• Journal of KIISE:Databases
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• v.28 no.3
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• pp.301-314
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• 2001

Data mining refers to a set of techniques for discovering implicit and useful knowledge from large database. Many studies on data mining have been pursued and some of them have involved issues of temporal data mining for discovering knowledge from temporal database, such as sequential pattern, similar time sequence, cyclic and temporal association rules, etc. However, all of the works treat problems for discovering temporal pattern from data which are stamped with time points and do not consider problems for discovering knowledge from temporal interval data. For example, there are many examples of temporal interval data that it can discover useful knowledge from. These include patient histories, purchaser histories, web log, and so on. Allen introduces relationships between intervals and operators for reasoning about relations between intervals. We present a new data mining technique that can discover temporal relation rules in temporal interval data by using the Allen's theory. In this paper, we present two new algorithms for discovering algorithm for generating temporal relation rules, discovers rules from temporal interval data. This technique can discover more useful knowledge in compared with conventional data mining techniques.

• Journal of Computing Science and Engineering
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• v.5 no.3
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• pp.183-196
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• 2011

There has recently been a surge of interest in relational database mining that aims to discover useful patterns across multiple interlinked database relations. It is crucial for a learning algorithm to explore the multiple inter-connected relations so that important attributes are not excluded when mining such relational repositories. However, from a data privacy perspective, it becomes difficult to identify all possible relationships between attributes from the different relations, considering a complex database schema. That is, seemingly harmless attributes may be linked to confidential information, leading to data leaks when building a model. Thus, we are at risk of disclosing unwanted knowledge when publishing the results of a data mining exercise. For instance, consider a financial database classification task to determine whether a loan is considered high risk. Suppose that we are aware that the database contains another confidential attribute, such as income level, that should not be divulged. One may thus choose to eliminate, or distort, the income level from the database to prevent potential privacy leakage. However, even after distortion, a learning model against the modified database may accurately determine the income level values. It follows that the database is still unsafe and may be compromised. This paper demonstrates this potential for privacy leakage in multi-relational classification and illustrates how such potential leaks may be detected. We propose a method to generate a ranked list of subschemas that maintains the predictive performance on the class attribute, while limiting the disclosure risk, and predictive accuracy, of confidential attributes. We illustrate and demonstrate the effectiveness of our method against a financial database and an insurance database.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.32 no.2
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• pp.101-123
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• 2021

This study analyses the questions and answers contained in the Knowledge Information Database of the collaborative digital reference service, 'Ask a librarian'. And based on the results of status of user requests, this study draws information usage behavior in the early stages of the service was derived. 1,124 Knowledge Information Database items out of 3,506 cases was analyzed by nine criterion. ① Number of questions and whether to be reference questions, ② Subject and keywords of the question, ③ Purpose of the question, ④ Type of question, ⑤ User's information request, ⑥ Information source and reference services provided by the librarian, ⑦ Number of days to answer, ⑧ Level of the participating library, ⑨ Question type by topic. As a results of analysis, first, users asked for reference questions from various topics as needed, rather than one from a similar topic at a time, but more than half of the total pure reference questions were from the field of library information science. Second, about 71.35% of users were using the 'Ask a librarian' service to recommend a list of information resources related to a particular topic or research problem, and there were also questions that required consultation on the reading situation. Third, the most preferred sources of information for users were bibliography, and in the case of online information sources, users did not relatively prefer them. Fourth, the number of days required to answer was able to confirm significant differences depending on the type of question and the level of the participating library. Fifth, 31.33% of the purpose of the general field question showed that were self-generated.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.105-113
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• 1999

최근 데이터와 데이터베이스의 폭발적 증가에 따라 무한한 데이터 속에서 정보나 지식을 찾고자하는 지식채굴과정 (knowledge discovery process)에 대한 관심이 높아지고 있다. 특히 기업 내외부 데이터베이스 뿐만 아니라 데이터웨어하우스 (data warehouse)를 기반으로 하는 OLAP환경에서의 데이터와 인터넷을 통한 웹 (web)에서의 정보 등 정보원의 다양화와 첨단화에 따라 다양한 환경 하에서의 지식채굴과정이 요구되고 있다. 본 연구에서는 인터넷 상의 지식을 효과적으로 채굴하기 위한 지식채굴과정을 제안한다. 제안된 지식채굴과정은 명시지 (explicit knowledge)외에 암묵지 (tacit knowledge)를 지식채굴과정에 반영하기 위해 선행지식베이스 (prior knowledge base)와 선행지식관리시스템 (prior knowledge management system)을 이용한다. 선행지식관리시스템은 퍼지인식도(fuzzy cognitive map)를 이용하여 선행지식베이스를 구축하여 이를 통해 웹에서 찾고자 하는 유용한 정보를 정의하고 추출된 정보를 지식변환시스템 (knowledge transformation system)을 통해 통합적인 추론과정에 사용할 수 있는 형태로 변환한다. 제안된 연구모형의 유용성을 검증하기 위하여 재무자료에 선행지식을 제외한 자료와 선행지식을 포함한 자료를 사례기반추론 (case-based reasoning)을 이용하여 실험한 결과, 제안된 지식채굴과정이 유용한 것으로 나타났다.

• Korean Journal of Health Education and Promotion
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• v.24 no.2
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• pp.45-61
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• 2007

Objectives: This study was to get database of health service for smoking preventing through investigating the smoking status of students and the knowledge, attitude on smoking. Methods: The subjects were consisted of 463 students who were currently enrolled in 1, 2 and 3 grade of 6 high schools located in Ulsan-city. The instruments for this study were smoking knowledge and smoking attitude questionnaire(each 20 items) developed by WHO. Results: Among the students 25.8% answered they had the experience of smoking. The experience of smoking related to general characteristics were showed significantly different according to opposite sex friends. Student's knowledge level about smoking prevention is high score to mean get obtain 0.65 out of 1. Smoking prevention knowledge level related to highest score(0.82) were have affect on pregnancy and an unborn child. Smoking prevention knowledge level related to low score(0.19) were get rid of stress. Therefore smoking prevention knowledge high level is non smoker rather than smoker. Student's attitude level about smoking prevention is high score to mean get obtain 2.0 out of 3. Smoking prevention attitude level related to highest score(2.5) were no smoking allowed public area and put a stop smoking to friends. Therefore smoking prevention attitude high level is non smoker rather than smoker. Conclusion: It follows from this study that education for smoking prevention should be continued from lower grade student and sustaining teaching for refusal skill against smoking is needed.

• Journal of the Korean Society for information Management
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• v.28 no.2
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• pp.97-115
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• 2011

A large and reliable citation database is necessary to identify and analyze citation behavior of Korean researchers in science and technology. Korea Institute of Science and Technology Information (KISTI) built the Korea Science Citation Database (KSCD), and have provided Korea Science Citation Index (KSCI) and Korea Journal Citation Reports (KJCR) services. In this article, citing behavior of Korean scientists on Korean journals was examined by using the KSCD that covers 459 Korean core journals. This research dealt with (1) statistical numeric information of journals in KSCD, (2) analysis of document types cited, (3) ratio of domestic to international documents cited and ratio of citing different disciplines, (4) analysis on immediacy index, peak time, and half-life of cited documents, and (5) analysis on impact of journals based on KJCR citation indicators. From this research, we could find the immediacy citation rate (average 2.36%), peak-time (average 1.7 years) and half-life (average 5.2 years) of cited journals in Korea. We also found that the average journal self-citation rate is more than 50% in every field. In sum, citing behavior of Korean scientists on Korean journals was comprehensively identified from this research.

• Journal of Food Hygiene and Safety
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• v.35 no.6
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• pp.607-617
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• 2020

Human bio-monitoring (HBM) data is a very important resource for tracking total exposure and concentrations of a parent chemical or its metabolites in human biomarkers. However, until now, it was difficult to execute the integration of different types of HBM data due to incompatibility problems caused by gaps in study design, chemical description and coding system between different sources in Korea. In this study, we presented a standardized code system and HBM knowledge model (KM) based on relational database modeling methodology. For this purpose, we used 11 raw datasets collected from the Ministry of Food and Drug Safety (MFDS) between 2006 and 2018. We then constructed the HBM database (DB) using a total of 205,491 concentration-related data points for 18,870 participants and 86 chemicals. In addition, we developed a summary report-type statistical analysis program to verify the inputted HBM datasets. This study will contribute to promoting the sustainable creation and versatile utilization of big-data for HBM results at the MFDS.

• Journal of Information Management
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• v.41 no.2
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• pp.95-110
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• 2010

We studied the methods for boosting a user participating service using KOSEN(The Global Network or Korean Scientists and Engineers) OpenLab service. OpenLab is a laboratory database of all the fields of science and technology, and it is different from other services in that basically it makes users register their information volunteerly. User participation service is not easy, so we made several strategies like promotion, event, and connection to other services. In result, we could collect about 4,300 laboratory database. Because user participation is a key concept for the Web 2.0, this study would contribute to the operation of the related services, especially to the services for scientists and engineers.

• Journal of the Korean Institute of Gas
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• v.4 no.4 s.12
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• pp.65-70
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• 2000

We develop the database program for accident cause analysis which can help to increase domestic safety custom and prevent recurrence of gas accident and analyze accidents easily The program developed in this study consists of two parts. one part uses accident case database applied if than rule, so it finds root causes by inference of some input values. The other uses Root Cause Analysis Map which divided human errors and equipment difficulties and so we get general root cause by reply some proper questions.

• Journal of Internet Computing and Services
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• v.2 no.5
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• pp.9-19
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• 2001

In this paper. we develop associate knowledge extraction method for finding and expanding user preference knowledge automatically from web document database. To reflect user interest or preferences, agent explores and extracts relevant information to central term involving the intent of users from the example documents. To do so, we apply association rule exploration data-mining method to the extraction of the relevant objects in the web documents. Also, to give the weighted-value to the extracted and relevant information, we present associate tag block-based weighting method. We applied to bioinformatics above associate knowledge extraction method to find related keywords.