• Journal of Korea Society of Industrial Information Systems
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• v.6 no.3
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• pp.51-64
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• 2001

Schema integration is one of the most difficult issues in the heterogeneous and distributed geographic database systems (GDSs). As the use of spatial information in various application areas becomes increasingly popular, the integration of geographic information has become a crucial task for decision makers. Most existing schema integration techniques described in the database literature, however, do not address the problems of managing heterogeneities among complex objects that contain visual data and/or spatial and temporal information. The difficulties arise not only from the semantic conflicts, but also from the different representations of spatial models. Consequently, it is much more complex to achieve interoperability in the area of geographic databases. This research attempts to provide a solution to such problems. The research reported in this paper describes a schema integration methodology and a prototype toolkit developed to assist in schema integration activities for GDSs.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.1-19
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• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.221-224
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• 2014

IoT(Internet of Things)기술은 현재 핵심 IT 트렌드 중 하나로서 여러 분야에서 앞으로도 지속적인 발전을 통해 수많은 기기가 연결 되어 빠르게 사물인터넷 시대에 도래하게 될 것이다. 이를 위해 세계 각국에서도 활발한 연구를 진행 중에 있는 한편, 아직까지 서비스 제공을 위한 개방형 통일 표준이 확립되지는 않아 이질성의 문제가 발생하게 된다. 따라서 본 논문에서는 IoT 여러 분야 중 홈 네트워크에서 사용되는 기기가 IoT화 된다고 가정하고 먼저 구현되어야 할 기본적인 공통 명령 및 데이터타입을 SNMP 프로토콜의 SMI 기반으로 설계하여 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.949-952
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• 2011

현재 IPv4 에서 IPv6 로의 전환은 매우 시급한 상태이다. 하지만 IPv6 로의 전환에 앞서 IPv4 에서 제기되었던 많은 보안 문제점이 IPv6 로의 전환에 걸림돌이 되고 있다. 차세대 인터넷 구축에 반드시 필요한 IPv6로의 변환 과정에 있어, 기존의 IPv4 와 IPv6 의 서로 다른 방식으로 인한 이질성으로 예상치 못한 보안상 문제점들이 드러나고 있다. 본 논문에서는 IPv4 에서 IPv6 로 전환 시 발생할 수 있는 보안상 위협에 대해서 분석한다. 또한 터널링 방법에서의 패킷 헤더 변조 공격을 방지하기 위해 패킷 무결성 검증에 의한 패킷 필터링 방법과 IPv4 주소 할당 방법에 있어 주소 할당 서버의 IP pool 주소 고갈 공격 문제를 해결하기 위한 방안을 제시 하였다.

• The KIPS Transactions:PartD
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• v.8D no.5
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• pp.473-482
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• 2001

Integrated heterogeneous database systems provide the unified interface for users and applications today in order to access the underlying diverse data sources located in different sites. The multiple heterogeneous data sources have the different and specialized data structures and transaction processing capabilities. Because of local autonomy, the local system does not have the capability of cooperation to control the global transaction. Hence designing the global transaction manager with supporting the global serializability is difficult task. To resolve the well-known indirect conflict, we define the global transaction model by using the characteristics of global integrity constraints. And then we propose the site-locking operation and its protocol to manage the global transaction. The correctness and analysis of our site-locking protocol is proved and performance gain over the related other methods is also estimated in this paper.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.185-188
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• 2010

온톨로지 간 이질성 문제를 해결하고 상호운용성을 향상시키기 위한 연구가 진행되어 왔으며, 최근 XMDR에 기반한 온톨로지 구축 방법이 제안되었으나 기존 연구와의 비교 평가가 부족하여 장점을 정확하게 보이지 못하였다. 따라서 이 논문에서는 XMDR 기반 온톨로지 구축 방법의 장점을 보다 명확하게 보이기 위해 정량적인 평가를 수행한다. 이를 위해 실제 온톨로지를 구축하고, 구축된 온톨로지는 온톨로지 참조 기반 온톨로지 구축 방법, 사전 참조 기반 온톨로지 구축 방법, 기존 방법론을 이용한 온톨로지 구축 방법을 평가 대상으로 하여 5가지 평가 지표로 분석된다. 평가 지표로는 구축된 온톨로지의 어휘 및 구조의 일관성 비교를 위하여 어휘 및 구조의 빈도수 평균과 엔트로피를 사용하고 구축 비용의 평가를 위하여 각 온톨로지의 구축 시간을 사용한다. 이러한 실험 및 평가의 결과로써, 온톨로지 참조 기반의 온톨로지 구축 방법은 다른 온톨로지 구축 방법들에 비해 온톨로지 어휘 및 구조가 일관적이고 효율적임을 보인다.

• Journal of Advanced Navigation Technology
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• v.12 no.1
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• pp.46-53
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• 2008

The ITS(Intelligent Transport System) enables us to provide solutions on traffic problems, while maximizing safety and efficiency of road and transportation systems, by combining technologies from information and communication, electrical engineering, electronics, mechanics, control and instrumentation with transportation systems. The issues that an integration system for massive traffic data sources must face are due to several factors such as the variety and amount of data available, the representational heterogeneity of the data in the different sources, and the autonomy and differing capabilities of the sources. In this paper, we describe how to extract and load of the heterogeneous massive traffic data from the operational databases, such as FTMS and ARTIS using commercial tools. Also, we experiment on traffic data warehouses with integrated quality management techniques for providing high quality data.

• Proceedings of the Korean Society of Computer Information Conference
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• 2009.01a
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• pp.19-24
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• 2009

이 논문에서는 메타데이터 레지스트리 (MDR, Metadata Registry) 기반의 웹 온톨로지 생성모델을 제안한다. 메타데이터 레지스트리는 국제 표준(ISO/IEC 11179)으로서 데이터베이스간 상호운용성 향상을 위해 개발되었다. 그러나 데이터 표현과 상호운용성을 위한 컴퓨팅 환경의 변화는 메타데이터 레지스트리의 확장은 물론 메타데이터 레지스트리의 활용 방법의 변화를 요구한다. 이 논문에서의 웹 환경의 변화란 정적인 웹 환경에서 웹 2.0 혹은 시맨틱 웹 이라고 정의하는 차세대 웹 환경으로의 변화를 의미한다. 이러한 환경을 위해서 다양한 기술 개발과 적용 기법에 관한 연구가 필요하다. 특히 차세대 웹을 위해서는 지원에 대한 명확한 의미 정의 및 활용이 요구된다. 이는 웹 온톨로지 스키마를 구성하는 개념들에 대한 보다 일관성 있는 정의 및 사용이 필요하다. 이러한 문제가 해결되지 않을 경우, 또 다시 온톨로지를 구성하는 개념들 간 이질성 문제를 야기한다. 메타데이터 레지스트리는 다양한 표준화 된 개념들을 포함하며, 응용을 위한 데이터를 위한 의미 또한 이 개념들을 이용하여 정의한다. 따라서 이러한 표준 요소를 이용한 웹 온톨로지 스키마 정의 및 활용이 요구되며, 이 논문에서 이와 관련된 기본 개념, 요구 사항을 장의하고 전체적인 모델을 제안한다.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.3
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• pp.251-260
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• 2002

The parallel/distributed processing with a lot of the idle hosts on the web has the high coot-performance ratio for large-scale applications. It's processing has to show the solutions for unpredictable status such as heterogeneity of hosts, variability of hosts, autonomy of hosts, the supporting performance continuously, and the number of hosts which are participated in computation and so on. In this paper, we propose the strategy of adaptive tack reallocation based on performance the host job processing, spread out geographically Also, It shows the scheme of dynamic host management with dynamic environment, which is changed by lots of hosts on the web during parallel processing for large-scale applications. This paper implements the PDSWeb (Parallel/Distributed Scheme on Web) system, evaluates and applies It to the generation of rendering image with highly intensive computation. The results are showed that the adaptive task reallocation with the variation of hosts has been increased up to maximum 90% and the improvement in performance according to add/delete of hosts.

• The Journal of the Korea Contents Association
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• v.22 no.9
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• pp.283-297
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• 2022

This study analyzed to strategy and structure of outward foreign direct investment(OFDI) by Chinese Enterprises in 'one belt, one load' countries along the line from the perspective of Dunning's OLI paradigm. Chinese enterprises' investment in 'one belt, one road' countries was largely promoted for two strategic purposes. One is an investment to secure energy resources due to the nature of resource holdings in 'one belt, one road' countries, and the other is a transfer investment to solve the problem of surplus facilities, a problem in China's domestic economy. Chinese enterprises' investments in these 'one belt, one road' countries is evaluated to have been made with Dunning's investment decision conditions in the OLI paradigm, namely, Ownership specific advantages, Location specific advantages, and Internalization specific advantages. only if there is a difference, investment country, investment method, and investment industry are different due to the structure of international relations, religious conflict and cultural heterogeneity, institutional investment environment of the region, and awareness of Chinese enterprises.