• The Korean Journal of Archival Studies
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• no.29
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• pp.175-223
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• 2011

A university is an organization charged with publicity and has accountability to the community for the operating process. Students account for a majority of members in a university. In universities, numerous creatures are pouring out every year and university students are major producers of these records. However, roles and functions of university students producing enormous amount of records as main agents of universities and focused concentration on produced records have not been made yet. It is reality that from the archival point of view, the importance of produced records of which main agents are university students has been relatively underestimated. In this background, this study attempted approach in archival point of view on records produced by university students, main agents. There are various types of records that university students produce such as records produced in the process of research and teaching as well as records produced in the process of various autonomy activities like clubs, students' associations. This study especially focused on university student autonomy activity process and placed emphasis on accountability securing measures on autonomy activity process of university students. To secure accountability of activities, records management should be based. Therefore, as a way to ensure accountability of unversity students autonomy activity, we tried to present records management systematization and records utilization measures. For this, a student body, a university student autonomy organization was analyzed and a student body of Myongji University Humanities Campus was selected as a specific target. First, to identify records management status, activities and organization and functions of the student body, we conducted an interview with the president of the student body. Through this, we analyzed the activities of the university student body and examined the necessity of accountability accordingly. Also, we derived the types and characteristics of records to be produced at each stage by analyzing the organization and functions of the student body of Myongji University. Like this, after deriving the types of production records according to the necessity, organization and functions of accountability and activities of the student body, we analyzed records management status of the present student body. First, to identify the general process status of activities of the student body, we analyzed activity process by stage of the student body of Myongji University. And we analyzed records management method of the student body and responsibility principal and conducted real condition analysis. Through this analysis, we presented the measures to ensure accountability of a university student body in three categories such as systematization of records management process, establishment of records management infrastructure, accountability guarantee measures. This study discussed accountability on society by analyzing activities and functions of a student body, targeting a student body, an autonomy organization of university students. And as a measure to secure accountability of a student body, we proposed a model for records management environment settlement. But in terms that a student body is an organization operated in one year basis, there is a limit that records management environment is hard to settle. This study pointed out this limit and was to provide clues when more active researches were carried out in the field of student records management in the future through presentation of student body records management model. Also, it is expected that the analysis results derived from this research will have significance in terms of school history arrangement and conservation.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.59-87
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• 2014

In MIS field, the researches on payment services are focused on adoption factors of payment service using behavior theories such as TRA(Theory of Reasoned Action), TAM(Technology Acceptance Model), and TPB (Theory of Planned Behavior). The previous researches presented various adoption factors according to types of payment service, nations, culture and so on even though adoption factors of identical payment service were presented differently by researchers. The payment service industry relatively has strong path dependency to the existing payment methods so that the research results on the identical payment service are different due to payment culture of nation. This paper aims to suggest a successful adoption factor of noble payment service regardless of nation's culture and characteristics of payment and prove it. In previous researches, common adoption factors of payment service are convenience, ease of use, security, convenience, speed etc. But real cases prove the fact that adoption factors that the previous researches present are not always critical to success to penetrate a market. For example, PayByPhone, NFC based parking payment service, successfully has penetrated to early market and grown. In contrast, Google Wallet service failed to be adopted to users despite NFC based payment method which provides convenience, security, ease of use. As shown in upper case, there remains an unexplained aspect. Therefore, the present research question emerged from the question: "What is the more essential and fundamental factor that should takes precedence over factors such as provides convenience, security, ease of use for successful penetration to market". With these cases, this paper analyzes four cases predicted on the following hypothesis and demonstrates it. "To successfully penetrate a market and sustainably grow, new payment service should find non-customer of the existing payment service and provide noble payment method so that they can use payment method". We give plausible explanations for the hypothesis using multiple case studies. Diners club, Danal, PayPal, Square were selected as a typical and successful cases in each category of payment service. The discussion on cases is primarily non-customer analysis that noble payment service targets on to find the most crucial factor in the early market, we does not attempt to consider factors for business growth. We clarified three-tier non-customer of the payment method that new payment service targets on and elaborated how new payment service satisfy them. In case of credit card, this payment service target first tier of non-customer who can't pay for because they don't have any cash temporarily but they have regular income. So credit card provides an opportunity which they can do economic activities by delaying the date of payment. In a result of wireless phone payment's case study, this service targets on second of non-customer who can't use online payment because they concern about security or have to take a complex process and learn how to use online payment method. Therefore, wireless phone payment provides very convenient payment method. Especially, it made group of young pay for a little money without a credit card. Case study result of PayPal, online payment service, shows that it targets on second tier of non-customer who reject to use online payment service because of concern about sensitive information leaks such as passwords and credit card details. Accordingly, PayPal service allows users to pay online without a provision of sensitive information. Final Square case result, Mobile POS -based payment service, also shows that it targets on second tier of non-customer who can't individually transact offline because of cash's shortness. Hence, Square provides dongle which function as POS by putting dongle in earphone terminal. As a result, four cases made non-customer their customer so that they could penetrate early market and had been extended their market share. Consequently, all cases supported the hypothesis and it is highly probable according to 'analytic generation' that case study methodology suggests. We present for judging the quality of research designs the following. Construct validity, internal validity, external validity, reliability are common to all social science methods, these have been summarized in numerous textbooks(Yin, 2014). In case study methodology, these also have served as a framework for assessing a large group of case studies (Gibbert, Ruigrok & Wicki, 2008). Construct validity is to identify correct operational measures for the concepts being studied. To satisfy construct validity, we use multiple sources of evidence such as the academic journals, magazine and articles etc. Internal validity is to seek to establish a causal relationship, whereby certain conditions are believed to lead to other conditions, as distinguished from spurious relationships. To satisfy internal validity, we do explanation building through four cases analysis. External validity is to define the domain to which a study's findings can be generalized. To satisfy this, replication logic in multiple case studies is used. Reliability is to demonstrate that the operations of a study -such as the data collection procedures- can be repeated, with the same results. To satisfy this, we use case study protocol. In Korea, the competition among stakeholders over mobile payment industry is intensifying. Not only main three Telecom Companies but also Smartphone companies and service provider like KakaoTalk announced that they would enter into mobile payment industry. Mobile payment industry is getting competitive. But it doesn't still have momentum effect notwithstanding positive presumptions that will grow very fast. Mobile payment services are categorized into various technology based payment service such as IC mobile card and Application payment service of cloud based, NFC, sound wave, BLE(Bluetooth Low Energy), Biometric recognition technology etc. Especially, mobile payment service is discontinuous innovations that users should change their behavior and noble infrastructure should be installed. These require users to learn how to use it and cause infra-installation cost to shopkeepers. Additionally, payment industry has the strong path dependency. In spite of these obstacles, mobile payment service which should provide dramatically improved value as a products and service of discontinuous innovations is focusing on convenience and security, convenience and so on. We suggest the following to success mobile payment service. First, non-customers of the existing payment service need to be identified. Second, needs of them should be taken. Then, noble payment service provides non-customer who can't pay by the previous payment method to payment method. In conclusion, mobile payment service can create new market and will result in extension of payment market.

• The Journal of the Korean life insurance medical association
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• v.24
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• pp.49-78
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• 2005

Ⅰ. 연구(硏究) 배경(背景) 및 목적(目的) o 우리나라 보험시장의 세대가입율은 86%로 보험시장 성숙기에 진입하였으며 기존의 전통적인 전업채널에서 방카슈랑스의 도입, 온라인전문보험사의 출현, TM 영업의 성장세 等멀티채널로 진행되고 있음 o LTC(장기간병), CI(치명적질환), 실손의료보험 등(等)선 진형 건강상품의 잇따른 출시로 보험리스크 관리측면에서 언더라이팅의 대비가 절실한 시점임 o 상품과 마케팅 等언더라이팅 측면에서 매우 밀접한 영역의 변화에 발맞추어 언더라이팅의 인수기법의 선진화가 시급히 요구되는 상황하에서 위험을 적절히 분류하고 평가하는 선진적 언더라이팅 기법 구축이 필수 적임 o 궁극적으로 고객의 다양한 보장니드 충족과 상품, 마케팅, 언더라이팅의 경쟁력 강화를 통한 보험사의 종합이익 극대화에 기여할 수 있는 방안을 모색하고자 함 Ⅱ. 선진보험시장(先進保險市場)Risk 세분화사례(細分化事例) 1. 환경적위험(環境的危險)에 따른 보험료(保險料) 차등(差等) (1) 위험직업 보험료 할증 o 미국, 유럽등(等) 대부분의 선진시장에서는 가입당시 피보험자의 직업위험도에 따라 보험료를 차등 적용중(中)임 o 가입하는 보장급부에 따라 직업 분류방법 및 할증방식도 상이하며 일반사망과 재해사망,납입면제, DI에 대해서 별도의 방법을 사용함 o 할증적용은 표준위험율의 일정배수를 적용하여 할증 보험료를 산출하거나, 가입금액당 일정한 추가보험료를 적용하고 있음 - 광부의 경우 재해사망 가입시 표준위험율의 300% 적용하며, 일반사망 가입시 $1,000당 $2.95 할증보험료 부가 (2) 위험취미 보험료 할증 o 취미와 관련 사고의 지속적 다발로 취미활동도 위험요소로 인식되어 보험료를 차등 적용중(中)임 o 할증보험료는 보험가입금액당 일정비율로 부가(가입 금액과 무관)하며, 신종레포츠 등(等)일부 위험취미는 통계의 부족으로 언더라이터가 할증율 결정하여 적용함 - 패러글라이딩 년(年)$26{\sim}50$회(回) 취미생활의 경우 가입금액 $1,000당 재해사망 $2, DI보험 8$ 할증보험료 부가 o 보험료 할증과는 별도로 위험취미에 대한 부담보를 적용함. 위험취미 활동으로 인한 보험사고 발생시 사망을 포함한 모든 급부에 대한 보장을 부(不)담보로 인수함. (3) 위험지역 거주/ 여행 보험료 할증 o 피보험자가 거주하고 있는 특정국가의 임시 혹은 영구적 거주시 기후위험, 거주지역의 위생과 의료수준, 여행위험, 전쟁과 폭동위험 등(等)을 고려하여 평가 o 일반사망, 재해사망 등(等)보장급부별로 할증보험료 부가 또는 거절 o 할증보험료는 보험全기간에 대해 동일하게 적용 - 러시아의 경우 가입금액 $1,000당 일반사망은 2$의 할증보험료 부가, 재해사망은 거절 (4) 기타 위험도에 대한 보험료 차등 o 비행관련 위험은 세가지로 분류(항공운송기, 개인비행, 군사비행), 청약서, 추가질문서, 진단서, 비행이력 정보를 바탕으로 할증보험료를 부가함 - 농약살포비행기조종사의 경우 가입금액 $1,000당 일반사망 6$의 할증보험료 부가, 재해사망은 거절 o 미국, 일본등(等)서는 교통사고나 교통위반 관련 기록을 활용하여 무(無)사고운전자에 대해 보험료 할인(우량체 위험요소로 활용) 2. 신체적위험도(身體的危險度)에 따른 보험료차등(保險料差等) (1) 표준미달체 보험료 할증 1) 총위험지수 500(초과위험지수 400)까지 인수 o 300이하는 25점단위, 300점 초과는 50점 단위로 13단계로 구분하여 할증보험료를 적용중(中)임 2) 삭감법과 할증법을 동시 적용 o 보험금 삭감부분만큼 할증보험료가 감소하는 효과가 있어 청약자에게 선택의 기회를 제공할수 있으며 고(高)위험 피보험자에게 유용함 3) 특정암에 대한 기왕력자에 대해 단기(Temporary)할증 적용 o 질병성향에 따라 가입후 $1{\sim}5$년간 할증보험료를 부가하고 보험료 할증 기간이 경과한 후에는 표준체보험료를 부가함 4) 할증보험료 반환옵션(Return of the extra premium)의 적용 o 보험계약이 유지중(中)이며, 일정기간 생존시 할증보험료가 반환됨 (2) 표준미달체 급부증액(Enhanced annuity) o 영국에서는 표준미달체를 대상으로 연금급부를 증가시킨 증액형 연금(Enhanced annuity) 상품을 개발 판매중(中)임 o 흡연, 직업, 병력 등(等)다양한 신체적, 환경적 위험도에 따라 표준체에 비해 증액연금을 차등 지급함 (3) 우량 피보험체 가격 세분화 o 미국시장에서는 $8{\sim}14$개 의적, 비(非)의적 위험요소에 대한 평가기준에 따라 표준체를 최대 8개 Class로 분류하여 할인보험료를 차등 적용 - 기왕력, 혈압, 가족력, 흡연, BMI, 콜레스테롤, 운전, 위험취미, 거주지, 비행력, 음주/마약 등(等) o 할인율은 회사, Class, 가입기준에 따라 상이(최대75%)하며, 가입연령은 최저 $16{\sim}20$세, 최대 $65{\sim}75$세, 최저보험금액은 10만달러(HIV검사가 필요한 최저 금액) o 일본시장에서는 $3{\sim}4$개 위험요소에 따라 $3{\sim}4$개 Class로 분류 우량체 할인중(中)임 o 유럽시장에서는 영국 등(等)일부시장에서만 비(非)흡연할인 또는 우량체할인 적용 Ⅲ. 국내보험시장(國內保險市場) 현황(現況)및 문제점(問題點) 1. 환경적위험도(環境的危險度)에 따른 가입한도제한(加入限度制限) (1) 위험직업 보험가입 제한 o 업계공동의 직업별 표준위험등급에 따라 각 보험사 자체적으로 위험등급별 가입한도를 설정 운영중(中)임. 비(非)위험직과의 형평성, 고(高)위험직업 보장 한계, 수익구조 불안정화 등(等)문제점을 내포하고 있음 - 광부의 경우 위험1급 적용으로 사망 최대 1억(億), 입원 1일(日) 2만원까지 제한 o 금융감독원이 2002년(年)7월(月)위험등급별 위험지수를 참조 위험율로 인가하였으나, 비위험직은 70%, 위험직은 200% 수준으로 산정되어 현실적 적용이 어려움 (2) 위험취미 보험가입 제한 o 해당취미의 직업종사자에 준(準)하여 직업위험등급을 적용하여 가입 한도를 제한하고 있음. 추가질문서를 활용하여 자격증 유무, 동호회 가입등(等)에 대한 세부정보를 입수하지 않음 - 패러글라이딩의 경우 위험2급을 적용, 사망보장 최대 2 억(億)까지 제한 (3) 거주지역/ 해외여행 보험가입 제한 o 각(各)보험사별로 지역적 특성상 사고재해 다발 지역에 대해 보험가입을 제한하고 있음 - 강원, 충청 일부지역 상해보험 가입불가 - 전북, 태백 일부지역 입원급여금 1일(日)2만원이내 o 해외여행을 포함한 해외체류에 대해서는 일정한 가입 요건을 정하여 운영중(中)이며, 가입한도 설정 보험가입을 제한하거나 재해집중보장 상품에 대해 거절함 - 러시아의 경우 단기체류는 위험1급 및 상해보험 가입 불가, 장기 체류는 거절처리함 2. 신체적위험도(身體的危險度)에 따른 인수차별화(引受差別化) (1) 표준미달체 인수방법 o 체증성, 항상성 위험에 대한 초과위험지수를 보험금삭감법으로 전환 사망보험에 적용(최대 5년(年))하여 5년(年)이후 보험 Risk노출 심각 o 보험료 할증은 일부 회사에서 주(主)보험 중심으로 사용중(中)이며, 총위험지수 300(8단계)까지 인수 - 주(主)보험 할증시 특약은 가입 불가하며, 암 기왕력자는 대부분 거절 o 신체부위 39가지, 질병 5가지에 대해 부담보 적용(입원, 수술 등(等)생존급부에 부담보) (2) 비(非)흡연/ 우량체 보험료 할인 o 1999년(年)최초 도입 이래 $3{\sim}4$개의 위험요소로 1개 Class 운영중(中)임 S생보사의 경우 비(非)흡연우량체, 비(非)흡연표준체의 2개 Class 운영 o 보험료 할인율은 회사, 상품에 따라 상이하며 최대 22%(영업보험료기준)임. 흡연여부는 뇨스틱을 활용 코티닌테스트를 실시함 o 우량체 판매는 신계약의 $2{\sim}15%$수준(회사의 정책에 따라 상이) Ⅳ. 언더라이팅 기법(技法) 선진화(先進化) 방안(方案) 1. 직업위험도별 보험료 차등 적용 o 생 손보 직업위험등급 일원화와 연계하여 3개등급으로 위험지수개편, 비위험직 기준으로 보험요율 차별적용 2. 위험취미에 대한 부담보 적용 o 해당취미를 원인으로 보험사고(사망포함) 발생시 부담보 제도 도입 3. 표준미달체 인수기법 선진화를 통한 인수범위 대폭 확대 o 보험료 할증법 적용 확대를 통한 Risk 헷지로 총위험지수 $300{\rightarrow}500$으로 확대(거절건 최소화) 4. 보험료 할증법 보험금 삭감 병행 적용 o 삭감기간을 적용한 보험료 할증방식 개발, 고객에게 선택권 제공 5. 기한부 보험료할증 부가 o 위암, 갑상선암 등(等)특정암의 성향에 따라 위험도가 높은 가입초기에 평준할증보험료를 적용하여 인수 6. 보험료 할증법 부가특약 확대 적용, 부담보 병행 사용 o 정기특약 등(等)사망관련 특약에 할증법 확대, 생존급부 특약은 부담보 7. 표준체 고객 세분화 확대 o 콜레스테롤, HDL 등(等)위험평가요소 확대를 통한 Class 세분화 Ⅴ. 기대효과(期待效果) 1. 고(高)위험직종사자, 위험취미자, 표준미달체에 대한 보험가입 문호개방 2. 보험계약자간 형평성 제고 및 다양한 고객의 보장니드에 부응 3. 상품판매 확대 및 Risk헷지를 통한 수입보험료 증대 및 사차익 개선 4. 본격적인 가격경쟁에 대비한 보험사 체질 개선 5. 회사 이미지 제고 및 진단 거부감 해소, 포트폴리오 약화 방지 Ⅵ. 결론(結論) o 종래의 소극적이고 일률적인 인수기법에서 탈피하여 피보험자를 다양한 측면에서 위험평가하여 적정 보험료 부가와 합리적 가입조건을 제시하는 적절한 위험평가 수단을 도입하고, o 언더라이팅 인수기법의 선진화와 함께 언더라이팅 인력의 전문화, 정보입수 및 시스템 인프라의 구축 등이 병행함으로써, o 보험사의 사차손익 관리측면에서 뿐만 아니라 보험시장 개방 및 급변하는 보험환경에 대비한 한국 생보언더라이팅 경쟁력 강화 및 언더라이터의 글로벌화에도 크게 기여할 것임.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Journal of Technology Innovation
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• v.17 no.1
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• pp.1-48
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• 2009

Recently we have found some symptoms that R&D fiscal incentives might not work well what it has intended through the analysis of current statistics of firm's R&D data. Firstly, we found that the growth rate of R&D investment in private sector during the recent decade has been slowdown. The average of growth rate (real value) of R&D investment is 7.1% from 1998 to 2005, while it was 13.9% from 1980 to 1997. Secondly, the relative share of R&D investment of SME has been decreased to 21%('05) from 29%('01), even though the tax credit for SME has been more beneficial than large size firm, Thirdly, The R&D expenditure of large size firms (besides 3 leading firms) has not been increased since late of 1990s. We need to find some evidence whether fiscal incentives are effective in increasing firm's R&D investment. To analyse econometric model we use firm level unbalanced panel data for 4 years (from 2002 to 2005) derived from MOST database compiled from the annual survey, "Report on the Survey of Research and Development in Science and Technology". Also we use fixed effect model (Hausman test results accept fixed effect model with 1% of significant level) and estimate the model for all firms, large firms and SME respectively. We have following results from the analysis of econometric model. For large firm: i ) R&D investment responds elastically (1.20) to sales volume. ii) government R&D subsidy induces R&D investment (0.03) not so effectively. iii) Tax price elasticity is almost unity (-0.99). iv) For large firm tax incentive is more effective than R&D subsidy For SME: i ) Sales volume increase R&D investment of SME (0.043) not so effectively. ii ) government R&D subsidy is crowding out R&D investment of SME not seriously (-0.0079) iii) Tax price elasticity is very inelastic (-0.054) To compare with other studies, Koga(2003) has a similar result of tax price elasticity for Japanese firm (-1.0036), Hall((l992) has a unit tax price elasticity, Bloom et al. (2002) has $-0.354{\sim}-0.124$ in the short run. From the results of our analysis we recommend that government R&D subsidy has to focus on such an areas like basic research and public sector (defense, energy, health etc.) not overlapped private R&D sector. For SME government has to focus on establishing R&D infrastructure. To promote tax incentive policy, we need to strengthen the tax incentive scheme for large size firm's R&D investment. We recommend tax credit for large size film be extended to total volume of R&D investment.