@article{MA35EDF1E,
title = "Fat Client-Based Abstraction Model of Unstructured Data for
Context-Aware Service in Edge Computing Environment",
journal = "KIPS Transactions on Computer and Communication Systems",
year = "2021",
issn = "2287-5891",
doi = "https://doi.org/10.3745/KTCCS.2021.10.3.59",
author = "Do Hyung Kim/Jong Hyeok Mun/Yoo Sang Park/Jong Sun Choi/Jae Young Choi",
keywords = "Edge Computing, Fat Client, Context Aware, Unstructured Data Abstraction, Deep Learning",
abstract = "With the recent advancements in the Internet of Things, context-aware system that provides customized services become important
to consider. The existing context-aware systems analyze data generated around the user and abstract the context information that expresses
the state of situations. However, these datasets is mostly unstructured and have difficulty in processing with simple approaches. Therefore,
providing context-aware services using the datasets should be managed in simplified method. One of examples that should be considered
as the unstructured datasets is a deep learning application. Processes in deep learning applications have a strong coupling in a way
of abstracting dataset from the acquisition to analysis phases, it has less flexible when the target analysis model or applications are modified
in functional scalability. Therefore, an abstraction model that separates the phases and process the unstructured dataset for analysis is
proposed. The proposed abstraction utilizes a description name Analysis Model Description Language(AMDL) to deploy the analysis phases
by each fat client is a specifically designed instance for resource-oriented tasks in edge computing environments how to handle different
analysis applications and its factors using the AMDL and Fat client profiles. The experiment shows functional scalability through examples
of AMDL and Fat client profiles targeting a vehicle image recognition model for vehicle access control notification service, and conducts
process-by-process monitoring for collection-preprocessing-analysis of unstructured data."
}