• Journal of the Korean Data and Information Science Society
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• v.24 no.5
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• pp.1013-1027
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• 2013

As the need for large-scale data analysis is rapidly increasing, Hadoop, or the platform that realizes large-scale data processing, and MapReduce, or the internal computational model of Hadoop, are receiving great attention. This paper reviews the basic concepts of Hadoop and MapReduce necessary for data analysts who are familiar with statistical programming, through examples that combine the R programming language and Hadoop.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.125-133
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• 1999

The objective of knowledge discovery and data mining lies in the generation of useful insights from a store of data. This paper presents a framework for knowledge mining to provide a systematic approach to the selection and deployment of tools for automated learning. Every methodology has its strengths and limitations. Consequently, a multistrategy approach may be required to take advantage of the strengths of disparate technique while circumventing their individual limitations. For concreteness, the general framework for data mining in marketing is examined in the context of developing agents for optimizing a supply chain network.

• Journal of Korea Society of Industrial Information Systems
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• v.2 no.2
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• pp.189-207
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• 1997

Recently, we have witnessed a host of emerging tools in the management support systems (MSS) area including the data warehouse/multidimensinal databases (MDDB), data mining, on-line analytical processing (OLAP), intelligent agents, World Wide Web(WWW) technologies, the Internet, and corporate intranets. These tools are reshaping MSS developments in organizations. This article reviews a set of emerging data management technologies in the knowledge discovery in databases(KDD) process and analyzes their implications for decision support. Furthermore, today's MSS are equipped with a plethora of AI techniques (artifical neural networks, and genetic algorithms, etc) fuzzy sets, modeling by example , geographical information system(GIS), logic modeling, and visual interactive modeling (VIM) , All these developments suggest that we are shifting the corporate decision making paradigm form information-driven decision making in the1980s to knowledge-driven decision making in the 1990s.

• Journal of Sericultural and Entomological Science
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• v.43 no.1
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• pp.29-32
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• 2001

At present, more than 300 races of the Silkworm are conserved and used as valuable genetic resources. But because of the uneffectiveness of manual data management, faster and systematic data base construction is needed. So, development of silkworm genetic resources management program has been begun and the result can be practically used. When developing the program, Visual basic was used for data input system construction, and MS Access for database. IIS(Internet Information System) and ASP(Active Server Page) was also used for searching data and information with Internet Web Server and Web Browser which is comfortable for constructing database and providing information. Data input item consists of 46 practical characteristics such as race name, moltinism, larval period and pupation percentage etc.. And these characteristics are classified with qualitative and quantitative character. Photographs of silkworm, cocoon and other related items were scanned and the image data was recorded on the database.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2007.11a
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• pp.388-394
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• 2007

In wireless network, we propose a predictive location update scheme which considers mobile user's(MU's) mobility patterns. MU's mobility patterns can be found from a movement history data. The prediction accuracy and model complexity depend on the degree of application of history data. The more data we use, the more accurate the prediction is. As a result, the location management cost is reduced, but complexity of the model increases. In this paper, we classify MU's mobility patterns into four types. For each type, we find the respective optimal number of application of history data, and predictive location area by using the simulation. The optimal numbers of four types are shown to be different. When we use more than three application of history data, the simulation time and data storage are shown to increase very steeply.

• 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.

• Proceedings of the Korea Contents Association Conference
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• 2005.05a
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• pp.329-332
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• 2005

The Internet advances and the documents of existing Is provided on web. An amount of data is increased through amendment and addition of information. The web is wide and is used and to follow the users of the majority which acquires information depend in 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 data management system, which is composed of backup and restoration. This system provides systematic, efficient and stable data management on web.

• Journal of Korean Society for Quality Management
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• v.11 no.2
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• pp.25-31
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• 1983

Often arrangement for effective product assessment and audit have not been completely satisfactory. The underlying reasons are: (a) The lack of early evidence of new unit quality. (b) The collection and processing of data. (c) Ineffective data analysis techniques. (d) The variability of information on which decision making is based. Because of the nature of the product the essential outputs from an affective QA organization would be: (a) Confirmation of new unit quality. (b) Detection of failures which are either epidemic or slowly degradatory. (c) Identification of failure cases. (d) Provision of management information at the right time to effect the necessary corrective action. The heart of an effective QA scheme is the acquisition and processing of data. With the advent of data processing for quality monitoring becomes feasible in an automotive testing environment. This paper shows how the method enables us to use Automotive Testing data for the cost benefits of QA management.

• Asia pacific journal of information systems
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• v.8 no.3
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• pp.51-73
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• 1998

This paper is to evaluate the application potentials of data mining in the areas of Supply Chain Management (SCM) and to suggest the architectures of Decision Support Systems (DSS) that support data mining activities. We first briefly introduce data mining and review the recent literatures on SCM and then evaluate data mining applications to SCM in three aspects: marketing, operations management and information systems. By analyzing the cases about pricing models in distribution channels, demand forecasting and quality control, it is shown that artificial intelligence techniques such as artificial neural networks, case-based reasoning and expert systems, combined with traditional analysis models, effectively mine the useful knowledge from the large volume of SCM data. Agent-based information system is addressed as an important architecture that enables the pursuit of global optimization of SCM through communication and information sharing among supply chain constituents without loss of their characteristics and independence. We expect that the suggested architectures of intelligent DSS provide the basis in developing information systems for SCM to improve the quality of organizational decisions.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.45
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• pp.33-45
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• 1998

The typical characteristic of PDM(Product Data Management) System seperates the databases to store the meta data and applications. Therefore, meta data contains the information for the location of file, user profiles, relationships between the files, and process. PDM utilizes these information efficiently and does file management, configuration management, and process management. In this view, the integration strategy of PDM is to merge data and process. In the view of architecture, the interface between data and application and the actions of each application execute seamlessly. This architecture is viewed as integrated data and process among enterprises and implemented with client/server technology in distributed process environment that interfaced with open object-oriented technology which is developed with business object in the object-oriented infrastructure. In this paper, we studied the definition, function, and scope of PDM and researched the core technologies to implement the PDM integration environment. We also researched the PDM utilization in distributed enterprise environment and implementation of PDM integration environment with this technical background.