• Journal of KIISE
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• v.41 no.9
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• pp.674-685
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• 2014

The Knowledge service system needs to infer a new knowledge from indicated knowledge to provide its effective service. Most of the Knowledge service system is expressed in terms of ontology. The volume of knowledge information in a real world is getting massive, so effective technique for massive data of ontology is drawing attention. This paper is to provide the method to infer massive data-ontology to the extent of RDFS, based on cloud computing environment, and evaluate its capability. RDFS inference suggested in this paper is focused on both the method applying MapReduce based on RDFS meta table, and the method of single use of cloud computing memory without using MapReduce under distributed file computing environment. Therefore, this paper explains basically the inference system structure of each technique, the meta table set-up according to RDFS inference rule, and the algorithm of inference strategy. In order to evaluate suggested method in this paper, we perform experiment with LUBM set which is formal data to evaluate ontology inference and search speed. In case LUBM6000, the RDFS inference technique based on meta table had required 13.75 minutes(inferring 1,042 triples per second) to conduct total inference, whereas the method applying the cloud computing memory had needed 7.24 minutes(inferring 1,979 triples per second) showing its speed twice faster.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2003.11a
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• pp.333-351
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• 2003

90년대 말부터 새로운 지식정보국가의 패러다임으로 세계 각국은 ‘모바일, 브로드밴드, 극소형 컴퓨터, IPv6’의 기술이 창출해내는 컴퓨팅의 실체를 유비쿼터스 컴퓨팅으로 파악하고 각 국의 정부, 기업, 연구소들이 주도권을 잡기 위해 많은 노력을 기울이고 있다. 과거 30여년 동안 국내 기간 시설로 잘 구축된 유. 무선 통신환경은 유비쿼터스 정보통신이 바로 미래의 국가적 운영을 결정 짓는 초석이 될 것이다. 그러나, 유비쿼터스시대의 구현은 기술적인 발전으로 완성되는 것이 아닌 기초, 기반, 요소, 응용 서비스들이 효율적인 구축이 되어야 가능하다. 현째 까지는 IT 기술의 발전에 관한 연구들은 많이 진행되었지만, 시장의 수용과 관련된 사업적 상용화에 대한 연구들은 다소 부족한 것이 현실이다. 본 연구는 유비쿼터스 컴퓨팅 기술 기반과 서비스 IT 기술 개발 분류와는 구별되는 마케팅 /고객 관점에서의 유비쿼터스 컴퓨팅 환경의 재 분류를 시도하였다. 즉, 유비쿼터스 네크워킹 환경에서의 마케팅 환경변화의 단계를 설정하였고, 그에 대한 가상 시나리오 및 예측을 제안했다. 또한, 이렇게 분류한 유비쿼터스 환경이 소비자의 구매의사결정단계 (구매 전, 정보수집, 대안평가, 구매결정, 구매 후 행동)에 어떻게 영향을 미치고있는지에 대한 분석과 사례를 제시하였다. 특히 유비쿼터스 환경이 구매의사결정단계에서 각 단계의 축소/압축의 형태에 대해서 집중 조명하였다. 이를 토대로 유비쿼터스 환경에서 기업의 마케팅 및 고객관리의 변화와 대응에 대해 제언하였다.

• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.147-158
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• 2008

Fuzzy Cognitive Map (FCM) and Bayesian Belief Network (BBN) are two major frameworks for modeling, representing and reasoning about causal knowledge. Despite their extensive use in causal knowledge engineering, there is no reported work which compares their respective roles. This paper aims to fill the gap by providing a qualitative comparison of the two frameworks through a systematic analysis based on some inherent features of the frameworks. We proposed a set of comparison criteria which covers the entire process of causal knowledge engineering, including modeling, representation, and reasoning. These criteria are usability, expressiveness, reasoning capability, formality, and soundness. The results of comparison have revealed some important facts about the characteristics of FCM and BBN, which will help to determine how FCM and BBN should be used, with respect to each other, in causal knowledge engineering.

• Journal of The Korean Association of Information Education
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• v.22 no.2
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• pp.195-203
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• 2018

This thesis is to presented a theoretical fundamental of an assessment criteria available to a conventional curriculum because informatics subject matter education became it. We analyzed Bloom's Knowledge Dimension, Taxonomy that have suggested most general theoretical base in the educational assessment area. Also a programming area which can improve computational thinking can be the most important chapter of the informatics subject matter. Thus this thesis applied Bloom's theory to KAIE's informatics subject matter curriculum made by 2017. And the result of the qualitative research through the expert panel was 14 items, 87% of Conceptual, Procedural Knowledge and 12 items, 75% of Understand, Apply Taxonomy of Bloom's Theories in the 16 items of the curriculum outlines. Applying Bloom Criteria to like these can provide theoretical fundamental of assessment trend, development of assessment tool requested in the conventional education.

• 대한공업교육학회지
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• v.41 no.2
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• pp.89-118
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• 2016

In this study, the model of Turtle Ship, which is evaluated as one of the world's first ironclad ship in battle as well as the traditional scientific and technological heritage in Korea, was combined with the Physical Computing Platform(Arduino and App Inventor) that enables students to learn the basic concepts of IT in an easy and fun way. Thus, this study contrived the Physical Computing Platform-based Turtle Ship model which will make the students of Industrial Specialized High School develop the technological literacy and humanities-based knowledge through flexible education out of stereotype and single subject as well as enhance the potential of creative convergence education. The following is a summary of the main results obtained through this study: First, Arduino-based Main-controller design and making is helpful to learn of the hardware and software knowledge about EEC(Electron Electronics Control) and to confirm the basic characteristics and performance of interaction of Arduino and actuators. Second, The fundamental Instructional environments of abilities such as implementing EEC systems, thinking logically, and problem-solving skills were provided by designing of pattern diagram, designing an actuator circuit and making, the creation of sketches as technical programming and developing of mobile app. Thirdly, This is physical computing platform based Turtle ship model that will enable students to bring up their technological literacy and interest in the cultural heritage.

• The Journal of the Korea Contents Association
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• v.12 no.7
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• pp.463-472
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• 2012

For last decade, the amount of information has been increased rapidly because of the internet and computing technology development, mobile devices and sensors, and social networks like facebook or twitter. People who want to gain important knowledge from database have been frustrated with large database. Many studies for automatic knowledge extracting meaningful knowledge from large database have been fulfilled. In that sense, automatic knowledge extracting with computing technology has been highly significant in information technology field, but still has many challenges to go further. In order to improve the effectives and efficiency of knowledge extracting system, test collection is strongly necessary. In this research, we introduce a test collection for automatic knwoledge extracting. We name the test collection KEEC/KREC(KISTI Entity Extraction Collection/KISTI Relation Extraction Collection) and present the process and guideline for building as well as the features of. The main feature is to tag by experts to guarantee the quality of collection. The experts read documents and tag entities and relation between entities with a tool for tagging. KEEC/KREC is being used for a research to evaluate system performance and will continue to contribute to next researches.

• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.75-80
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• 2018

In this paper, we propose heterogeneous lifelog mining model in health big-data platform. It is an ontology-based mining model for collecting user's lifelog in real-time and providing healthcare services. The proposed method distributes heterogeneous lifelog data and processes it in real time in a cloud computing environment. The knowledge base is reconstructed by an upper ontology method suitable for the environment constructed based on the heterogeneous ontology. The restructured knowledge base generates inference rules using Jena 4.0 inference engines, and provides real-time healthcare services by rule-based inference methods. Lifelog mining constructs an analysis of hidden relationships and a predictive model for time-series bio-signal. This enables real-time healthcare services that realize preventive health services to detect changes in the users' bio-signal by exploring negative or positive correlations that are not included in the relationships or inference rules. The performance evaluation shows that the proposed heterogeneous lifelog mining model method is superior to other models with an accuracy of 0.734, a precision of 0.752.

• Journal of Internet Computing and Services
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• v.14 no.4
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• pp.53-62
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• 2013

In cloud robotics, the model to share information efficiently is still a research challenge. This paper presents an information sharing model for cloud-enabled robots to collaborate and share intelligence. To provide the efficient message dissemination, an adaptive group communication based on multi-agent is proposed. The proposed algorithm uses a weight function for the link nodes to determine the significant links. The performance evaluation showed that the proposed algorithm produced minimal message overhead and was faster to answer queries because of the significant links compared to traditional group communication methods.

• Journal of Digital Contents Society
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• v.15 no.6
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• pp.711-719
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• 2014

Along with the advent of ubiquitous computing environment, it is becoming a part of our common life style that the demands for enjoying the wireless internet using intelligent portable device such as smart phone and iPad, are increasing anytime or anyplace without any restriction of time and place. The recommending service becomes a very important technology which can find exact information to present users, then is easy for customers to reduce their searching effort to find out the items with high purchasability in e-commerce. Traditional mining association rule ignores the difference among the transactions. In order to do that, it is considered the importance of type of merchandise or service and then, we suggest a new recommending service using mining sequential pattern based on weight to reflect frequently changing trends of purchase pattern as time goes by and as often as customers need different merchandises on e-commerce being extremely diverse. To verify improved better performance of proposing system than the previous systems, we carry out the experiments in the same dataset collected in a cosmetic internet shopping mall.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.954-957
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• 2015

본 연구는 컴퓨터시스템을 이용한 맞춤형 정음기반 한국어 학습 지원시스템의 효율적인 모델에 관한 연구 논문으로서, IMS/AICC 국제규격을 준수하는 LCMS와 SCORM 기반의 정음 정보처리로 한국어 교육이 가능한 학습지원시스템에 구축 시스템의 세부 모음들을 정의하였다. 주 내용은 학습자의 학습결과와 학습 습관을 분석 평가하여 자가 주도 식으로 학습할 수 있는 기능을 제공한 것이다. 본 연구는 효율적인 PMS 세부모듈 시스템은 물론, 표준 역량모델관리시스템, 학습자 개별 역량 관리시스템, 역량저장 및 저장소, 커뮤니티를 활용한 역량 모델기반의 지식관리시스템, 교육 수요분석용 e-Survey시스템, 모바일 학습지원서비스 시스템 등이다. 앞으로 클라우드컴퓨팅 기술을 적용한 외국인을 위한 정음기반 한국어 학습지원시스템 구축 표준 모델을 제시하였다.