• ETRI Journal
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• 제27권6호
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• pp.666-676
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• 2005

In recent years, motivated by the emergence of ubiquitous computing technologies, a new class of networked robots, ubiquitous robots, has been introduced. The Ubiquitous Robotic Companion (URC) is our conceptual vision of ubiquitous service robots that provide users with the services they need, anytime and anywhere in ubiquitous computing environments. To realize the vision of URC, one of the essential requirements for robotic systems is to support ubiquity of services: that is, a robot service must be always available even though there are changes in the service environments. Specifically robotic systems need to be automatically interoperable with sensors and devices in current service environments, rather than statically preprogrammed for them. In this paper, the design and implementation of a semantic-based ubiquitous robotic space (SemanticURS) is presented. SemanticURS enables automated integration of networked robots into ubiquitous computing environments exploiting Semantic Web Services and AI-based planning technologies.

• ETRI Journal
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• 제29권5호
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• pp.545-558
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• 2007

This paper presents an efficient semantic service discovery scheme called UbiSearch for a large-scale ubiquitous computing environment. A semantic service discovery network in the semantic vector space is proposed where services that are semantically close to each other are mapped to nearby positions so that the similar services are registered in a cluster of resolvers. Using this mapping technique, the search space for a query is efficiently confined within a minimized cluster region while maintaining high accuracy in comparison to the centralized scheme. The proposed semantic service discovery network provides a number of novel features to evenly distribute service indexes to the resolvers and reduce the number of resolvers to visit. Our simulation study shows that UbiSearch provides good semantic searchability as compared to the centralized indexing system. At the same time, it supports scalable semantic queries with low communication overhead, balanced load distribution among resolvers for service registration and query processing, and personalized semantic matching.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권6호
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• pp.2761-2781
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• 2018

With the continuous growth of data size and the use of compression technology, data reduction has great research value and practical significance. Aiming at the shortcomings of the existing semantic compression algorithm, this paper is based on the analysis of ItCompress algorithm, and designs a method of bidirectional order selection based on interval partitioning, which named An Optimized Iterative Semantic Compression Algorithm (Optimized ItCompress Algorithm). In order to further improve the speed of the algorithm, we propose a parallel optimization iterative semantic compression algorithm using GPU (POICAG) and an optimized iterative semantic compression algorithm using Spark (DOICAS). A lot of valid experiments are carried out on four kinds of datasets, which fully verified the efficiency of the proposed algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제2권5호
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• pp.239-252
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• 2008

To facilitate the processes of e-learning resource description, discovery and reuse, e-learning objects should be appropriately described and classified using standard metadata that need to be published in a registry to reduce duplication of effort and enhance semantic interoperability. This paper describes how standard ebXML registries can be used for semantic grid computing for annotating, storing, discovering and retrieving e-learning object metadata. For semantic annotation of e-learning objects, IEEE Learning Object Metadata (LOM) is adopted as the metadata ontology. In order to support the e-learning metadata ontology in interoperable ebXML registries, a mapping scheme between LOM and ebXML Registry Information Model (RIM) is proposed. The usefulness of sharing e-learning object metadata is demonstrated by prototyping a semantic registry based on the scheme.

• Architectural research
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• 제12권2호
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• pp.15-22
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• 2010

Our prediction model is based on the development of "Semantic Location Model." It embodies geometrical and topological information which can increase the efficiency in prediction and make it easy to manipulate the prediction model. Data mining is being implemented to extract the inhabitant's location patterns generated day by day. As a result, the self-learning system will be able to semantically predict the inhabitant's location in advance. This context-aware system brings about the key component of the ubiquitous computing environment. First, we explain the semantic location model and data mining methods. Then the location prediction model for the ubiquitous computing system is described in details. Finally, the prototype system is introduced to demonstrate and evaluate our prediction model.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.178-183
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• 2009

Online image dictionary has become more and more popular in concepts cognition. However, for existing online systems, only very few images are manually picked to demonstrate the concepts. Currently, there is very little research found on automatically choosing large scale online images with the help of semantic analysis. In this paper, we propose a novel framework to utilize community-generated online multimedia content to visually illustrate certain concepts. Our proposed framework adapts various techniques, including the correlation analysis, semantic and visual clustering to produce sets of high quality, precise, diverse and representative images to visually translate a given concept. To make the best use of our results, a user interface is deployed, which displays the representative images according the latent semantic coherence. The objective and subjective evaluations show the feasibility and effectiveness of our approach.

• 한국컴퓨터정보학회논문지
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• 제17권8호
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• pp.71-80
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• 2012

최근 클라우드 컴퓨팅을 위한 많은 표준화 연구가 진행되어 왔으나 데이터 공유 및 교환을 위한 표준 연구는 거의 이루어지지 않았다. 무엇보다 해당 표준화 기구에서는 의미 상호운용성 향상을 위한 구체적인 표준화 항목 및 참조 모델을 제시하고 있지 않다. 이 논문에서는 현재까지의 표준화 문제점을 정의하고, 의미 상호운용성 제공을 위한 구체적인 표준화 항목 및 참조 모델을 제안한다. 이를 위해 먼저 클라우드 컴퓨팅을 위한 전반적인 표준화 동향을 분석하고, 특히 데이터의 의미적 상호운용성을 제공을 위한 표준 개발 현황을 기술한다. 또한 다양한 분야의 표준개발에 이용된 데이터 교환 및 관리 분야의 표준 개념을 기반으로 잠재적인 표준화 항목을 정의한다. 마지막으로, 표준화 항목 간의관계성과 전체적인 의미 상호운용성 과정을 보여주기 위해 참조 모델을 기술한다. 이 논문은 표준 개발을 위한 가이드라인으로서 이용될 수 있으며, 아울러 클라우드 컴퓨팅의 표준화를 촉진할 수 있다.

• 인터넷정보학회논문지
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• 제10권4호
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• pp.87-103
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• 2009

그리드 컴퓨팅 환경은 기존의 분산 컴퓨팅 기술의 확장된 개념으로, 다양한 분야에서 저비용의 고성능 컴퓨팅 퍼포먼스를 얻기 위하여 그리드를 이용하고 있다. 그러나 공통적으로 이용할 수 있는 정보 시스템의 부재로, 현재의 그리드는 대규모의 단일한 환경으로서의 그리드가 아닌, 기존의 클러스터 컴퓨터와 같은 의미로 사용되고 있다. 따라서 자신이 구성한 분산 컴퓨팅 환경을 그리드 환경의 한 부분으로 포함시키기 위해서는 컴퓨팅 환경에 대한 정보를 그리드의 이용자가 공유할 수 있어야 하며, 정보 서비스를 공개하여 공유할 수 있도록 해야 한다. 따라서 본 논문에서는 정보의 공유와 확장이 용이하며, 플랫폼에 독립적인 서비스를 제공할 수 있는 정보 시스템을 구축하기 위한 웹 서비스, 온톨로지 기반의 Semantic Information System Framework(WebSIS)을 제안한다. WebSIS를 통하여 온톨로지를 이용하는 정보 시스템의 제작을 용이하게 하며, 온톨로지의 공유와 재사용, 확장의 특징을 활용할 수 있는 구조를 제안한다. 또, 그리드 컴퓨팅 환경이 기존의 분산 컴퓨팅 환경에 비해 복잡해짐에 따라 작업을 수행하기 위한 적절한 자원의 할당에도 좀 더 많은 고려사항이 존재하게 되었고, 이를 효과적으로 처리하기 위해서는 좀 더 고수준의 정보처리를 필요로 하게 되었다. 이에 우리는 WebSIS와 함께 WebSIS를 이용하여 그리드 환경을 이용하는 작업의 자동화를 위한, 온톨로지 기반의 고수준의 정보처리가 가능한 Resource Information Service를 구현하였고, 이를 통하여, 효율적인 공유와 확장을 위한 온톨로지 모델을 제안한다. 또한, 기존의 용어 매칭이 아닌 의미 정보를 이용한 검색과 정보 처리를 제안한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권6호
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• pp.2022-2042
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• 2014

The term "big data" has recently gained widespread attention in the field of information technology (IT). One of the key challenges in making use of big data lies in finding ways to uncover relevant and valuable information. The high volume, velocity, and variety of big data hinder the use of solutions that are available for smaller datasets, which involve the manual interpretation of data. Semantic computing technologies have been proposed as a means of dealing with these issues, and with the advent of linked data in recent years, have become central to mainstream semantic computing. This paper attempts to uncover the state-of-the-art semantics-based approaches and tools that can be leveraged to enrich and enhance today's big data. It presents research on the latest literature, including 61 studies from 2011 to 2014. In addition, it highlights the key challenges that semantic approaches need to address in the near future. For instance, this paper presents cutting-edge approaches to ontology engineering, ontology evolution, searching and filtering relevant information, extracting and reasoning, distributed (web-scale) reasoning, and representing big data. It also makes recommendations that may encourage researchers to more deeply explore the applications of semantic technology, which could improve the processing of big data. The findings of this study contribute to the existing body of basic knowledge on semantics and computational issues related to big data, and may trigger further research on the field. Our analysis shows that there is a need to put more effort into proposing new approaches, and that tools must be created that support researchers and practitioners in realizing the true power of semantic computing and solving the crucial issues of big data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권4호
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• pp.881-902
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• 2024

Fast decision support systems and accurate diagnosis have become significant in the rapidly growing healthcare sector. As the number of disparate medical IoT devices connected to the human body rises, fast and interrelated healthcare data retrieval gets harder and harder. One of the most important requirements for the Healthcare Internet of Things (HIoT) is semantic interoperability. The state-of-the-art HIoT systems have problems with bandwidth and latency. An extension of cloud computing called fog computing not only solves the latency problem but also provides other benefits including resource mobility and on-demand scalability. The recommended approach helps to lower latency and network bandwidth consumption in a system that provides semantic interoperability in healthcare organizations. To evaluate the system's language processing performance, we simulated it in three different contexts. 1. Polysemy resolution system 2. System for hyponymy-hypernymy resolution with polysemy 3. System for resolving polysemy, hypernymy, hyponymy, meronymy, and holonymy. In comparison to the other two systems, the third system has lower latency and network usage. The proposed framework can reduce the computation overhead of heterogeneous healthcare data. The simulation results show that fog computing can reduce delay, network usage, and energy consumption.