• International conference on construction engineering and project management
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• 2013.01a
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• pp.231-234
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• 2013

According to the recent development of USN technology, it has been applied in various fields of construction management. In particular, the concrete curing management using the wireless measurement system is actively being conducted. However, the existing method has limitations such as the reinstallation of temperature sensors and repositioning of repeaters. It is also not easy to acquire the measured data. Thus, this study focuses on the concrete curing management. This study proposes data acquisition method using the smartphone on construction site and tests applicability of the data measuring device and the smartphone. The test allows us to suggest the actual communication distance on construction site and to determine the correction value that is applied to the measured temperature. The data acquisition method proposed in this study is intended to enable appropriate management on construction site and will be able to be applied effectively to a variable construction site. It can also be used in all fields of construction management.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.111-119
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• 2014

The cloud environment need resource management method that to enable the big data issue and data analysis technology. Existing resource management uses the limited calculation method, therefore concentrated the resource bias problem. To solve this problem, the resource management requires the learning-based scheduling using resource history information. In this paper, we proposes the ART (Adaptive Resonance Theory)-based adaptive resource management. Our proposed method assigns the job to the suitable method with the resource monitoring and history management in cloud computing environment. The proposed method utilizes the unsupervised learning method. Our goal is to improve the data processing and service stability with the adaptive resource management. The propose method allow the systematic management, and utilize the available resource efficiently.

• International Journal of Contents
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• v.12 no.1
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• pp.60-64
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• 2016

As new applications of cloud data management system (CDMS) such as online games, cooperation edit, social network, and so on, are increasing, transaction processing capabilities for CDMS are required. Several transaction processing methods for cloud data management system (CDMS) have been proposed. However, existing transaction processing methods have some problems. Some of them provide limited transaction processing capabilities. Some of them are hard to be integrated with existing CDMSs. In this paper, we proposed a new concurrency control method to support transaction processing capability for CDMS to solve these problems. The proposed method was designed and implemented based on Spark, an in-memory distributed processing framework. It uses RDD (Resilient Distributed Dataset) model to provide fault tolerant to data in the main memory. In our proposed method, database stored in CDMS is loaded to main memory managed by Spark. The loaded data set is then transformed to RDD. In addition, we proposed a multi-version concurrency control method through immutable characteristics of RDD. Finally, we performed experiments to show the feasibility of the proposed method.

• Knowledge Management Research
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• v.15 no.3
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• pp.169-188
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• 2014

Big data seems to change knowledge management system and method of enterprises to large extent. Further, the type of method for utilization of unstructured data including image, v ideo, sensor data a nd text may determine the decision on expansion of knowledge management of the enterprise or government. This paper, in this light, attempts to figure out the prediction model of demands and supply for big data market of Korea trough data mining decision making tree by utilizing text bit data generated for 3 years on web and SNS for expansion of form for knowledge management. The results indicate that the market focused on H/W and storage leading by the government is big data market of Korea. Further, the demanders of big data have been found to put important on attribute factors including interest, quickness and economics. Meanwhile, innovation and growth have been found to be the attribute factors onto which the supplier puts importance. The results of this research show that the factors affect acceptance of big data technology differ for supplier and demander. This article may provide basic method for study on expansion of analysis form of enterprise and connection with its management activities.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.2
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• pp.421-431
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• 1999

NOD systems contain the data of multiple types such as text, image and video, and the size of NOD data depend on their data types. Therefore, in this paper, we propose a Data Placement Method based on Data Types(DPMDT), in which the data placement method depends on their type. Then, we analyze the performance of DPMDT with that of a Time Based Storage Management(TBSM) in which the data placement method depends on their created date, and that of Rate Based Storage Management(RBSM) in which the data placement method depends on their created date and accessed rate. In case of long playback of video news and a few disks(one disk), our results show that the performance of DPMDT is less efficient than that of TBSM and RBSM methods, however, in case of over 2 disks, the performance of DPMDT is more efficient than that of TBSM and RBSM methods.

• Journal of Engineering Education Research
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• v.17 no.5
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• pp.51-58
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• 2014

This study proposes an effective data management method for easing the burden on self-study report by analyzing operational data on accredited engineering programs. Four analysis criteria are developed: variability, difficulty level of collecting, urgency of analysis, timeliness. After the operational data are analyzed in terms of the analysis criteria, the data which should be managed in time are extracted according to the analysis results. This study proposes a data management method in which tasks of managing the timely-managed data are performed based on the regular academic schedule, so that the result of this study may be used as a working-level reference material.

• Journal of the Semiconductor & Display Technology
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• v.21 no.2
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• pp.133-137
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• 2022

In the smart industry where data plays an important role, cloud computing is being used in a complex and advanced way as a convergence technology because it has and fits well with its strengths. Accordingly, in order to utilize artificial intelligence rather than human beings for quality management of cloud computing services, a consistent standardization method of data collected from various nodes in various areas is required. Therefore, this study analyzed technologies and cases for incorporating artificial intelligence into specific services through previous studies, suggested a plan to use artificial intelligence to comprehensively standardize data in quality management of cloud computing services, and then verified it through case studies. It can also be applied to the artificial intelligence learning model that analyzes the risks arising from the data formalization method presented in this study and predicts the quality risks that are likely to occur. However, there is also a limitation that separate policy development for service quality management needs to be supplemented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.6 no.6
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• pp.486-490
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• 2005

The web is used widely and the users of the majority are depending to get the information on web, the necessity of the data management by web is increasing. The sudden system failure frequently occurs from network environment, we must protect data from this danger. In this paper, we present web based data management method, which is composed of two type backup and restoration. This method support data management through web browser and provides systematic, efficient and stable data management on web.

• Proceedings of the Korea Database Society Conference
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• 1994.09a
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• pp.105-129
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• 1994

The information system of corporations are facing a new environment expressed by miniaturization, decentralization and Open System. It is therefore of utmost importance for corporations to adapt flexibly th such new environment by providing for corresponding changes to their existing information systems. The objectives of this study are to identify this new environment faced by today′s information system and develop effective methods for data resource management under this new environment. In this study, it is assumed that the new environment faced by information systems can be specified as Distributed Computing Environment, and in order to achieve such system, presents Client/server architecture as its representative computing structure, This study defines Client/server architecture as a computing architecture which specialize the fuctionality of the client system and the server system in order to have an application distribute and perform cooperative processing at the best platform. Furthermore, from among the five structures utilized in Client/server architecture for distribution and cooperative processing of application between server and client this study presents two different data management methods under the Client/server environment; one is "Remote Data Management Method" which uses file server or database server and. the other is "Distributed Data Management Method" using distributed database management system. The result of this study leads to the conclusion that in the client/server environment although distributed application is assumed, the data could become centralized (in the case of file server or database server) or decentralized (in the case of distributed database system) and the data management method through a distributed database system where complete responsibility and powers with respect to control of data used by the user are given not only is it more adaptable to modern flexible corporate environment, but in terms of system operation, it presents a more efficient data management alternative compared to existing data management methods in terms of cutting costs.

• Korean Journal of Computational Design and Engineering
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• v.19 no.2
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• pp.103-110
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• 2014

Engineering changes are indispensable engineering and management activities for manufactures to develop competitive products and to maintain consistency of its product data. Analysis of engineering changes provides a core functionality to support decision makings for engineering change management. This study aims to develop a method for analysis of engineering changes based on On-Line Analytical Processing (OLAP), a proven database analysis technology that has been applied to various business areas. This approach automates data processing for engineering change analysis from product databases that follow an international standard for product data management (PDM), and enables analysts to analyze various aspects of engineering changes with its OLAP operations. The study consists of modeling a standard PDM database and a multidimensional data model for engineering change analysis, implementing the standard and multidimensional models with PDM and data cube systems and applying the implemented data cube to core functions of engineering change management, the evaluation and propagation of engineering changes.