• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.850-853
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• 2002

This paper described some applications using Open GIS Component that was called MapBase. There were 4 applications : KSDI(Korea Spatial Data Infrastructure) funded by MOIC(Ministry of Information and Communication), National Plants Resource Management System supported by Korea Forest Service, 7-Underground Facilities Management System of Cheongju funded by MOCT(Ministry of Construction and Transportation), and National Disaster Management System supported by MOGAHA(Ministry of Government Administration and Home Affairs. Because they wanted to access heterogeneous spatial database, it was necessary to select MapBase as their base methodology. The main feature of MapBase was component S/W which provided the interoperability and reusability among GIS applications as well as non-GIS information system through common specification. In this paper, we showed some applications' architectures and functions to increase understanding MapBase. That would help you to make application using MapBase.

• Journal of The Korean Association For Science Education
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• v.27 no.8
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• pp.724-742
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• 2007

The purpose of this study was to develop a scientific inquiry model that makes scientific inquiry accessible to science teachers as well as students. To develop a scientific inquiry model, we investigated the research process demonstrated by ten scientists who were working at academic research institutions or industrial research institutions. We collected data through scientists' journal articles, lab meetings and seminars, and observation of their inquiry process. After we analyzed the scientists' inquiry strategies and processes of inquiry, we finally developed the Scientist's Methodology of Investigation Process model named SMIP. The SMIP model consists of four domains, 15 stages, and link questions, such as "if, why", and "how". The SMIP model stressed that inquiry process is a selective process rather than a linear or a circular process. Overall, these findings can have implication science educators in their attempt to design instruction to improve the scientific inquiry process.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.87-99
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• 2013

Massive increases in data available to an organization are creating a new opportunity for competitive advantage. In this era of big data, developing analytics capabilities, therefore, becomes critical to take advantage of internal and external data and gain insights for data-driven decision making. However, the use of data in education is in its infancy, in comparison with business and government, and the potential for data analytics to impact education services is growing. In this paper, I survey how universities are currently using education data to improve students' performance and administrative efficiency, and propose new ways of extending the current use. In addition, with the so-called data scientist shortage, universities should be able to train professionals with data analytics skills. This paper discusses which skills are valuable to data scientists and introduces various training and certification programs offered by universities and industry. I finally conclude the paper by exploring new curriculums where students, by themselves, can learn how to find and use relevant data even in any courses.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.151-159
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• 2019

As the era of big data, which is the foundation of the fourth industry, has come, most related industries are developing related solutions focusing on the technologies of data storage, statistical analysis and visualization. However, for the diffusion of bigdata technology, it is necessary to develop the prediction analysis technologies using artificial intelligence. But these advanced technologies are only possible by some experts now called data scientists. For big data-related industries to develop, a non-expert, called a citizen data scientist, should be able to easily access the big data analysis process at low cost because they have insight into their own data. In this paper, we propose a system for analyzing bigdata and building business models with the support of easy-to-use analysis system without knowledge of high-level data science. We also define the necessary components and environment for the prediction analysis system and present the overall service plan.

• Genomics & Informatics
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• v.18 no.1
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• pp.7.1-7.7
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• 2020

In this paper, we present few technical notes about the distance distribution paradigm for Mosaab-metric using 1, 2, and 3 grams feature extraction techniques to analyze composite data points in high dimensional feature spaces. This technical analysis will help the specialist in bioinformatics and biotechnology to deeply explore the biodiversity of influenza virus genome as a composite data point. Various technical examples are presented in this paper, in addition, the integrated statistical learning pipeline to process segmented genomes of influenza virus is illustrated as sequential-parallel computational pipeline.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.2
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• pp.59-67
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• 2010

Rice varietal information exchange is vital for agricultural experiments and trials. With the growing size of rice data gathered around the world, and numerous research and development achievements, the effective collection and convenient ways of data dissemination is an important aspect to be dealt with. The collection of this data is continuously worked out through various international cooperation and network programs. The problem in acquiring this information anytime anywhere is the new challenge faced by rice breeders, scientist and crop information specialists, in order to perform rapid analysis and obtain significant results in rice research, thus alleviating rice production. To address these constraints, we propose an Online Analytical Query System, a web query application to provide breeders and rice scientist around the world a fast web search engine for rice varieties, giving the users the freedom to choose from which trial it has been used, trait observation parameters as well as geographical or weather conditions, and location specifications. The application uses data warehouse techniques and OLAP for summarization of agricultural trials conducted, and statistical analysis in deriving outstanding varieties used in these trials, consolidated in an Model-View-Controller Web framework.

• Journal of The Korean Association For Science Education
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• v.28 no.5
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• pp.453-463
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• 2008

This study investigated the students' perceptions of environmental scientists and their work and the factors influencing the students' images of them by using the Draw-An-Environmental-Scientist-Test (DAEST). The DAEST was administered to 413 students in 10th and 11th grades selected from three high schools in Seoul. The results revealed that the students' images of environmental scientists were different from the stereotypical images of scientists. In the students' drawings, it was difficult to distinguish the gender and age of environmental scientists. Most students also perceived environmental scientists collecting data on environmental pollution by using experimental equipments and a laptop computer in the field. The students answered that the factors affecting their images of environmental scientists were mass media, school education, internet, and so on. According to the students' grade and gender, there were differences in the perceptions of environmental scientists and their work, and there were factors that influenced their images. Educational implications of these findings are discussed.

• Genomics & Informatics
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• v.18 no.1
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• pp.2.1-2.11
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• 2020

In this paper, we propose a new approach to detecting outliers in a set of segmented genomes of the flu virus, a data set with a heterogeneous set of sequences. The approach has the following computational phases: feature extraction, which is a mapping into feature space, alignment-free distance measure to measure the distance between any two segmented genomes, and a mapping into distance space to analyze a quantum of distance values. The approach is implemented using supervised and unsupervised learning modes. The experiments show robustness in detecting outliers of the segmented genome of the flu virus.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.23-43
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• 2022

In order to apply AI (Artificial Intelligence) in various industries, interest in algorithm selection is increasing. Algorithm selection is largely determined by the experience of a data scientist. However, in the case of an inexperienced data scientist, an algorithm is selected through meta-learning based on dataset characteristics. However, since the selection process is a black box, it was not possible to know on what basis the existing algorithm recommendation was derived. Accordingly, this study uses k-means cluster analysis to classify types according to data set characteristics, and to explore suitable classification algorithms and methods for resolving class imbalance. As a result of this study, four types were derived, and an appropriate class imbalance resolution method and classification algorithm were recommended according to the data set type.

• Journal of Korean Elementary Science Education
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• v.33 no.4
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• pp.655-673
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• 2014

We investigated perceptions and image analysis on scientists studying small organisms reflected in elementary student's drawing using a modified version of the Drawing-A-Scientist-Test. The participants were 530 of fifth and sixth graders consisted of 449 ordinary students and 81 science gifted students. The data were collected from associated words, images and explanatory notes depicted by students engaged in questionnaires. The results indicated that a larger number of students reminded small sized animals and/or plants as words associated with small organisms. In addition, some students depicted anthropomorphic or abstract microorganisms. In this study, more stereotypes of scientists' appearance were exhibited at sixth graders and city region group. Most of the students depicted indicators such as lab coat, glasses, scientific instruments for observing, indoor, male and young, whereas only a few students depicted collaborative work. There was statistically significant difference between girls and boys, because boys perceived male scientists only, while half of girls depicted female. More frequent research instruments and scientific captions were used when science gifted students depicted scientists studying small organisms. These results could be contributed to education on microorganisms in elementary science.