• Journal of the Korean Operations Research and Management Science Society
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• v.21 no.3
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• pp.197-214
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• 1996

Financial analysis of stock data usually involves extensive computation of large amount of time series data sets. To handle the large size of the data sets and complexity of the analyses, database management systems have been increasingly adaopted for efficient management of stock data. Specially, relational database management system is employed more widely due to its simplistic data management approach. However, the normalized two-dimensional tables and the structured query language of the relational system turn out to be less effective than expected in accommodating time series stock data as well as the various computational operations. This paper explores a new data management approach to stock data management on the basis of an object-oriented database management system (ODBMS), and proposes a data model supporting times series data storage and incorporating a set of financial analysis functions. In terms of functional stock data analysis, it specially focuses on a primitive set of operations such as variance of stock data. In accomplishing this, we first point out the problems of a relational approach to the management of stock data and show the strength of the ODBMS. We secondly propose an object model delineating the structural relationships among objects used in the stock data management and behavioral operations involved in the financial analysis. A prototype system is developed using a commercial ODBMS.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.239-246
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• 2011

This paper is a study on the management system of data and documents for the LRT Project using the systems engineering approach. It is addressed a computed based management system using the Systems Engineering Approach toward system thinking to manage effectively and efficiently data and documents. Because the unified efforts was insufficient data and outcomes of project management and systems engineering during the existing LRT Project, we proposed the unified computer management system for the project management and systems engineering efforts using the systems engineering methodology like as a operation concept, collection and analysis of requirements, and functional architecture analysis. This will offer supply efficiently data and outcomes management for the LRT project and then support the automatical document publish output.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.1 no.1
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• pp.9-21
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• 2020

Ecological communities adapt the concept of informatics in the late 20 century and develop rapidly in the early 21 century to form Ecoinformatics as the new approach of ecological research. The new approach takes into account the data-intensive nature of ecology, the precious information content of ecological data, and the growing capacity of computational technology to leverage complex data as well as the critical need for informing sustainable management of complex ecosystems. It comprehends techniques for data management, data analysis, synthesis, and forecasting on ecological research. The present paper attempts to review the development history, studies and application cases of ecoinformatics in ecological research especially on Long Term Ecological Research (LTER). From the applications show that the ecoinformatics approach and management system have formed a new paradigm in ecological research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3182-3203
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• 2023

With the growing adoption of cloud-based technologies, maintaining the privacy and security of cloud data has become a pressing issue. Privacy-preserving encryption schemes are a promising approach for achieving cloud data security, but they require careful design and implementation to be effective. The integrated approach to cloud data security that we suggest in this work uses CogniGate: the orchestrated permissions protocol, index trees, blockchain key management, and unique Opacus encryption. Opacus encryption is a novel homomorphic encryption scheme that enables computation on encrypted data, making it a powerful tool for cloud data security. CogniGate Protocol enables more flexibility and control over access to cloud data by allowing for fine-grained limitations on access depending on user parameters. Index trees provide an efficient data structure for storing and retrieving encrypted data, while blockchain key management ensures the secure and decentralized storage of encryption keys. Performance evaluation focuses on key aspects, including computation cost for the data owner, computation cost for data sharers, the average time cost of index construction, query consumption for data providers, and time cost in key generation. The results highlight that the integrated approach safeguards cloud data while preserving privacy, maintaining usability, and demonstrating high performance. In addition, we explore the role of differential privacy in our integrated approach, showing how it can be used to further enhance privacy protection without compromising performance. We also discuss the key management challenges associated with our approach and propose a novel blockchain-based key management system that leverages smart contracts and consensus mechanisms to ensure the secure and decentralized storage of encryption keys.

• Korean Journal of Computational Design and Engineering
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• v.20 no.3
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• pp.254-262
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• 2015

This study proposes an approach to Product Data Management (PDM) education for collaborative product data management, which can support collaborative product development process. This approach introduces social media and a PDM system into a framework for PDM education supported by consistent product development process and product data model. It has been applied to two PDM classes and the result shows that the social media in PDM education can support not only experiences of the collaborative product data management but also interactive and informal communications among instructors and participants using integrated social media with product data during courses.

• Journal of the Korea Safety Management & Science
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• v.16 no.1
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• pp.213-220
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• 2014

This study suggests new approach to identify core technologies through patent analysis. Specially, the approach applied data mining technique and multi-criteria decision making method to the co-classification information of registered patents. First, technological interrelationship matrices of intensity, relatedness, and cross-impact perspectives are constructed with support, lift and confidence values calculated by conducting an association rule mining on the co-classification information of patent data. Second, the analytic network process is applied to the constructed technological interrelationship matrices in order to produce the importance values of technologies from each perspective. Finally, data envelopment analysis is employed to the derived importance values in order to identify priorities of technologies, putting three perspectives together. It is expected that suggested approach could help technology planners to formulate strategy and policy for technological innovation.

• Journal of Korean Institute of Industrial Engineers
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• v.36 no.1
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• pp.13-21
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• 2010

The prediction of contract cancellation of customers is essential in insurance companies but it is a difficult problem because the customer database is large and the target or cancelled customers are a small proportion of the database. This paper proposes a new data mining approach to the binary classification by handling a large-scale unbalanced data. Over-sampling, clustering, regularized logistic regression and boosting are also incorporated in the proposed approach. The proposed approach was applied to a real data set in the area of insurance and the results were compared with some other classification techniques.

• Management Science and Financial Engineering
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• v.20 no.1
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• pp.1-9
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• 2014

In multi-objective scheduling problems, the objectives are usually in conflict. To obtain a satisfactory compromise and resolve the issue of NP-hardness, most existing works have suggested employing meta-heuristic methods, such as genetic algorithms. In this research, we propose a novel data-driven approach for generating a single solution that compromises multiple rules pursuing different objectives. The proposed method uses a data mining technique, namely, random forests, in order to extract the logics of several historic schedules and aggregate those. Since it involves learning predictive models, future schedules with the same previous objectives can be easily and quickly obtained by applying new production data into the models. The proposed approach is illustrated with a simulation study, where it appears to successfully produce a new solution showing balanced scheduling performances.

• International Journal of Quality Innovation
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• v.2 no.2
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• pp.93-100
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• 2001

This paper illustrates a prototyping framework of the documentation-standards retrieval system via the data mining approach for enhancing software development quality. We first present an approach for designing a retrieval algorithm based on data mining, with the three basic technologies of machine learning, statistics and database management, applied to this system to speed up the searching time and increase the fitness. This approach derives from the observation that data mining can discover unsuspected relationships among elements in large databases. This observation suggests that data mining can be used to elicit new knowledge about the design of a subject system and that it can be applied to large legacy systems for efficiency. Finally, software development quality will be improved at the same time when the project managers retrieving for the documentation standards.

• International Journal of Internet, Broadcasting and Communication
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• v.5 no.1
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• pp.14-17
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• 2013

RDF data is widely used for exchanging data nowadays to enable semantic web era. This leads to the need for storing and retrieving these data efficiently and effectively. Recently, the structure index in graph-based perspective is considered as a promising approach to deal with issues of complex query graphs. However, even though there are many researches based on structure indexing, there can be a better architectural approach instead of addressing the issue as a part. In this research, we propose architecture for storing, query processing and retrieving RDF data in efficient manner using structure indexing. Our research utilizes research results from iStore and 2 relation-based approaches and we focus on improving query processing to reduce the time of loading data and I/O cost.