• Proceedings of the Korean Information Science Society Conference
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• 2006.10a
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• pp.557-562
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• 2006

최근 기업환경에서는 분산되어 있는 정보를 통합하여 정보 공유의 필요성이 증가함에 따라 기존 레거시 시스템간의 협업을 하기 위한 상호 운용이 강조되고 있다. 독립적인 레거시 시스템의 상호 연결을 위해서는 플랫폼 이질성, 의미 이질성 등을 극복할 필요가 있다. 이러한 문제를 해결하기 위해 ISO/IEC 11179에서 진행하고 있는 XMDR을 이용하여 미들웨어를 설계하였다. 설계한 미들웨어를 레거시 시스템에 적용하여 데이터 공유 및 통합의 일관성을 유지할 수 있게 되었다. 하지만 설계된 미들웨어는 각 노드의 자원 상황과 작업 상황에 대한 조정기능이 없기 때문에 정보 활용의 효율성을 보장할 수 없다. 따라서 레거시 시스템을 관리하고 조정하는 방안이 필요하다. 본 논문에서는 정보를 요청하는 요청 에이전트와 정보를 제공하는 정보 에이전트간의 정확한 정보 교환을 할 수 있도록 조정하고, 각 레거시 시스템의 정보 모니터링과 작업 분배 및 로컬 노드의 자원 관리를 담당하는 협력 에이전트를 설계함으로써 통합된 정보를 효율적으로 활용할 수 있도록 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.9
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• pp.1865-1872
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• 2009

This study suggests a method for extending XMDR to integrate and search legacy system. This extension blends MSO(Meta Semantic Ontology) for the management of metadata, ML(Meta Location) for the management of location information, and Topic Map which is the standard language used to represent semantic web. This study refers to it as TMDR(Topic Map MetaData Registry). As an intelligent layer, Topic Map functions like an index. However, if the data frequently changes, the efficiency of Topic Map may drop. To solve this problem, the proposed system represents the relation among metadata, the relation among real data, and the relation between metadata and real data as Topic Map. The represented Topic Map proposes a method to reduce the changing relation among real data caused by the relation among metadata.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.268-269
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• 2015

The current Internet environment, it is accessible by a computer, but also transferred to the IoT(Internet of Things). These data become large. If the data are provided to the application without any adjustment, it is difficult to exert the original performance. In this paper, we propose a method for filtering the data using the MapReduce of big data processing techniques to refine the collected data. We want to address the heterogeneity of the data generated by the sensor by adding a knowledge identification step in MapReduce. We use XMDR for this purpose.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.966-969
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• 2012

This study suggests a query conversion method based on Topic Maps MetaData Registry(TMDR) in order to solve heterogeneity problems distributed in networks and to integrate data efficiently. In order to integrate distributed data, TMDR provides global schema and it solves heterogeneity problem within local data using query conversion method. After analyzing relationship between Meta Schema Ontology(MSO) of eXtended Meta Data Registry(XMDR) and Topic Maps, this method allows integrated access through Meta Location(ML) which manages accessing information of local data. The processing method is to produce a global query for global processing by using TMDR and then to make the produced global query approach to systems distributed through networks so that allows integrated access at the end. For this, we propose a method to convert a global query into a query which is adaptive to local query.

• International Journal of Internet, Broadcasting and Communication
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• v.8 no.1
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• pp.83-90
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• 2016

The emergence of cloud has been able to provide a variety of IT service to the user. As organizations and companies are increased that provide these cloud service, many problems arises on integration. However, with the advent of latest technologies such as big data, document-oriented database, and MapReduce, this problem can be easily solved. This paper is intended to design the Cloud Aggregator to provide them as a service to collect information of the cloud system providing each service. To do this, we use the DBaaS(DataBase as a Service) and MapReduce techniques. This makes it possible to maintain the functionality of existing system and correct the problem that may occur depending on the combination.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.52-59
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• 2021

In this paper, we propose a system that allows small business owners focusing on the restaurant business to easily understand the management situation, and to manage the operation and management centering on the cost of food materials and profits and losses. In general, the metadata structure is different depending on the POS system, so it is necessary to first develop a standardized metadata model for a food material cost management system for small business owners in various industries. For that reason, the system proposed in this paper was applied to the cost management app by referring to the development of a data model using the metadata standard. In addition, in order to implement a cost profit/loss management system for small business owners in the restaurant industry, it was designed to support standardized metadata models from various types of POS systems, and is a hybrid app that can support a smart environment. Interface) was configured.

• Journal of Internet Computing and Services
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• v.16 no.3
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• pp.43-57
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• 2015

In this thesis was conducted to propose a method for efficient information retrieval using concept lattices. Since this thesis designed a new system based on ordinary concept lattices, it has the same approach method as ontology, but this thesis proposes new concept lattices to be used by establishing collaborative relations between objects and concepts that users are likely to search information more efficiently. The system suggested by this thesis can be summarized as below. Firstly, this system leads to a collaborative search by using Three kinds of concepts, such as keyword concept lattices, which focus on input key words, expert concept lattices recommended by experts and theme concept lattices, and based on these 3 concept lattices, it will help users search information they want more efficiently. Besides, as the expert concept and the keyword concept become combined, further providing users with the frequency of keyword and the frequency of category, this system can function to recommend key words related to search words entered by users. Another function of this system is to inform users of key words and categories used in users' interested themes by using the theme concept lattices. Secondly, when there is not keyword entered by a user, it is possible for users to achieve the goal of search through the secondary search when this system provides them with key words related to the input keyword. Thirdly, since most of the information is managed while being dispersed, such dispersed and managed information not only has different expression methods but changes as time goes. Accordingly, By using XMDR for efficient data access and integration of distributed information, this thesis proposes a new technique and retrieval system to integrate dispersed data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.1
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• pp.183-190
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• 2012

This study constructs an expert search system which has a mutual cooperation function based on thesis and author profile. The proposed methodology is as follows. First, we propose weighting method which can search a keyword and the most relevant keyword. Second, we propose a method which can search the experts efficiently with this weighting method. On the preferential basis, keywords and author profiles are extracted from the papers, and experts can be searched through this method. This system will be available to many fields of social network. However, this information is distributed to many systems. We propose a method using multi-ontology to integrate distributed data. The multi-ontology is composed of meta ontology, instance ontology, location ontology and association ontology. The association ontology is constructed through analysis of keyword association dynamically. An expert network is constructed using this multi-ontology, and this expert network can search expert through association trace of keyword. The expert network can check the detail area of expertise through the research list which is provided by the system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.970-973
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• 2012

In cloud environment, enterprises use a number of systems which are not interconnected and save master information in a distributed way in each of them. Master information which is not managed is incorrect and discord each other so that lowers efficiency of business process and disables optimum decision making. It is necessary to do high-qualified management of master information to operate efficient and errorless business process. This paper suggests TMDM as a solution to solve heterogeneous problems occurred between interconnected master information in cloud environment and to manage business process in an efficient method. TMDM is an information storage that is suggested to solve mutual discord problems between master information using Topic Maps that considers correlation between data. Topic Maps can be connected by association between topics in order to access through a topic to all related knowledge information which is described by the topic. This also can be applied to master information between legacy systems within cloud.