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System Design to improve the Stability of the Education Information System

첫 페이지 보기
  • 발행기관
    국제인공지능학회(구 한국인터넷방송통신학회) 바로가기
  • 간행물
    The International Journal of Advanced Smart Convergence 바로가기
  • 통권
    Volume 14 Number 4 (2025.12)바로가기
  • 페이지
    pp.447-457
  • 저자
    Changbae Mun
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A481213

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원문정보

초록

영어
This study proposes a design methodology for a self-evolving system that can proactively detect failures occurring in educational information systems and automatically implement corrective measures. Because educational information systems exhibit highly regular and cyclical structures—driven by academic schedules and recurrent user activity patterns. To this end, the study designs an anomaly-detection framework that combines Poisson-distribution–based frequency analysis with regular-expression–based pattern grouping. Detected anomalous events are forwarded to an automated deployment process that generates and applies corrective logic, which is then incorporated into active service instances without downtime through suggested deployment structure. This study presents a reliability-enhancement model that shifts failure management in educational information systems from reactive recovery to a proactive and automated paradigm. In experiments involving 79 stability test cases, the proposed system achieved 88.5% precision in anomaly detection and a 94.9% success rate in automated deployment, demonstrating its practical effectiveness in improving system stability. The approach is broadly applicable not only to educational platforms but also to a wide range of public and private information-system environments.

목차

Abstract
1. Introduction
1.1 Purpose of the study
1.2 Research Gap and Research Purpose
2. Related Work
2.1 Research Trends in Educational Information Systems and LMS
2.2 Research on System Failure Analysis and Anomaly Detection
3. System Design Methodology
3.1 Design of Log-Based Statistical Pattern Analysis (Detection Architecture)
3.2 Design of the Mitigation and Automated Validation Architecture
4. Functional Experiment and Analysis
4.1 Statistical Anomaly Detection Experiment
4.2 Automated Mitigation and Stability Verification
5. Conclusion and Discussion
References

키워드

education information system learning management systems feedback Poisson Modeling Log-based Analysis Anomaly Detection Design Patterns Automated Deployment; Online learning management systems

저자

  • Changbae Mun [ Professor, Department of Computer Science Engineering, Hanyang Cyber University, Korea ] Corresponding Author

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    국제인공지능학회(구 한국인터넷방송통신학회) [The International Association for Artificial Intelligence]
  • 설립연도
    2000
  • 분야
    공학>전자/정보통신공학
  • 소개
    인터넷방송, 인터넷 TV , 방송 통신 네트워크 및 관련 분야에 대한 국내는 물론 국제적인 학술, 기술의 진흥발전에 공헌하고 지식 정보화 사회에 기여하고자 한다.

간행물

  • 간행물명
    The International Journal of Advanced Smart Convergence
  • 간기
    계간
  • pISSN
    2288-2847
  • eISSN
    2288-2855
  • 수록기간
    2012~2025
  • 십진분류
    KDC 326 DDC 380

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