A method to generate context information sets from analysis results with a unified abstraction model based on an extension of data enrichment scheme

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. K E Y W O R D Scontext-awareness, data abstraction, edge computing environment, embedding convergence, knowledge representation INTRODUCTIONRecent improvements in computational methodologies and technologies such as cloud computing, Internet of Things (IoT), 5G networks, and big data have enabled to build meaningful knowledge for artificial intelligence. [1][2][3][4][5] Among these improvements, there has been innovative approaches to develop the subsets of an architecture for application in computation domains. It is important to consider that bringing the advantages provides benefits to lives for human being and society for transformative innovation. [6][7][8] Because recent trends and challenges have focused on processing massive amounts of data, also known as big data, so that different approaches related to the collection, management, and application of such datasets have been conducted. Therefore, it is necessary to design the stages that are highly associated with handling these datasets. 9,10 As the result, both processing and providing meaningful context information are important techniques where massive data can be generated on state-of-the-art domains such as IoT, edge computing, or big data analysis. For context-aware systems, despite combining the key techniques mentioned above, there remain certain difficulties to overcome. [11][12][13] In general, context-awareness consists of resolving hardware heterogeneity, data acquisition, collection of

Human oriented solutions for intelligent analysis, multimedia and communication systems

“…The article entitled “A method to generate context information sets from analysis results with a unified abstraction model based on an extension of data enrichment scheme” by Park et al 2 presents studies on a method for processing analysis modules that can enrich result datasets with context information based on a data abstraction model. Data abstraction provides not only capabilities for context‐aware systems and users to inspect the context at four levels from raw datasets to situational relationships, but also supports unified context levels for each entity that can be deployed at any location where systems deal with context to provide dedicated services.…”