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In ethnographic research, data collected through surveys, interviews, or questionnaires in the fields of sociology and anthropology often appear in diverse forms and languages. Building a powerful database system to store and process such data, as well as making good and efficient queries, is very challenging. This paper extensively investigates modern database technology to find out what the best technologies to store these varied and heterogeneous datasets are. The study examines several database categories: traditional relational databases, the NoSQL family of key-value databases, graph databases, document databases, object-oriented databases and vector databases, crucial for the latest artificial intelligence solutions. The research proves that when it comes to field data, the NoSQL lineup is the most appropriate, especially document and graph databases. Simplicity and flexibility found in document databases and advanced ability to deal with complex queries and rich data relationships attainable with graph databases make these two types of NoSQL databases the ideal choice if a large amount of data has to be processed. Advancements in vector databases that embed custom metadata offer new possibilities for detailed analysis and retrieval. However, converting contents into vector data remains challenging, especially in regions with unique oral traditions and languages. Constructing such databases is labor-intensive and requires domain experts to define metadata and relationships, posing a significant burden for research teams with extensive data collections. To this end, this paper proposes using Generative AI (GenAI) to help in the data-transformation process, a recommendation that is supported by testing where GenAI has proven itself a strong supplement to document and graph databases. It also discusses two methods of vector database support that are currently viable, although each has drawbacks and benefits.

This study addresses the challenge of digitally modeling Indigenous Traditional Ecological Knowledge (TEK) in a manner that respects and preserves its epistemic integrity. Grounded in ethnographic inquiry and system design, the research introduces a four-tier knowledge typology that conceptualizes how tacit, explicit, tribal and cultural knowledge circulate within Indigenous communities. This cyclical model highlights recursive and embodied processes of knowledge internalization, transmission, and integration, offering a dynamic alternative to linear knowledge flow frameworks. Building upon this epistemological foundation, this study traces the transition from traditional data practices, which are centered on oral histories, ritual performances, and ecological observation, to a contemporary AI-assisted architecture that operationalizes these forms through structured semantic enrichment, modular knowledge storage, and culturally aligned reasoning systems. The proposed system integrates layered components, from data acquisition to multi-agent inference models, while embedding ethical protocols that affirm community sovereignty and relational authority. The findings suggest that TEK systems can be effectively encoded into modern digital infrastructures without erasing their socio-cultural contexts. By foregrounding Indigenous epistemologies within system design, the research advances a critical paradigm for culturally responsive knowledge technologies in sustainability, education, and heritage preservation.

ZigBee is a protocol suite based upon IEEE standard 802.15.4 for the construction of low-rate wireless personal area networks. Since most of ZigBee devices are powered by battery, the power saving is an important issue. In the past several years, many solutions are proposed to extend battery life by reducing device transmitting and receiving time. However, few studies have noticed that most energy is consumed in sensing and monitoring rather than communicating activities, since the data rates in most ZigBee networks are low. Besides, energy will consume the most of those nodes which act as routers or are orphans that cannot join the network. In order to extend the life time of network, we proposed a dynamic topology reformation algorithm. The algorithm consists of a cluster reformation scheme for enhancing connectivity and a self-swapping method for averaging the loading of router nodes and end devices. As the experimental result shows, compared with the ZigBee standard, our method gains 6.02% to 15.13% improvement in network join ratio. And the role swapping balances the power consumption which makes the lifetime of the whole network 1.3 to 1.375 times longer.

Services in the ubiquitous computing are heterogeneous in nature. To be pervasive, these services should be defined in terms of their functionality and capabilities rather than the meaningless Universally Unique IDentifiers (UUIDs) or types of services. With that, clients can access the proper service based on semantic requests, rather then a pre-configured profile. In this paper, we study the requirements for semantic query to be feasible in service discovery processes. Current discovery protocols and the concept of semantics are brought together to construct a framework to realize the semantic service discovery for ubiquitous computing. Many issues are discussed in relation to service discovery topologies, ontology languages, and semantic query languages.