the reliability and validity of the data. Key methodologies include the use of existing ethnographic materials, the transformation of qualitative descriptions, standardization of coding procedures, retention of source information, addressing biases and limitations, incorporation of metadata and interdisciplinary collaboration. These methodologies are used as the reference criteria in our evaluations. The current lack of theoretical or practical technical comparisons in the transition from ethnography to databases makes it difficult to find appropriate reference models when we try to construct these databases. This research aims to evaluate various database technologies such as Traditional Relational Databases, NoSQL Key-Value Databases, Graph Databases, Document Databases, Object-Oriented Databases and Vector Databases, which are building the capability to improve traditional database architecture for managing diverse data types such as ERD from field surveys and questionnaires. The various technologies are compared to the specific needs for storing heterogeneous data derived from the combination of modern technology and traditional knowledge. In contrast, the recent surge in artificial intelligence has introduced the complex challenge of integrating AI with database construction. [PERSON] et al. [5] conducted a detailed analysis of the early relationship between AI and databases. They described a learning-based database configuration that automates tasks such as knob tuning and indexing and viewing, a learning-based database optimization to improve performance through cost estimation and join order selection, a learning-based database design, monitoring and security, all using AI to improve efficiency. These techniques address the capabilities of traditional database systems, making them more intelligent and capable of handling large-scale applications. The term \"early\" refers to the period before the advent of generative AI, despite being relatively recent. Some scholars have also explored the primary aim of this paper--addressing database issues pertinent to humanities data. [PERSON] [6] developed a database system specifically for the preservation and management of intangible cultural heritage, using modern database technology, but only the MySQL with MVC Framework is addressed. With the rapid advancement of generative AI in recent years, research has increasingly focused on integrating large language models with databases. Some scholars have endeavored to transform substantial data from traditional databases into the knowledge base of large language models. [PERSON] et al. [7] identified three core challenges in applying generative AI to databases. They are accuracy, scale and privacy. They also proposed a framework that automates the process of turning data from databases into actionable insights. It leverages a large data model to generate relevant data models in response to user questions and employs a scalable retrieval approach to handle databasesbut can only be applied on SQL databases. Other researchers have sought to leverage large language models to tackle issues related to knowledge graphs. [PERSON] et al. [8] proposed a large language model with knowledge graph-enhancement to solve the challenge of complex knowledge structures between actions and events. This is the same concept as what we do with graph databases and vector databases, as seen below. Furthermore, our research adopts a more pragmatic approach, aiming to construct large language models in real-world environments to facilitate the development of humanities databases. Another prominent concern with integrating ethnographic data into digital formats is research ethics, particularly when AI is used in data processing.