• 지식경영연구
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• 제1권1호
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• pp.91-107
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• 2000

This paper proposes the Organizational Knowledge Discovery Process Model (OK-DPM) as an initiative for developing a knowledge management methodology. OK-DPM is a model designed to effectively discover knowledge useful to the organization. It explains the knowledge discovery process from the conceptual level to the application level. It decomposes the organizational knowledge discovery process into 3 sub-processes; Creation, Suggestion and Validation. For each sub-process, design components are identified and possible methods for supporting each one are suggested. Also, the relationship patterns between the knowledge discovery process and knowledge type are explored. By applying OK-DPM to two real cases where the knowledge management projects are ongoing, the model was validated and revised. Even though we need to investigate with more cases to refine the OK-DPM, we found that it could provide some insights in developing the effective knowledge discovery process.

• 지식경영연구
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• 제1권1호
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• pp.81-90
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• 2000

This study proposes the knowledge discovery process for the effective mining of knowledge on the web. The proposed knowledge discovery process uses the Prior knowledge base and the Prior knowledge management system to reflect tacit knowledge in addition to explicit knowledge. The prior knowledge management system constructs the prior knowledge base using a fuzzy cognitive map, and defines information to be extracted from the web. In addition, it transforms the extracted information into the form being handled in mining process. Experiments using case-based reasoning and neural network" are performed to verify the usefulness of the proposed model. The experimental results are encouraging and prove the usefulness of the proposed model.

• 지식경영연구
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• 제2권1호
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• pp.109-132
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• 2001

The purpose of this article is to develop a dynamic model of organizational capabilities and knowledge creation, and at the same time identify the organizational capability factors for effective knowledge creation, by empirically analyzing the history of new Quinolone antibacterial drug compound (LB20304a) discovery process at LG, as a case in point. Major findings of this study are as follows. First, in a science-based area such as drug development, the core of successful knowledge creation lies in creative combination of different bodies of scientific explicit knowledge. Second, the greater the difficulty of learning external knowledge, the more tacit knowledge is needed for the recipient firm to effectively exploit that knowledge. Third, in science-based sector such as pharmaceutical industry, the key for successful knowledge creation lies in the capability of recruiting and retaining star scientists. Finally, for effective knowledge creation, a firm must keep its balance among three dimensions of organizational capabilities: local, process, architectural capabilities.

• Journal of Electrical Engineering and information Science
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• 제1권2호
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• pp.146-146
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• 1996

Although knowledge discovery is increasingly important in databases, the discovered knowledge sets may not be effectively used for application domains. It is partly because knowledge discovery does not take user's interests into account, and too many knowledge sets are discovered to handle efficiently. We believe that user's interests are conveyed by a query and if a nested query is concerned it may include a user's thought process. This paper describes a novel concept for discovering knowledge sets based on query processing. Knowledge discovery process is performed by: extracting features from databases, spanning features to generate range features, and constituting a knowledge set. The contributions of this paper include the following: (1) not only simple queries but also nested queries are considered to discover knowledge sets regarding user's interests and user's thought process, (2) not only positive examples (answer to a query) but also negative examples are considered to discover knowledge sets regarding database abstraction and database exceptions, and (3) finally, the discovered knowledge sets are quantified.

• Journal of Electrical Engineering and information Science
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• 제1권2호
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• pp.145-156
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• 1996

Although knowledge discovery is increasingly important in databases, the discovered knowledge sets may not be effectively used for application domains. It is partly because knowledge discovery does not take user's interests into account, and too many knowledge sets are discovered to handle efficiently. We believe that user's interests are conveyed by a query and if a nested query is concerned it may include a user's thought process. This paper describes a novel concept for discovering knowledge sets based on query processing. Knowledge discovery process is performed by: extracting features from databases, spanning features to generate range features, and constituting a knowledge set. The contributions of this paper include the following: (1) not only simple queries but also nested queries are considered to discover knowledge sets regarding user's interests and user's thought process, (2) not only positive examples (answer to a query) but also negative examples are considered to discover knowledge sets regarding database abstraction and database exceptions, and (3) finally, the discovered knowledge sets are quantified.

• 한국초등과학교육학회지:초등과학교육
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• 제23권1호
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• pp.61-73
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• 2004

본 연구는 다양한 경향성을 발견할 수 있는 과제를 피험자들에게 제시하고, 피험자들이 경향성 지식을 생성하는 과정에서 표상 된 지식의 종류와 사고 유형을 분석하여 경향성 지식의 생성 과정을 알아보고자 하였다. 본 연구의 결과, 경향성 지식의 생성 과정에서 표상 된 지식은 요소, 요소변화, 관련선지식, 예측경향성, 최종 경향성 지식 등 5가지 유형의 과정적 지식이 생성되었다. 그리고 사용된 사고 유형은 `대상인식', '관련지식회상', '요소 또는 요소변화의 탐색', '예측경향성 발견', '예측경향성 확인', 경향성 조합', '경향성 선택' 등 7가지 유형의 사고가 경향성 지식 생성 과정에 관여함을 볼 수 있었다. 또한 경향성 지식 생성과정은 `요소 \$\longrightarrow$요소변화 \$\longrightarrow$(관련선지식 \$\longrightarrow$)예측경향성 생성ㆍ확인 \$\longrightarrow$최종경향성'의 순으로 과정적 지식들이 단계적으로 표상 되어 생성되었으며, 이러한 과정에서 귀납적 추론뿐만 아니라, 귀추적 추론과 연역적 추론도 함께 관여하는 것을 볼 수 있었다.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.431-434
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• 2001

Knowledge discovery in databases(KDD) is the process for extracting valid, novel, potentially useful and understandable knowledge form real data. There are many academic and industrial activities with new technologies and application areas. Particularly, data mining is the core step in the KDD process, consisting of many algorithms to perform clustering, pattern recognition and rule induction functions. The main goal of these algorithms is prediction and description. Prediction means the assessment of unknown variables. Description is concerned with providing understandable results in a compatible format to human users. We introduce an efficient data mining algorithm considering predictive and descriptive capability. Reasonable pattern is derived from real world data by a revised neural network model and a proposed fuzzy rule extraction technique is applied to obtain understandable knowledge. The proposed neural network model is a hierarchical self-organizing system. The rule base is compatible to decision makers perception because the generated fuzzy rule set reflects the human information process. Results from real world application are analyzed to evaluate the system\`s performance.

• International Journal of Computer Science & Network Security
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• 제21권7호
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• pp.221-232
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• 2021

In recent years, with the existence of huge amounts of data stored in huge databases, the need for developing accurate tools for analyzing data and extracting information and knowledge from the huge and multi-source databases have been increased. Hence, new and modern techniques have emerged that will contribute to the development of all other sciences. Knowledge discovery techniques are among these technologies, one popular technique of knowledge discovery techniques is data mining which aims to knowledge discovery from huge amounts of data. Such modern technologies of knowledge discovery will contribute to the development of all other fields. Data mining is important, interesting technique, and has many different and varied algorithms; Therefore, this paper aims to present overview of data mining, and clarify the most important of those algorithms and their uses.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.111.5-111
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• 2002

●NGMS ●Digital Factory ●S-Diagnostics ●Fuzzy Logic ●Neural Network ●DPNN ●Data Mining & Knowledge Discovery

• 인터넷정보학회논문지
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• 제13권2호
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• pp.109-118
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• 2012

본 논문에서는 워크플로우 협력네트워크 지식의 발견 알고리즘을 제안한다. 즉, 워크플로우 인텔리전스 (또는 비즈니스 프로세스 인텔리전스) 기술은 워크플로우 모델들과 그의 실행이력으로부터 일련의 지식을 발견, 분석, 모니터링 및 제어, 그리고 예측하는 세부기법들로 구성되는데, 본 논문에서는 워크플로우 모델을 구성하는 액티버티들과 그들의 수행자들간의 협력네트워크 지식을 "워크 플로우 협력네크워크 지식"라고 정의하고, 그의 발견기법인 정보제어넷(ICN, information control net)기반 워크플로우 협력네트워크 지식 발견 알고리즘을 제안한다. 특히, 제안한 알고리즘의 적용 사례를 통해 특정 워크플로우 모델로부터 해당 워크플로우 협력네트워크 지식을 성공적으로 생성할 수 있음을 증명함으로써 본 논문에서 제안한 알고리즘의 정확성 및 적합성을 검증한다.