• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.7
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• pp.1701-1707
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• 2010

This study is intended for implementing the system environment that can help integrate and retrieve various types of data in real-time by providing semantic interoperability among distributed heterogeneous information systems. The semantic interoperability is made possible by providing a TMDR(Topicmaps Metadata Registry), a set of ontologies. TMDR, which has been made by combining MDR(MetaData Registry) and TopicMaps and storing them in the database, is able to generate distributed query and provide efficient knowledge. MDR is a metadata management technique for distributed data management. TopicMaps is an ontology representation technique that takes into consideration the hierarchy and association for accessing knowledge data. We have created TMDR, a kind of ontology, that is fit for any system and able to detect and resolve semantic conflicts on the level of data and schema. With this system we propose a query-processing technique to integrate and access heterogeneous information sources. Unlike existing retrieval methods this makes possible efficient retrieval and reasoning by providing association focusing on subjects.

• Journal of Intelligence and Information Systems
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• v.13 no.3
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• pp.141-160
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• 2007

User interface has been regarded as a crucial issue to increase the acceptance of mobile services. In special, even though to what extent the machine as speaker communicates with human as listener in a timely and polite manner is important, fundamental studies to come up with these issues have been very rare. Hence, the purpose of this paper is to propose a methodology of estimating politeness level in a certain context-aware setting and then to design a context-aware system for polite mobile interface. We will focus on Korean language for the polite level estimation simply because the polite interface would highly depend on cultural and linguistic characteristics. Nested Minkowski aggregation model, which amends Minkowski aggregation model, is adopted as a privacy-preserving similarity evaluation for case retrieval under distributed computing environment such as ubiquitous computing environment. To show the feasibility of the methodology proposed in this paper, simulation-based experiment with drama cases has performed to show the performance of the methodology proposed in this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.6
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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.

• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.137-146
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• 2008

Fault localization plays a significant role in enormous distributed system because it can identify root cause of observed faults automatically, supporting self-managing which remains an open topic in managing and controlling complex distributed systems to improve system reliability. Although many Artificial Intelligent techniques have been introduced in support of fault localization in recent research especially in increasing complex ubiquitous environment, the provided functions such as diagnosis and prediction are limited. In this paper, we propose fault localization for self-managing in performance evaluation in order to improve system reliability via learning and analyzing real-time streams of system performance events. We use probabilistic reasoning functions based on the basic Bayes' rule to provide effective mechanism for managing and evaluating system performance parameters automatically, and hence the system reliability is improved. Moreover, due to large number of considered factors in diverse and complex fault reasoning domains, we develop an efficient method which extracts relevant parameters having high relationships with observing problems and ranks them orderly. The selected node ordering lists will be used in network modeling, and hence improving learning efficiency. Using the approach enables us to diagnose the most probable causal factor with responsibility for the underlying performance problems and predict system situation to avoid potential abnormities via posting treatments or pretreatments respectively. The experimental application of system performance analysis by using the proposed approach and various estimations on efficiency and accuracy show that the availability of the proposed approach in performance evaluation domain is optimistic.

• 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.

• Journal of KIISE
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• v.42 no.8
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• pp.998-1009
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• 2015

In recent years, there has been a growing interest in RDFS Inference to build a rich knowledge base. However, it is difficult to improve the inference performance with large data by using a single machine. Therefore, researchers are investigating the development of a RDFS inference engine for a distributed computing environment. However, the existing inference engines cannot process data in real-time, are difficult to implement, and are vulnerable to repetitive tasks. In order to overcome these problems, we propose a method to construct an in-memory distributed inference engine that uses a parallel graph structure. In general, the ontology based on a triple structure possesses a graph structure. Thus, it is intuitive to design a graph structure-based inference engine. Moreover, the RDFS inference rule can be implemented by utilizing the operator of the graph structure, and we can thus design the inference engine according to the graph structure, and not the structure of the data table. In this study, we evaluate the proposed inference engine by using the LUBM1000 and LUBM3000 data to test the speed of the inference. The results of our experiment indicate that the proposed in-memory distributed inference engine achieved a performance of about 10 times faster than an in-storage inference engine.

• Journal of The Korean Association For Science Education
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• v.15 no.1
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• pp.27-38
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• 1995

The purposes of the 'Students' Science Inquiry Experiment Tournament' which is one of the 'Students' Science Inquiry Olympic Tournaments' are; 1) cultivate students' intellectual interests, inquiry abilities, and scientific attitude dealing with students' scientific reasoning abilities, problem solving abilities, and experimental apparatuses operation abilities. 2) contribute substantiality of science education through experimental inquiry learning. 3) make the ground of basic science development of the future society by selecting excellent students who have talents for science. 4) elevate science teachers' morale by this tournament. The test items set and evaluation results of the tournament were analysed in this study. The results of this study were ; 1) the discrimination ability of the paper-and pencils test and the experiments were low because the students' scores of the items were not normally distributed and standard deviations were very small values. 2) most of the tournament participation students did not answered to the subjective type test items. 3) according to the responses of the tournament participation students, the tournament contribute to the students' interests in science. But the opinion was dominant that the tournament didn't contribute to school science education improvement.

• Journal of Communications and Networks
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• v.13 no.6
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• pp.567-582
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• 2011

The growing popularity of the Internet has caused the size and complexity of communications and computing systems to greatly increase in recent years. To alleviate this increased management complexity, novel autonomic management architectures have emerged, in which many automated components manage the network's resources in a distributed fashion. However, in order to achieve effective collaboration between these management components, they need to be able to efficiently exchange information in a timely fashion. In this article, we propose a context dissemination framework that addresses this problem. To achieve scalability, the management components are structured in a hierarchy. The framework facilitates the aggregation and translation of information as it is propagated through the hierarchy. Additionally, by way of semantics, context is filtered based on meaning and is disseminated intelligently according to dynamically changing context requirements. This significantly reduces the exchange of superfluous context and thus further increases scalability. The large size of modern federated cloud computing infrastructures, makes the presented context dissemination framework ideally suited to improve their management efficiency and scalability. The specific context requirements for the management of a cloud data center are identified, and our context dissemination approach is applied to it. Additionally, an extensive evaluation of the framework in a large-scale cloud data center scenario was performed in order to characterize the benefits of our approach, in terms of scalability and reasoning time.

• Journal of Distribution Science
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• v.15 no.3
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• pp.27-37
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• 2017

Purpose - The current study analyzes the effect of business startup education on business startup education satisfaction and entrepreneurial intention. Also, it attempts to discover if business startup confidence plays a moderating role between business startup education satisfaction and entrepreneurial intention. Research design, data, and methodology - The survey was conducted for approximately 60 days, from July 20, 2016 to September 20, 2016, and distributed to 300 restaurant founders who started their business through business startup education provided by Korea Food Service Industry Association in Seoul, Kyungki and Incheon or existing business founders. Results - Per multiple regression analysis of business startup education and business startup education satisfaction, among business startup education, all law, entrepreneurship, commercial power analysis, and practical education have significantly positive effect. Per simple regression analysis of business startup education satisfaction and entrepreneurial intention, business startup education has significantly positive effects on entrepreneurial intention. Business startup education satisfaction and business startup confidence interaction show that they do not have a moderating role between business startup education satisfaction and entrepreneurial intention. Conclusions - Setting up theoretical reasoning, this study supports conclusions drawn by prior studies: business startup education has significantly positive effects on business startup education satisfaction and entrepreneurial intention.

• Journal of the Korean Institute of Gas
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• v.8 no.2 s.23
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• pp.28-34
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• 2004

Enhanced methodologies for process diagnosis and abnormal situation management have been developed for the last two decades. However, there is no single method that always shows better performance over all kinds of diagnostic problems. In this paper, a framework of message-passing, cooperative, intelligent diagnostic agents is presented for improved on-line fault diagnosis through cooperative problem solving of different expertise. A group of diagnostic agents in charge of different process functional perform local diagnoses in parallel; exchange related information with other diagnostic agents; and cooperatively solve the global diagnostic problem of the whole process plant or business units just like human experts would do. For their better understanding, sharing and exchanging of process knowledge and information, we also suggest a way of remodeling processes and protocols, taking into account semantic abstracts of process information and data. The benefits of the suggested multi-agents-based approach are demonstrated by the implementations for solving the diagnostic problems of various chemical processes.