• Journal of KIISE:Databases
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• v.31 no.3
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• pp.205-218
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• 2004

As the development of database technology for managing spatiotemporal data, new types of spatiotemporal application services that need the spatiotemporal knowledge discovery from the large volume of spatiotemporal data are emerging. In this paper, a new 3-layered discovery framework for the development of spatiotemporal knowledge discovery techniques is proposed. The framework supports the foundation model in order not only to define spatiotemporal knowledge discovery problem but also to represent the definition of spatiotemporal knowledge and their relationships. Also the components of spatiotemporal knowledge discovery system and its implementation model are proposed. The discovery framework proposed in this paper satisfies the requirement of the development of new types of spatiotemporal knowledge discovery techniques. The proposed framework can support the representation model of each element and relationships between objects of the spatiotemporal data set, information and knowledge. Hence in designing of the new types of knowledge discovery such as spatiotemporal moving pattern, the proposed framework can not only formalize but also simplify the discovery problems.

• Journal of Information Processing Systems
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• v.2 no.2
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• pp.124-129
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• 2006

With the explosive increase in the generation and utilization of spatiotemporal data sets, many research efforts have been focused on the efficient handling of the large volume of spatiotemporal sets. With the remarkable growth of ubiquitous computing technology, mining from the huge volume of spatiotemporal data sets is regarded as a core technology which can provide real world applications with intelligence. In this paper, we propose a 3-tier knowledge discovery framework for spatiotemporal data mining. This framework provides a foundation model not only to define the problem of spatiotemporal knowledge discovery but also to represent new knowledge and its relationships. Using the proposed knowledge discovery framework, we can easily formalize spatiotemporal data mining problems. The representation model is very useful in modeling the basic elements and the relationships between the objects in spatiotemporal data sets, information and knowledge.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.435-440
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• 2001

The development of web-aware knowledge discovery system has received a great deal of attention in recent years. It plays a key-enabling role for competitive businesses in the E-commerce era. One of the challenges in developing web-aware knowledge discovery systems is to integrate and coordinate and coordinate existing standalone or legacy knowledge discovery applications in a seamless manner, so that cost-effective systems can be developed without the need of costly proprietary products. In this paper, we present an approach for developing a framework of web-aware interoperable knowledge discovery system to achieve this purpose. This approach applies RMI and high-level code wrapper of Java distributed object computing to address the issues of interoperability in heterogeneous environments, which includes programming language, platform, and visual object model. The effectiveness of the proposed framework is demonstrated through the integration and extension of the two well-known standalone knowledge discovery tools, SOM_PAK and Nenet. It confirms that a variety of interoperable knowledge discovery systems can be constructed efficiently on the basis of the framework to meet various requirements of knowledge discovery tasks.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.51-66
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• 2023

In this paper, we propose a new type of process discovery framework, which is named as control-path-driven process group discovery framework, to be used for process mining and process reengineering in supporting life-cycle management of business process models. In addition, we develop a process mining system based on the proposed framework and perform experimental verification through it. The process execution event logs applied to the experimental effectiveness and verification are specially defined as Process BIG-Logs, and we use it as the input datasets for the proposed discovery framework. As an eventual goal of this paper, we design and implement a control path-driven process group discovery algorithm and framework that is improved from the ρ-algorithm, and we try to verify the functional correctness of the proposed algorithm and framework by using the implemented system with a BIG-Log dataset. Note that all the process mining algorithm, framework, and system developed in this paper are based on the structural information control net process modeling methodology.

• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.877-885
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• 2009

In this paper, we propose the SINDI-Grid which is a high-performance framework for scientific and technological knowledge discovery using the grid computing. By using the advantages of the grid computing providing data repository of large-volume and high-speed computing power, the SINDI-Grid framework provides a variety of grid services for distributed data analysis and scientific knowledge processing. And the SINDI-Workflow tool exploits these services so that performs the design and execution for scientific and technological knowledge discovery applications which integrate various information processing algorithms.

• Asia pacific journal of information systems
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• v.11 no.2
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• pp.99-119
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• 2001

The creation of knowledge is a major concern for knowledge management practice. In particular, effective knowledge creation is one of the critical success factor in SI(System Integration) industry. This paper designs a framework of effective knowledge creation methods for organizations in SI industry. First, we give a comprehensive survey on knowledge creation and discovery methods. And the structure of SI knowledge has been analysed. Also, characteristics of knowledge management processes of SI industry have been surveyed and analysed. A framework for effective knowledge creation of SI organization has been discussed based on the characteristics of SI knowledge and knowledge management processes. Organizational issues and theoretical issues of the methods have been discussed. Future research will be needed to expand the current framework and to examine the effectiveness of the proposed framework.

• Journal of Information Technology Services
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• v.20 no.3
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• pp.41-56
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• 2021

New drug discovery and development research enable clinical treatment that saves human life and improves the quality of life, but the possibility of success with new drugs is significantly low despite a long time of 14 to 16 years and a large investment of 2 to 3 trillion won in traditional methods. As artificial intelligence is expected to radically change the new drug development paradigm, artificial intelligence new drug discovery and development projects are underway in various forms of collaboration, such as joint research between global pharmaceutical companies and IT companies, and government-private consortiums. This study uses the TOE framework and the Value-based Adoption Model, and the technical, organizational, and environmental factors that should be considered for the acceptance of AI technology at the level of the new drug research organization are the value of artificial intelligence technology. By analyzing the explanatory power of the relationship between perception and intention to use, it is intended to derive practical implications. Therefore, in this work, we present a research model in which technical, organizational, and environmental factors affecting the introduction of artificial intelligence technologies are mediated by strategic value recognition that takes into account all factors of benefit and sacrifice. Empirical analysis shows that usefulness, technicality, and innovativeness have significantly affected the perceived value of AI drug development systems, and that social influence and technology support infrastructure have significant impact on AI Drug Discovery and Development systems.

• 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.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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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.

• Management Science and Financial Engineering
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• v.20 no.1
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• pp.11-16
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• 2014

Extending from Grossman and Stiglitz (1980), we provide an asset pricing model of a synchronously traded cross-listed pair under information asymmetry. Following Garbade and Silber (1983), the model further embraces multi-market price discovery in a dynamic framework. The implications are as follows: The price sensitivity of holdings is higher for informed traders than for uninformed traders; the largest cross-border price spread occurs in the absence of arbitrageurs; price discovery is more likely in markets with a larger population of informed traders; and parity convergence accelerates with a higher price elasticity of demand of arbitrageurs.