• Korean Management Science Review
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• v.18 no.1
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• pp.135-145
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• 2001

In recent environment of dynamic management, there is growing recognition that information and knowledge management systems are essential for efficient/effective decision making by CEO. To cope with this situation, we suggest the Data-Miming scheme as a key component of integrated information and knowledge management system. The proposed system measures business performance by considering both VA(Value-Added), which represents stakeholder’s point of view and EVA (Economic Value-Added), which represents shareholder’s point of view. To mine the new information & Knowledge discovery, we applied the improved genetic algorithms that consider predictability, understandability (lucidity) and reasonability factors simultaneously, we use a linear combination model for GAs learning structure. Although this model’s predictability will be more decreased than non-linear model, this model can increase the knowledge’s understandability that is meaning of induced values. Moreover, we introduce a random variable scheme based on normal distribution for initial chromosomes in GAs, so we can expect to increase the knowledge’s reasonability that is degree of expert’s acceptability. the random variable scheme based on normal distribution uses statistical correlation/determination coefficient that is calculated with training data. To demonstrate the performance of the system, we conducted a case study using financial data of Korean automobile industry over 16 years from 1981 to 1996, which is taken from database of KISFAS (Korea Investors Services Financial Analysis System).

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.2
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• pp.139-149
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• 2013

As the importance of technology forecasting while countries and companies manage the R&D project is growing bigger, the methodology of technology forecasting has been diversified. One of the forecasting method is patent analysis. This research proposes quick forecasting process of emerging technology based on keyword approach using text mining. The forecasting process is following: First, the term-document matrix is extracted from patent documents by using text mining. Second, emerging technology keyword are extracted by analyzing the importance of word from utilizing mean values and standard deviation values of the term and the emerging trend of word discovered from time series information of the term. Next, association between terms is measured by using cosine similarity. finally, the keyword of emerging technology is selected in consequence of the synthesized result and we forecast the emerging technology according to the results. The technology forecasting process described in this paper can be applied to developing computerized technology forecasting system integrated with various results of other patent analysis for decision maker of company and country.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.94-100
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• 2010

Data Mining is mainly used for pattern extracting and information discovery from collected data. However previous methods is difficult to reflect changing patterns with time. In this paper, we introduce Streaming Decision Tree(SDT) analyzing data with continuity, large scale, and changed patterns. SDT defines continuity data as blocks and extracts rules using a Decision Tree's learning method. The extracted rules are combined considering time of occurrence, frequency, and contradiction. In experiment, we applied time series data and confirmed resonable result.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.4
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• pp.59-69
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• 2016

We proposed a specialized model of performance measurement to measure the training performance of the trainees in cyber practical training. Cyber security professionals are cultivating their expertise, skills, and competencies through cyber practical training in specialized education and training institutions. The our proposed process of trainee evaluation is consisted of an evaluation component discovery, evaluation item selection, evaluation index catalog, ratings and criteria decision, and calculation formula. The trainee evaluation is consisted of a formative evaluation during the training and an overall evaluation after finished training. Formative evaluation includes progress evaluation and participation evaluation, and overall evaluation includes practice evaluation and learning evaluation. The evaluation is weighted according to the importance of evaluation type. Because it is evaluated actual skills and abilities, competencies are assigned a high weight, while knowledge and attitudes are assigned a low weight. If cyber security trainees are evaluated by the proposed evaluation model, cyber security professionals can be cultivated by each skill and knowledge level and can be deployed by importance of security task.

• Journal of Communications and Networks
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• v.15 no.3
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• pp.301-311
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• 2013

This paper proposes a policy-based quality of service (QoS) management framework for adaptive routing decisions. We present an approach considering interior gateway protocol (IGP) for path discovery mechanisms and QoS-aware policies for configuring the network elements. The integration of the aforementioned modules into this policy-based network management (PBNM) system is demonstrated by conducting experiments in a real environment, the hellenic public administration network SYZEFXIS. These experiments combine different traffic conditioning mechanisms through event detectors, consider IP service level agreement mechanisms that interoperate with the PBNM system and analyze the enforcement of IGP and QoS policies. Finally, validation and measurement tools are used to prove the efficiency of this framework. It is shown that this architecture offers significantly increased performance and learning capabilities, while the PBNM system achieves adaptive QoS routing through automated configuration considering the avoidance of suboptimal routing issues or under-performance conditions of the network entities.

• Human Ecology Research
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• v.56 no.3
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• pp.301-315
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• 2018

This study explores the critical reflection process experienced by home economics preservice teachers during practicum. Data were collected in a critical analysis of class, practicum review, and journals written by sixteen preservice teachers. Text material were composed of 188 transcripts on A4 paper and 36 page of mini-notes. The collected data were analyzed by a thematic coding method in qualitative research and proceeded in the order of three steps of transference, coding, and subject discovery. The emerging themes were: 1) Observing class 2) Practicing class 3) Growth of class practice 4) Reflecting class. First, the observing class was an exploration process through the viewing of daily classes that involved the process of recognizing the classroom situation and various classroom contexts. Second, the practicing class was to strengthen the consideration of the class to form a relationship that could lead to learning in educational situations. Third, the growth of class practice was intended to recognize the orientation of the subject matter with pedagogical content knowledge. Four, the reflecting class was the process of experiencing practice with a continuous understanding of the class, class reflection, and changing the perspective from the current status. There is a part where critical reflection is difficult to be promoted deeply during 4 weeks; however, there was a possibility of a reflection practice that could promote achievement through the experience of a practicing class.

• Korea Information Processing Society Review
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• v.11 no.6
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• pp.56-75
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• 2004

In this article we address some system characteristics and challenging issues in developing Context-aware Middleware for Ubiquitous Computing. The functionalities of a Context-aware Middleware includes gathering context data from hardware/software sensors, reasoning and inferring high-level context data, and disseminating/delivering appropriate context data to interested applications/services. The Middleware should facilitate the query, aggregation, and discovery for the contexts, as well as facilities to specify their privacy policy. Following a formal context model using ontology would enable syntactic and semantic interoperability, and knowledge sharing between different domains. Moddleware should also provide different kinds of context classification mechanical as pluggable modules, including rules written in different types of logic (first order logic, description logic, temporal/spatial logic, fuzzy logic, etc.) as well as machine-learning mechanical (supervised and unsupervised classifiers). Different mechanisms have different power, expressiveness and decidability properties, and system developers can choose the appropriate mechanism that best meets the reasoning requirements of each context. And finally, to promote the context-trigger actions in application level, it is important to provide a uniform and platform-independent interface for applications to express their need for different context data without knowing how that data is acquired. The action could involve adapting to the new environment, notifying the user, communicating with another device to exchange information, or performing any other task.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.481-486
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• 2021

Machine learning algorithms adopt criterion function as a key component to measure the quality of their model derived from data. Cluster analysis also uses this function to rate the clustering result. All the criterion functions have in general certain types of favoritism in producing high quality clusters. These clusters are then described by attributes and their values. Category utility and partition utility play an important role in cluster analysis. These are fully analyzed in this research particularly in terms of how they are related to the favoritism in the final results. In this research, several data sets are selected and analyzed to show how different results are induced from these criterion functions.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.339-345
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• 2022

Outcome-based education (OBE) is a tried-and-true teaching technique based on a set of predetermined goals. Program Educational Objectives (PEOs), Program Outcomes (POs), and Course Outcomes (COs) are the components of OBE. At the end of each year, the Program Outcomes are evaluated, and faculty members can submit many recommended measures which dependent on the relationship between the program outcomes and its courses outcomes to improve the quality of program and hence the overall educational program. When a vast number of courses are considered, bad actions may be proposed, resulting in unwanted and incorrect decisions. In this paper, a recommender system, using collaborative filtering and association rules algorithms, is proposed for predicting the best relationship between the program outcomes and its courses in order to improve the attributes of the graduates. First, a parallel algorithm is used for Collaborative Filtering on Data Model, which is designed to increase the efficiency of processing big data. Then, a parallel similar learning outcomes discovery method based on matrix correlation is proposed by mining association rules. As a case study, the proposed recommender system is applied to the Computer Information Systems program, College of Computer Sciences and Information Technology, Al-Baha University, Saudi Arabia for helping Program Quality Administration improving the quality of program outcomes. The obtained results revealed that the suggested recommender system provides more actions for boosting Graduate Attributes quality.

• Journal of The Korean Association For Science Education
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• v.24 no.2
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• pp.357-374
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• 2004

This paper described the types and characteristics of educational programs in major natural history museums of the world. Data were collected from the websites, annual reports, and written materials of the Natural History Museum at London, Australian Museum at Sydney, Field Museum of Natural History at Chicago, Smithsonian Museum at Washington D.C, Royal Tyrrell Museum at Canada and American Museum of Natural History at New York. As the result of this study, we presented ten types of educational programs, which were moveable museums, workshops, lecture/courses, festival/events, discovery activities, scientific exploration/research projects, field trips, youth curators/internship, loan materials and camp/tours. We also described the examples equivalent to each program type. The characteristics of educational programs provided by museums as informal learning settings were analyzed in terms of their themes, participation levels, connection with exhibition, relation to curriculum, and learning activity levels. Information in this paper will assist science teachers, museum educators and curators: (1) to design and implement various types and contents of educational programs; (2) to use characteristics of educational programs to assess and develop them; (3) to make important contributions to science education that involves the introduction of various scientific aspects and collections to the public, and the use of programs for science learning and teaching coherent to existing curricula.