• Journal of Information Processing Systems
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• v.7 no.4
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• pp.635-652
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• 2011

In ubiquitous environments, many applications need to process data with time and space dimensions. Because of this, there is growing attention not only on gathering spatiotemporal data in ubiquitous environments, but also on processing such data in databases. In order to obtain the full benefits from spatiotemporal data, we need a data model that naturally expresses the properties of spatiotemporal data. In this paper, we introduce three spatiotemporal data models extended from temporal data models. The main goal of this paper is to determine which data model is less complex in the spatiotemporal context. To this end, we compare their query languages in the complexity aspect because the complexity of a query language is tightly coupled with its underlying data model. Throughout our investigations, we show that it is important to intertwine space and time dimensions and keep one-to-one correspondence between an object in the real world and a tuple in a database in order to naturally express queries in ubiquitous applications.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.5
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• pp.215-223
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• 2021

3D data can be categorized into two parts : Euclidean data and non-Euclidean data. In general, 3D data exists in the form of non-Euclidean data. Due to irregularities in non-Euclidean data such as mesh and point cloud, early 3D deep learning studies transformed these data into regular forms of Euclidean data to utilize them. This approach, however, cannot use memory efficiently and causes loses of essential information on objects. Thus, various approaches that can directly apply deep learning architecture to non-Euclidean 3D data have emerged. In this survey, we introduce various deep learning methods for mesh and point cloud data. After analyzing the operating principles of these methods designed for irregular data, we compare the performance of existing methods for shape classification and segmentation tasks.

• Convergence Security Journal
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• v.21 no.1
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• pp.73-78
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• 2021

Big data is a key driver of the fourth industrial revolution, represented by ultra-connected, ultra-intelligence, and ultra-convergence, and it is important to create innovation and share, link, and utilize data to discover business models. However, it is difficult to secure and utilize high-quality and abundant data when big data platforms are built in a regular manner without considering shared-linked. Therefore, this paper presents the development direction of big data platform infrastructure by comparing and analyzing various cases of big data platforms to enable data production, construction, linkage, and distribution.

• Journal of information and communication convergence engineering
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• v.19 no.4
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• pp.228-233
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• 2021

In this study, we analyze the credit information (loan, delinquency information, etc.) of individual business owners to generate voluminous training data to establish a bankruptcy prediction model through a partial synthetic training technique. Furthermore, we evaluate the prediction performance of the newly generated data compared to the actual data. When using conditional tabular generative adversarial networks (CTGAN)-based training data generated by the experimental results (a logistic regression task), the recall is improved by 1.75 times compared to that obtained using the actual data. The probability that both the actual and generated data are sampled over an identical distribution is verified to be much higher than 80%. Providing artificial intelligence training data through data synthesis in the fields of credit rating and default risk prediction of individual businesses, which have not been relatively active in research, promotes further in-depth research efforts focused on utilizing such methods.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.2
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• pp.301-307
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• 2021

After the spread of the data collection devices such as smart meters, energy data is increasingly collected in a variety of ways, and its importance continues to grow. However, due to technical or practical limitations, errors such as missing or outliers in the data occur during data collection process. Especially in the case of customer-related data, billing problems may occur, so energy companies are conducting various research to process such data. In addition, efforts are being made to create added value from data, which makes it difficult to provide such services unless reliability of data is guaranteed. In order to solve these challenges, this research analyzes prior research related to bad data processing specifically in the energy field, and propose new missing value processing methods to improve the reliability and field utilization of energy data.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.338-345
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• 2023

Nowadays, many companies are striving to turn customer value into business value. Customer Relationship Management is a management system that develops effective and efficient marketing strategies by classifying customers in detail based on their information, i.e. databases, and consists of various information technologies. To implement this management system, a customer integration database must be established, and customer characteristics (buying behavior, preferences, etc.) must be analyzed with the databases established and the behavior of each customer must be predicted. This study aims to systematically manage a large amount of customer data generated by companies that apply Customer Relationship Management, in order to develop data design and data governance strategies that should be considered to increase customer value and even company value. We mainly looked at the characteristics of customer relationship management and data governance, and then explored the link between the field of customer relationship management and data governance. In addition, we have developed a data strategy that companies need to perform data governance for customer relationship management.

• Journal of Information Processing Systems
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• v.20 no.4
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• pp.524-534
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• 2024

The release of relational data containing personal sensitive information poses a significant risk of privacy breaches. To preserve privacy while publishing such data, it is important to implement techniques that ensure protection of sensitive information. One popular technique used for this purpose is data perturbation, which is popularly used for privacy-preserving data release due to its simplicity and efficiency. However, the data perturbation has some limitations that prevent its practical application. As such, it is necessary to propose alternative solutions to overcome these limitations. In this study, we propose a novel approach to preserve privacy in the release of relational data containing personal sensitive information. This approach addresses an intuitive, syntactic privacy criterion for data perturbation and two perturbation methods for relational data release. Through experiments with synthetic and real data, we evaluate the performance of our methods.

• The Journal of Information Technology and Database
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• v.6 no.1
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• pp.15-25
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• 1999

We propose an extended relational data model which can represent the reliability of data. In this paper, the reliability of data is defined as the reliability of the source, from which the data originated. We represent the reliability of data at the level of attribute values, instead of tuples, then define the selection, product and join operators.

• Journal of Korean Society for Quality Management
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• v.26 no.3
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• pp.150-155
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• 1998

It is necessary to describe a data set after collection of data in elementary statistics course. Two major numerical summary of the data set may be measures of central location and dispersion. There are various unmerical summary methods in presenting how data are dispersed and each method has its own advantages and disadvantages. Quartiles are discussed among several methods to describe dispersion of data set. When data type is discrete, exact quartile values are sometimes ambiguous to find, whereas exact quartile values are obtained for contionuous data. Examples of both data types are given. Programs listed below may be used to provide quartiles in MINITAB and SAS.

• Journal of Industrial Technology
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• v.21 no.A
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• pp.141-146
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• 2001

XML is fast emerging as the dominant standard to represent and exchange data in the Web. As the amount of data available in the Web has increased dramatically in recent years, the data resides in different forms ranging from semi-structured data to highly structured data in relational database. As semi-structured data will be represented by XML, XML will increase the ability of semi-structured data. In this paper, we propose an idea for extracting schema in XML document using DTD.