• KIPS Transactions on Computer and Communication Systems
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• v.10 no.3
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• pp.59-70
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• 2021

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.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.4
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• pp.264-274
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• 2006

Recently, a variety of organizations including enterprises tend to try to use reporting tools as a data analysis tool for decision making support because reporting tools are capable of formatting data flexibly. Traditional reporting tools have thin-client structure in which all of dynamic documents are generated in the server side. This structure enables reporting tools to avoid repetitive process to generate dynamic documents, when many clients intend to access the same dynamic document. However, generating dynamic documents for data analysis doesn't consider a number of potential readers and increases requests to the server by making clients input various parameters at short intervals. In the structure of the traditional reporting tools, the increase of these requests leads to the increase of processing load in the server side. Thus, we present the reporting tool that can generate dynamic documents at the client side. This reporting tool has a processing mechanism to deal with a number of data despite the limited memory capacity of the client side.