• Journal of information and communication convergence engineering
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• v.9 no.3
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• pp.287-294
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• 2011

This paper proposes a search system using the topic maps that it extends XMDR into Knowledge based XMDR for solving of the problems of the heterogeneity of distributed data on a network and integrate data by an efficient way. The proposed system combined Topic Maps and the extended metadata registry effectively. The Topic Maps represent related knowledge and reasoning relationship by associations of topic. And the extended metadata registry standards and manages the metadata of the local systems through registration and certification on the distributed environment. We also proposed a meta layer, include the meta topic and meta association to achieve semantic classification grouping of topics and to define relationship between Topic Maps and extended metadata registry.

• ETRI Journal
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• v.20 no.2
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• pp.192-213
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• 1998

The current approaches in Object-Oriented Analysis have limitations on modeling complex real world systems because they require priori knowledge about objects and their interactions before applying them. This may be practical in small systems and systems with clear domain knowledge, but not in large real world systems with unclear domain knowledge. Our approach uses a stepwise refinement technique in a top-down manner to the Object-Oriented Analysis stage with the application of use cases. This approach is especially good for new areas where we do not know all the information in advance. We present the approach with an example of its application to the B-ISDN service modeling and distributed systems.

• Journal of the Korea Institute of Building Construction
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• v.7 no.2 s.24
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• pp.107-112
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• 2007

Recently, a lot of domestic construction companies have implemented Knowledge Management System. However, most of the knowledge in the construction fields still cannot written down on papers, and it is just locked in people's heads. The purpose of this study was to analyze the current situation of knowledge sharing in the construction fields. For this research, questionnaires were distributed and the results of this study was as follows; the essential factor of successful knowledge sharing is not introducing a high quality of electronic system but developing culture of knowledge sharing. When positive culture of knowledge sharing is combined with technical infrastructure, knowledge sharing will be actively promoted.

• Journal of the Korean Society for information Management
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• v.25 no.1
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• pp.5-17
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• 2008

The purpose of this paper is to identify the usefulness of Weblog genre analysis in knowledge creation within an organization where communications are occurred frequently among the employees with information and communication technologies (ICTs). Knowledge creation is essential to achieve competitive advantage in today's knowledge-oriented working environments. There has been huge investment on knowledge management systems to achieve such advantages. It is, however, widely recognized that distributed knowledge management systems often fail due to the different social contexts across the sub-organizations where the local information systems are deployed. It is important to coordinate such social gaps across the sub-organizations to achieve better advantages. Given that Weblogs users often feel a shared social norm. Weblogs playa positive role of narrowing the social gaps. This paper argues that the genre analysis of Web logs could provide important clues to narrow the social gaps existed across the sub-organizations within an organization. Hence, such taxonomical practice may be a solution for the high rate of failure in knowledge management system implementations in an organization. This paper uses the theory of organizational knowledge creation (Nonaka & Takeuchi. 1995) and social construction of technology (SCOT) approach (Bijker, 1995) to compare the socialized with the non-socialized integration of distributed information systems. The findings of this research provide a useful framework for better implementing knowledge management practices especially in distributed working environments.

• Knowledge Management Research
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• v.12 no.1
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• pp.111-121
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• 2011

Multinational Corporations (MNCs) are establishing globally-distributed R&D centers around the world. Firms can overcome resource constraints and achieve superior innovative performance not only by using internal resources but also acquiring knowledge from oversea R&D centers. This paper explores the key factors that have been cited as significant influences on the ability to transfer knowledge, an important area of knowledge management of MNCs. Also, we identify the emerging outcomes in terms of R&D transfer and for the education, employment, and retention of knowledge works in MNCs. The result will be helpful for both MNC's managers and governments' decision makers with respect to R&D centers.

• Asia pacific journal of information systems
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• v.13 no.3
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• pp.243-259
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• 2003

Many R&D organizations have interests on knowledge management system(KMS) to organize and utilize their knowledge resources. Currently, for research collaboration and knowledge management R&D organizations use either a specialized knowledge management system or a set of general application systems such as basic messaging system and document management system. The objectives of this paper are to identify important functionalities of knowledge management systems based on team characteristics and knowledge process of research teams in research organizations and to provide implications to design and implement knowledge management system for R&D teams. Survey results show that research teams perceive communication, collaboration and connection functionalities are important when their team sizes are large or they are distributed. During knowledge capture process, they need personalization of knowledge to reduce information overload.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.115-117
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• 2004

Recently, mining far association rules in distributed database environments is a central problem in knowledge discovery area. While the data are located in different share-nothing machines, and each data site grows by time. Mining global frequent itemsets is hard and not efficient in large number of distributed sewen. In many distributed databases. time component(which is usually attached to transactions in database), contains meaningful time-related rules. In this paper, we design a new DTA(distributed temporal association) algorithm that combines temporal concepts inside distributed association rules. The algorithm confirms the time interval for applying association rules in distributed databases. The experiment results show that DTA can generate interesting correlation frequent itemsets related with time periods.

• Journal of KIISE
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• v.42 no.10
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• pp.1195-1206
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• 2015

For the management of a knowledge system, systems that automatically infer and manage scalable knowledge are required. Most of these systems use ontologies in order to exchange knowledge between machines and infer new knowledge. Therefore, approaches are needed that infer new knowledge for scalable ontology. In this paper, we propose an approach to perform rule based reasoning for scalable SHIF ontologies in a spark framework which works similarly to MapReduce in distributed memories on a cluster. For performing efficient reasoning in distributed memories, we focus on three areas. First, we define a data structure for splitting scalable ontology triples into small sets according to each reasoning rule and loading these triple sets in distributed memories. Second, a rule execution order and iteration conditions based on dependencies and correlations among the SHIF rules are defined. Finally, we explain the operations that are adapted to execute the rules, and these operations are based on reasoning algorithms. In order to evaluate the suggested methods in this paper, we perform an experiment with WebPie, which is a representative ontology reasoner based on a cluster using the LUBM set, which is formal data used to evaluate ontology inference and search speed. Consequently, the proposed approach shows that the throughput is improved by 28,400% (157k/sec) from WebPie(553/sec) with LUBM.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.3
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• pp.1-27
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• 2009

Since online knowledge networks usually consist of a larger, loosely knit, and geographically distributed group of "strangers" who may not know each other very well, members may not willingly share their knowledge with others. In order to address this challenge, this study looks Into the factors that are expected to affect knowledge sharing in an online knowledge network. For empirical validation, we choose "the global network of Korean scientists and engineers (KOSEN)" as one of the best practices of online knowledge networks. By using the archival, network, and survey data, we validate two models of knowledge sharing in sequence (i.e., knowledge contribution and knowledge utilization models) and then discuss the results. The findings of this study show that individuals not only contribute but also utilize knowledge in an online knowledge network when they are structurally embedded and perceive a strong reciprocity. In the network. In addition, taking pleasure in helping is found to positively affect knowledge contribution, whereas perceiving usefulness is found to Influence knowledge utilization. Contributions of this study and future research opportunities are also discussed.

• The Journal of Korean Institute of Information Technology
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• v.17 no.7
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• pp.31-45
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• 2019

The core technologies for intelligent services today are deep learning, that is neural networks, and parallel and distributed processing technologies such as GPU parallel computing and big data. However, for intelligent services and knowledge sharing services through globally shared ontologies in the future, there is a technology that is better than the neural networks for representing and reasoning knowledge. It is a knowledge representation of IF-THEN in RIF or SWRL, which is the standard rule language of the Semantic Web, and can be inferred efficiently using the rete algorithm. However, when the number of rules processed by the rete algorithm running on a single computer is 100,000, its performance becomes very poor with several tens of minutes, and there is an obvious limitation. Therefore, in this paper, we analyze the past and current studies on parallel and distributed processing of rete algorithm, and examine what aspects should be considered to implement an efficient rete algorithm.