• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.331-334
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• 2023

데이터 처리 과정에서 데이터 손실 및 장애 상황을 감지하고 예방하기 위한 모니터링 시스템의 필요성이 증가하고 있다. 복잡한 데이터 파이프라인에서 각 단계를 실시간으로 관찰하고 문제 상황에 신속하게 대응하기 위해서는 종합적인 모니터링 시스템을 구축하는 것이 중요하다. 본 논문에서는 엔터프라이즈 레벨의 파이프라인 모니터링 시스템을 개발하여 데이터 파이프라인의 안정성을 향상하고 데이터의 신뢰성을 높이고자 하였다. 모니터링을 데이터, 애플리케이션, 운영, 그리고 외부서비스 및 인프라 관점으로 분류 및 설계하고 각 관점에 따라 어떤 방식으로 활용되었는지 소개한다. 본 논문에서 개발한 모니터링 시스템을 통해 비즈니스 및 연구 분야의 데이터 처리 작업을 보다 효과적으로 관리하고, 문제 상황을 조기에 탐지하여 안정성을 향상시킬 수 있을 것으로 기대된다.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.337-340
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• 1991

The most difficult, time-consuming, and expensive task in building an ES (Expert System) is constructing and debugging its knowledge base. Our goals are to eliminate the knowledge-acquisition bottle-neck for ES creation in data rich situations and to make an ANN (Artificial Neural Network) model behave as much as possible like an ES. The ANN ES has many benifits: Once it has been learned, inference time is very short. It can provide a reasonable conclusion for insufficient input data. But it has also several demerits : Learning time is too long to converge. We cannot guarantee the convergence of its weights. We introduce an ANN ES model which makes most of its benefits and compensates its shortcomings.

• ETRI Journal
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• v.32 no.5
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• pp.676-686
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• 2010

Mining sequential patterns is an important research issue in data mining and knowledge discovery with broad applications. However, the existing sequential pattern mining approaches consider only binary frequency values of items in sequences and equal importance/significance values of distinct items. Therefore, they are not applicable to actually represent many real-world scenarios. In this paper, we propose a novel framework for mining high-utility sequential patterns for more real-life applicable information extraction from sequence databases with non-binary frequency values of items in sequences and different importance/significance values for distinct items. Moreover, for mining high-utility sequential patterns, we propose two new algorithms: UtilityLevel is a high-utility sequential pattern mining with a level-wise candidate generation approach, and UtilitySpan is a high-utility sequential pattern mining with a pattern growth approach. Extensive performance analyses show that our algorithms are very efficient and scalable for mining high-utility sequential patterns.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.53-55
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• 1993

This paper describes the development of a software that has the man expert knowledge, experience and inference. This software is helpful for selecting the motors and driving systems which are best fit for the applications. Developed software can automatically select the most reasonable motor driving systems, only if a semi-skilled engineer inputs the performance criteria for the applications and mechanical data. Expert system inference engine and knowledge-base are implemented by C programming language. Data-base was implemented from manufacturer's catalogues for DC motors and brushless DC motors. Efficiencies of the various motor driving systems are compared reference on the average efficiency depends on the operating profiles. Developed expert system was tested in various of applications to verify the reliability, quick and easy selecting of the motor driving systems.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.47-49
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• 2003

Automatic land use and land cover change (LUCC) detection via remotely sensed imagery has a wide application in the area of LUCC research, nature resource and environment monitoring and protection. Under the condition that one time (T1) data is existed land use and land cover maps, and another time (T2) data is remotely sensed imagery, how to detect change automatically is still an unresolved issue. This paper developed a land use and land cover class knowledge guided method for automatic change detection under this situation. Firstly, the land use and land cover map in T1 and remote sensing images in T2 were registered and superimposed precisely. Secondly, the remotely sensed knowledge database of all land use and land cover classes was constructed based on the unchanged parcels in T1 map. Thirdly, guided by T1 land use and land cover map, feature statistics for each parcel or pixel in RS images were extracted. Finally, land use and land cover changes were found and the change class was recognized through the automatic matching between the knowledge database of remote sensing information of land use & land cover classes and the extracted statistics in that parcel or pixel. Experimental results and some actual applications show the efficiency of this method.

• Soft Computing and Machine Intelligence
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• v.1 no.1
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• pp.1-10
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• 2021

Analysis and Tracking of bug reports is a challenging field in software repositories mining. It is one of the fundamental ways to explores a large amount of data acquired from defect tracking systems to discover patterns and valuable knowledge about the process of bug triaging. Furthermore, bug data is publically accessible and available of the following systems, such as Bugzilla and JIRA. Moreover, with robust machine learning (ML) techniques, it is quite possible to process and analyze a massive amount of data for extracting underlying patterns, knowledge, and insights. Therefore, it is an interesting area to propose innovative and robust solutions to analyze and track bug reports originating from different open source projects, including Mozilla and Eclipse. This research study presents an ML-based classification model to analyze and track bug defects for enhancing software engineering management (SEM) processes. In this work, Artificial Neural Network (ANN) and Naive Bayesian (NB) classifiers are implemented using open-source bug datasets, such as Mozilla and Eclipse. Furthermore, different evaluation measures are employed to analyze and evaluate the experimental results. Moreover, a comparative analysis is given to compare the experimental results of ANN with NB. The experimental results indicate that the ANN achieved high accuracy compared to the NB. The proposed research study will enhance SEM processes and contribute to the body of knowledge of the data mining field.

• Journal of the Korea Society of Computer and Information
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• v.26 no.5
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• pp.17-22
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• 2021

With the application of the Data 3 method, the scope of the use of pseudonym information has expanded. In the case of pseudonym information, a specific individual can be identified by linking and combining with various data, and personal information may be leaked due to incorrect use of the pseudonym information. In this paper, we propose the scope of use of data is subdivided and a differentiated pseudonym information processing method according to the scope. For the study, the formula was modified by using zero-knowledge proof among the pseudonym information processing methods, and when the proposed formula was applied, it was confirmed that the performance improved by an average of 10% in terms of verification time compared to the case of applying the formula of the existing zero-knowledge proof.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.851-860
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• 2021

The Knowledge Transfer (KT) for higher education institutions (HEIs) is boundless. Still and all, the members of the staff affiliated with these institutions do recognize an array of hitches in relation to KT practices. The study in question underscores social interactions, training, and Information and Communication Technology (ICT) as the primary barriers and treats them as the independent variables of the study. The study posits that inadequate management of the above-mentioned barriers would impact effective KT unfavorably. Besides, putting forth some striking solutions needed to fix the obstructions that hamper the adequate management of the KT exercises is another aim of the study. For data collection purposes, the study picks out higher education institutions (public) of the Quetta district. The reckoned sample size is 317 subjects. The research type that has been used is cross-sectional research and, in this context, the cross-sectional explanatory sequential design has been used. Concerning the findings of the paper, the results of PLS-SEM show positive and significant relationships of social interaction and training with knowledge transfer, while ICT shows an insignificant positive relationship with the knowledge transfer. The most influencing factor for the knowledge transfer is social interaction as suggested by social interaction theory.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.274-277
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• 2007

기업의 원활한 활동에 필수적인 요소는 살아 있는 정보의 공급이며, 기업 데이터 베이스는 데이터의 보관/흐름/활용 입장에서의 정보 인프라를 기반으로 하고 있다. 최근 기업 Knowledge Base 구축은 정보의 품질이 좌우하나 기업 정보의 양은 비대해져 데이터의 일관성이 결여되고 DB 의 성능은 점점 저하되고 있다. 본 고에서는 데이터 Hub 의 적용을 통해 얻을 수 있는 DB 성능 향상에 대해 정성적/정량적 관점으로 논의한다.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.232-235
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• 2015

Defect data contains experiential knowledge about specific work conditions. And the number of projects performed by a company is too limited for an individual to experience the various defects under the current complex construction environment. Therefore, in order to manage and prevent a reoccurrence of defects, a proper data feedback mechanism is required. However, most defect data are stored in unstructured ways, resulting in the fundamental problem of data utilization. In this paper, a new framework is proposed by using linked data technologies to improve defect data utilization. The target of this framework is to convert defect data to the ontology-based linked data format for sharing defect data from different data sources. To demonstrate it, some technical solutions are implemented by using real cases. The proposed approach can reduce data search time and improve the accuracy of search results as well. Moreover, the proposed approach can be applied to other domains that need to refer to external sources such as safety, specification, product, and regulation.