• Proceedings of the Korea Information Processing Society Conference
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• 2003.11c
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• pp.1437-1440
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• 2003

의사결정지원시스템으로서의 DW는 기업정보시스템에서 두뇌역할을 담당하는 만큼 전략적 중요도에 있어서 매우 중요한 역할을 차지하고 있다. 이러한 추세와 더불어 많은 기업들이 DW구축에 엄청난 예산을 투입하여 시스템을 구축하지만 프로젝트의 실패 역시 흔하게 찾아 볼 수 있는 사실이다. 이러한 실패의 주요인은 기술적인 문제에서 발생하기 보다는 낮은 시스템 활용도와 명확하지 않은 분석요구사항에서 주요원인을 찾아 볼 수 있는데 이는 구축단계에서 이용자와 이해관계자들이 DW의 시스템적 목적을 제대로 이해하지 못하여 사용자 관점의 요구사항을 제대로 제시하지 못하고 이에 따라 시스템의 활용도 역시 낮아지는 데에서 본질적인 원인을 찾을 수 있다. 본 논문에서는 시스템의 사용자가 요구사항을 적극적으로 제시하고 시스템에 끊임없는 관심을 같도록 유도하여 이용자의 요구사항을 충족시킨 수 있는 정확한 주제분야와 분석관점을 발굴함과 통시에 시스템의 활용도를 높이기 위한 방안으로 정형화되고 주기적인 분석정보를 제시하기 위하여 정적 Reporting을 위한 Web Reporting Tool과 함께 시스템의 이용자와 주기적으로 Communication을 유지하여 시스템에 지속적으로 관심을 갖도록 하기 위한 상호 Communication기능을 통한 문제의 해결방안을 제시하고자 한다.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.79-81
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• 2005

공간 데이터 웨어하우스 구축기는 운영 데이터베이스의 데이터를 추출하여, 공간 데이터 웨어하우스 서버에 적재하는 과정을 효율적으로 관리하는 시스템이다. 구축기는 적재로 인한 서버의 부하를 줄이기 위하여 적재할 데이터를 임시 저장하는데, 기존 기법은 적재할 데이터를 하나의 저장 공간에 관리한다. 따라서 서버가 특정 차원 테이블에 대한 실시간 질의처리를 위해 특정 차원 테이블의 즉시 적재를 요청할 경우, 구축기는 이를 위해 임시 저장한 모든 데이터를 검색하므로 처리비용이 증가한다. 또한, 하나의 저장공간에 적재할 데이터를 유지하여 서버에 데이터 적재 시, 저장을 위해 혼합된 데이터를 분석하는 비용이 증가한다. 본 논문에서는 공간 데이터 웨어하우스 구축기에서 추출된 데이터의 효율적인 적재를 위한 테이블 단위의 데이터 관리 기법을 제안한다. 제안 기법은 운영 데이터베이스로부터 추출한 데이터를 서버에 적재할 차원 테이블 단위로 구축기에서 각각 다른 저장 공간에 관리한다. 따라서 테이블 단위의 데이터 관리로 실시간 질의처리를 위한 특정 차원 테이블의 즉시 적재 비용이 감소하며, 테이블 단위의 병렬전송이 가능하여 전송비용이 감소한다. 또한, 서버로 전송된 데이터는 테이블 단위의 벌크 삽입이 가능하여 적재시간이 감소한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1708-1734
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• 2022

In the development of logistics system, the breakthrough of important technologies such as technology platform for logistics information management and control is the key content of the study. Based on Javascript and JQuery, the logistics system realizes real-time monitoring, collection of historical status data, statistical analysis and display, intelligent recommendation and other functions. In order to strengthen the cooperation of warehouse storage, enhance the utilization rate of resources, and achieve the purpose of real-time and visual supervision of transportation equipment and cargo tracking, this paper studies the VNF dynamic deployment and SFC routing problem in the network load change scenario based on the logistics system. The BIP model is used to model the VNF dynamic deployment and routing problem. The optimization objective is to minimize the total cost overhead generated by each SFCR. Furthermore, the application of the SFC mapping algorithm in the routing topology solving problem is proposed. Based on the concept of relative cost and the idea of topology transformation, the SFC-map algorithm can efficiently complete the dynamic deployment of VNF and the routing calculation of SFC by using multi-layer graph. In the simulation platform based on the logistics system, the proposed algorithm is compared with VNF-DRA algorithm and Provision Traffic algorithm in the network receiving rate, throughput, path end-to-end delay, deployment number, running time and utilization rate. According to the test results, it is verified that the test results of the optimization algorithm in this paper are obviously improved compared with the comparison method, and it has higher practical application and promotion value.

• Journal of Intelligence and Information Systems
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• v.11 no.3
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• pp.57-67
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• 2005

Data mining techniques enable us to generate useful information for decision support from the data sources which are generated and accumulated in the process of routine organizational management activities. College administration system is a typical example that produces a warehouse of student records as each and every student enters a college and undertakes the curricular and extracurricular activities. So far, these data have been utilized to a very limited student service purposes, such as issuance of transcripts, graduation evaluation, GPA calculation, etc. In this paper, we utilized Holland career search test results, TOEIC score, course work list and GPA score as the input for data mining, and we were able to generate knowledge and rules with regard to the college road map advisory service. Factor analysis and AHP(Analytic Hierarchy Process) were the primary techniques deployed in the data mining process. Since these data mining techniques are very powerful in processing and discovering useful knowledge and information from large scale student databases, we can expect a highly sophisticated student advisory knowledge and services which may not be obtained from the human student advice experts.

• The KIPS Transactions:PartD
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• v.11D no.3
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• pp.519-532
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• 2004

With the explosion of bioinformatics data such proteins and genes, biologists need a integrated system to analyze and organize large datasets that interact with heterogeneous types of biological data. In this paper, we propose a integration system based on a mediated data warehouse architecture using a XML model in order to combine protein related data at biology laboratories. A fact constellation model in this system is used at a common model for integration and an integrated schema it translated to a XML schema. In addition, to track source changes and provenance of data in an integrated database employ incremental update and management of sequence version. This paper shows modeling of integration for protein structures, sequences and classification of structures using the proposed system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.8
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• pp.1861-1867
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• 2010

A datawarehouse is generally used in organizations for decision and policy making. And In a distribute environment when a new system is added, there needs considerable amount of time and cost due to the difference between the systems. Therefore, to solve this matter. Firstly, heterogeneous data structures can be handled by creating abstract queries according to the standard schema and by separating the queries using XMDR. Secondly, metadata dictionary which defines synonyms of metadata and methods for data expression is used to overcome difference of definition and expression of data. Especially, work presented in this thesis provides standardized information for data integration and minimizing the effects of integration on local systems in discrete environments using XMDR to create information of data warehouse in realtime.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.9
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• pp.349-354
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• 2014

Since Hadoop which is the Big-data processing platform was announced, SQL-on-Hadoop is the spotlight as the technique to analyze data using SQL on Hadoop. Tajo created by Korean programmers has recently been promoted to Top-Level-Project status by the Apache in April and has been paid attention all around world. Despite a sensible change caused by Hadoop's appearance in DW market, researches of those performance is insufficient. Thus, this study has been conducted to help choose a DW solution based on SQL-on-Hadoop as progressing the test on comparison analysis of RDBMS and Tajo. It has shown that Tajo based on Hadoop is more superior than RDBMS if it is used with accurate strategy. In addition, open-source project Tajo is expected not only to achieve improvements in technique due to active participation of many developers but also to be in charge of an important role of DW in the filed of data analysis.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.05a
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• pp.266-273
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• 2005

Data mining techniques enable us to generate useful information for decision support from the data sources which are generated and accumulated in the process of routine organizational management activities. College administration system is a typical example that produces a warehouse of student records as each and every student enters a college and undertakes the curricular and extracurricular activities. So far, these data have been utilized to a very limited student service purposes, such as issuance of transcripts, graduation evaluation, GPA calculation, etc. In this paper, we utilize Holland career search test results, TOEIC score, course work list, and GPA score as the input for data mining and generation the student advisory information. Factor analysis, AHP(Analytic Hierarchy Process), artificial neural net, and CART(Classification And Regression Tree) techniques are deployed in the data mining process. Since these data mining techniques are very powerful in processing and discovering useful knowledge and information from large scale student databases, we can expect a highly sophisticated student advisory knowledge and services which may not be obtained with the human student advice experts.

• The Journal of Information Systems
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• v.30 no.3
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• pp.21-45
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• 2021

Purpose This paper analyzed the impacts of domestic stock market by a global pandemic such as COVID-19. We investigated how the overall pattern of the stock market changed due to the impact of the COVID-19 pandemic. In particular, we analyzed in depth the pattern of stock price, as well, tried to find what factors affect on stock market index(KOSPI) in the healthcare industry due to the COVID-19 pandemic. Design/methodology/approach We built a data warehouse from the databases in various industrial and economic fields to analyze the changes in the KOSPI due to COVID-19, particularly, the changes in the healthcare industry centered on bio-medicine. We collected daily stock price data of the KOSPI centered on the KOSPI-200 about two years before and one year after the outbreak of COVID-19. In addition, we also collected various news related to COVID-19 from the stock market by applying text mining techniques. We designed four experimental data sets to develop decision tree-based prediction models. Findings All prediction models from the four data sets showed the significant predictive power with explainable decision tree models. In addition, we derived significant 10 to 14 decision rules for each prediction model. The experimental results showed that the decision rules were enough to explain the domestic healthcare stock market patterns for before and after COVID-19.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.293-300
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• 2016

Traditional databases exploit a record-based model, where the attributes of a record are placed contiguously in a slow hard disk to achieve high performance. On the other hand, for read-intensive data analysis systems, the column-based compressed database has become a proper model because of its superior read performance. Currently, flash memory SSD is largely recognized as the preferred storage media for high-speed analysis systems. This paper introduces a compressed column-storage model and proposes a new index and its data management scheme for a high-capacity data warehouse system. The proposed index management scheme is based on the asymmetric index duplication and achieves superior search performance using the master index and compact index, particularly for large read-mostly databases. In addition, the data management scheme contributes to the read performance and high reliability by compressing the related columns and replicating them in two mirrored SSD. Based on the results of the performance evaluation under the high workload conditions, the data management scheme outperforms the traditional scheme in terms of the search throughput and response time.