A method to generate context information sets from analysis results with a unified abstraction model based on an extension of data enrichment scheme. (8th December 2020)

• Record Type: Journal Article
• Authors: Park, Yoosang; Mun, Jonghyeok; Choi, Jongsun; Choi, Jaeyoung; Kim, Hoda
• Other Names: Ogiela Marek R. guestEditor.; Rahayu Wenny guestEditor.; Palmieri Francesco guestEditor.; Kalyanam Rajesh guestEditor.; Stankovski Vlado guestEditor.
• Is Part Of: Concurrency and computation. Volume 33:Number 19(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-08
• Subjects: context‐awareness -- data abstraction -- edge computing environment -- embedding convergence -- knowledge representation; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6117
• Languages: English
• ISSNs: 1532-0626

Abstract

Abstract: 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.