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A method to generate context information sets from analysis results with a unified abstraction model based on an extension of data enrichment scheme
Author(s) -
Park Yoosang,
Mun Jonghyeok,
Choi Jongsun,
Choi Jaeyoung,
Kim Hoda
Publication year - 2020
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.6117
Subject(s) - computer science , abstraction , context (archaeology) , cloud computing , data science , process (computing) , extension (predicate logic) , object (grammar) , data model (gis) , big data , data processing , scheme (mathematics) , data mining , theoretical computer science , database , artificial intelligence , programming language , paleontology , mathematical analysis , philosophy , mathematics , epistemology , biology , operating system
Context‐awareness techniques generally deal with steps for processing information that can be gathered from perspectives of objects in real‐world scenes in accordance with resolving heterogeneous devices and providing meaningful context information in recent computing domains such as Internet of Things, cloud computing, edge computing, and big data analysis. However, analysis applications are necessary to be improved in a way of cooperative approaches because they are independently executed far away from context‐aware systems. This means that the stages are applied to process the data or the derived results, are isolated. That leads difficulties of management and integration for other software modules. As a result, a computing paradigm called edge computing has been suggested to compromise such isolations. For processing context information, it is necessary to establish knowledge parts with validation processes against the isolations. In this paper, we suggest a method for processing datasets in a view of building knowledge parts. The suggested method uses an extensible data enrichment scheme, which represents the relations for the object. In the experiment section, applications of the proposed method are shown in one of the healthcare community centers, and the data abstraction with the analysis jobs can be processed for each real‐world object.