• The Journal of Korean Association of Computer Education
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• v.9 no.2
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• pp.47-55
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• 2006

To determine efficient methods of database teaching, this paper applied the situated learning theory to the teaching and learning method and analyzed the effects. Previous related studies were also examined, with the essential contents in database analyzed based on Bloom's taxonomy of educational objectives. Moreover, this paper presented a strategy wherein the contents of database learning are classified into two categories: basic knowledge and technical and extended knowledge. Experimental and control groups were selected based on related studies, and the effects of database teaching and learning method, determined by technique and attitude area as well as knowledge area. After preparing and applying to the teaching and learning method the actual educational curriculum, the following results were drawn: (1) the experimental group showed better performance in terms of understanding the concept of database, operating database, and constructing a database table when the situated learning theory was applied to the teaching method, and; (2) the experimental group was also more receptive compared to the control group, which opted to take technique-oriented database courses. Therefore, various teaching and learning methods aside from the situated learning theory should be applied and analyzed in database and computer science fields for maximum effects.

• Journal of the Korean Data and Information Science Society
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• v.15 no.4
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• pp.707-716
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• 2004

Recently, many areas of application such as statistics and industrial engineering are interested in the effective education of databases. In this article we design and implement a database learning system based on learner management techniques. The system supports a personalized/ team-centered learning environment, monitoring the learning attitude of learners, and a method for the assessment.

• The Journal of Information Technology and Database
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• v.4 no.1
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• pp.65-77
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• 1997

This paper describes and effective teaching approach to an undergraduate database course. This research draws on practical experience based on the hands-on practice approach which leads students to develop a database application utilizing various tools. Students not only learn concepts, methodologies and tools of database technology in class and through online multimedia learning aids, but also practice how to integrate them through collaborative team projects. The course employs collaborative learning approach and multimedia and internet technologies. Students are encouraged to work collaboratively on assignments and projects and to learn independently through online multimedia learning aids.

• Journal of IKEEE
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• v.22 no.4
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• pp.948-952
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• 2018

Haze removal is an object of scientific desire due to its various practical applications. Existing algorithms are founded upon histogram equalization, contrast maximization, or the growing trend of applying machine learning in image processing. Since machine learning-based algorithms solve problems based on the data, they usually perform better than those based on traditional image processing/computer vision techniques. However, to achieve such a high performance, one of the requisites is a large and reliable training database, which seems to be unattainable owing to the complexity of real hazy and haze-free images acquisition. As a result, researchers are currently using the synthetic database, obtained by introducing the synthetic haze drawn from the standard uniform distribution into the clear images. In this paper, we propose the enhanced equidistribution, improving upon our previous study on equidistribution, and use it to make a new database for training machine learning-based haze removal algorithms. A large number of experiments verify the effectiveness of our proposed methodology.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1459-1466
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• 2018

Robots which detect human and recognize action are important factors for human interaction, and many researches have been conducted. Recently, deep learning technology has developed and learning based robot's technology is a major research area. These studies require a database to learn and evaluate for intelligent human perception. In this paper, we propose a multi-modal sensor-based image database condition considering the security task by analyzing the image database to detect the person in the outdoor environment and to recognize the behavior during the running of the robot.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.183-186
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• 2018

In this paper, we propose the database design for skill learning service through the internet from the viewpoint of service engineering. This paper we describe the outlines for a design theory for skill learning service, which can lead to the satisfaction of both learner and instructor. Compared to other services, motion control learning takes a considerable amount of time, and this leads to a difficulty for learners to rate the quality of the service as well as for the instructors to provide consistent quality and standard of teaching. To solve these problems, we use a relational database with MongoDB which is an unstructured database allowing to flexibly incorporate the demands of both learner and instructor into the database itself.

• The Journal of Information Systems
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• v.2
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• pp.194-206
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• 1993

Intelligent remote learning system is a system that incorporate communication technology and others : a database engine, an intelligent tutorial system. Learners can study by themselves through the intelligent tutorial system. The existence of a communication, database and artificial intelligence enhance the capability of IRLS. According to Parsaye, an intelligent databases should have the following features : 1) Knowledge discovery. 2) Data integrity and quality control. 3) Hypermedia management. 4) Data presentation and display. 5) Decision support and scenario analysis. 6) Data format management. 7) Intelligent system design tools. I hope that this research of framework for IRLS paves for the future research. As mentioned in the above, the future work will include an intelligent database, self-learning mechanism using neural network.

• Journal of Computing Science and Engineering
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• v.5 no.3
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• pp.183-196
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• 2011

There has recently been a surge of interest in relational database mining that aims to discover useful patterns across multiple interlinked database relations. It is crucial for a learning algorithm to explore the multiple inter-connected relations so that important attributes are not excluded when mining such relational repositories. However, from a data privacy perspective, it becomes difficult to identify all possible relationships between attributes from the different relations, considering a complex database schema. That is, seemingly harmless attributes may be linked to confidential information, leading to data leaks when building a model. Thus, we are at risk of disclosing unwanted knowledge when publishing the results of a data mining exercise. For instance, consider a financial database classification task to determine whether a loan is considered high risk. Suppose that we are aware that the database contains another confidential attribute, such as income level, that should not be divulged. One may thus choose to eliminate, or distort, the income level from the database to prevent potential privacy leakage. However, even after distortion, a learning model against the modified database may accurately determine the income level values. It follows that the database is still unsafe and may be compromised. This paper demonstrates this potential for privacy leakage in multi-relational classification and illustrates how such potential leaks may be detected. We propose a method to generate a ranked list of subschemas that maintains the predictive performance on the class attribute, while limiting the disclosure risk, and predictive accuracy, of confidential attributes. We illustrate and demonstrate the effectiveness of our method against a financial database and an insurance database.

• Asia pacific journal of information systems
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• v.5 no.2
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• pp.143-176
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• 1995

We present an overview of the Extensional Object Model (ExOM) and describe in detail the learning and classification components which integrate concepts from machine learning and object-oriented databases. The ExOM emphasizes flexibility in information acquisition, learning, and classification which are useful to support tasks such as diagnosis, planning, design, and database mining. As a vehicle to integrate machine learning and databases, the ExOM supports a broad range of learning and classification methods and integrates the learning and classification components with traditional database functions. To ensure the integrity of ExOM databases, a subsumption testing rule is developed that encompasses categories defined by type expressions as well as concept definitions generated by machine learning algorithms. A prototype of the learning and classification components of the ExOM is implemented in Smalltalk/V Windows.

• Asia pacific journal of information systems
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• v.17 no.1
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• pp.33-58
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• 2007

Quiet often, an organization tries to grapple with inconsistent and partial information to generate relevant information to support decision making and action. As such, an organization scans the environment interprets scanned data, executes actions, and learns from feedback of actions, which boils down to computational interpretations and learning in terms of machine learning, statistics, and database. The ExOM proposed in this paper is geared to facilitate such knowledge discovery found in large databases in a most flexible manner. It supports a broad range of learning and classification styles and integrates them with traditional database functions. The learning and classification components of the ExOM are tightly integrated so that learning and classification of objects is less burdensome to ordinary users. A brief sketch of a strategy as to the expressiveness of terminological language is followed by a description of prototype implementation of the learning and classification components of the ExOM.