DOI QR코드

DOI QR Code

[Retracted]Data management of academic information system using data quality diagnosis technique

[논문철회]데이터 품질진단 기법을 이용한 학사정보시스템의 데이터 관리

  • Ryu, Donghwan (Department of Computer Engineering, Paichai University) ;
  • Sung, Mikyung (Department of Computer Engineering, Paichai University) ;
  • Lee, Jieun (Business School, Sogang University) ;
  • Jung, Hoekyung (Department of Computer Engineering, Paichai University)
  • 류동환 ;
  • 성미경 ;
  • 이지은 ;
  • 정회경
  • Received : 2022.01.20
  • Accepted : 2022.01.27
  • Published : 2022.04.30

Abstract

The academic information system of a university is the core system of the university, and since it has to manage all the various activities in the university, such as student academic records, it becomes complicated every year and the data increases indiscriminately. As a result, the reliability of the data of the academic information system is lowered, which causes communication problems with users and may cause a major failure in the system. Therefore, in this paper, column attribute analysis, allowable value list analysis, string pattern analysis, date type analysis, and unique value analysis methods were designed for the academic information system using the data profiling technique of data quality management. In the implementation stage, the script was implemented using the above five analysis methods, and by executing the script, errors by type of the academic information system were found, the cause of the error was found and corrected inside the system, and the probability of internal system failure was lowered.

대학의 학사정보시스템은 대학의 핵심이 되는 시스템으로 학생의 학적 등 다양한 대학내 모든 활동을 관리해야 하므로 해마다 복잡해지고 데이터가 무분별하게 많아진다. 이에 따라 학사정보시스템의 데이터는 신뢰성이 저하되어 사용자와의 의사소통 문제가 발생하게 되고 시스템 내부에 큰 장애를 불러올 수 있기에 학사정보시스템의 데이터 검증 연구가 필요하다. 이에 본 논문에서는 학사정보시스템에 대해 데이터 품질관리의 데이터 프로파일링 기법을 이용하여 컬럼 속성 분석, 허용 값 목록 분석, 문자열 패턴 분석, 날짜 유형 분석, 유일 값 분석 방법으로 설계하였다. 구현 단계에서는 위의 5가지 분석 방법을 이용하여 스크립트를 구현하였고, 스크립트를 실행하여 학사정보시스템의 유형별 오류를 발견하여 오류의 원인을 시스템 내부에서 찾아 수정하였으며 내부시스템 장애 확률을 낮출 수 있었다.

Keywords

References

  1. S. O. Yoon, "A Study on the Main Issues of Artificial Intelligence-based Public Services," Korea Public Management Review, vol. 32, no. 2, pp. 83-104, Jun. 2018. https://doi.org/10.24210/KAPM.2018.32.2.004
  2. K. R. Seo, D. S. Kang, J. K. Eo, S. J. Park, J. M. Kim, and H. J. Kim, "A Study on Development Model of Public Intelligent Virtual Assistant Service based on Artificial Intelligence," in Proceedings of Symposium of the Korean Institute of Communications and Information Sciences, Jeju, pp. 890-891, Jun. 2017.
  3. J. h. Park, K. I. Yoon, and S. T. Min, "AI-based Chatbot System Technology Trend," Korea Information Processing Society Review, vol. 26, no. 2, pp. 107-117, Jul. 2019.
  4. J. T. Kim, H. G. Lee, and H. S. Kim, "Effective Generative Chatbot Model Trainable with a Small Dialogue Corpus," Journal of the Korea Information Science Society, vol. 46, no. 3, pp. 246-252, Mar. 2019.
  5. H. Y. Song, C. -W. Gwak, and Y. S. Lee, "Informational Chatbot System about COVID-19 based on Natural Language Processing," in Proceedings of the Korean Information and Communication Society Women's ICT Conference, Online, pp. 823-825, Aug. 2020.
  6. J. W. Lee, I. Y. Yeo, and H. K. Jung, "Document Analysis based Main Requisite Extraction System," Journal of the Korea Institute of Information and Communication Engineering, vol. 23, no. 4, pp. 401-406, Apr. 2019. https://doi.org/10.6109/JKIICE.2019.23.4.401
  7. J. W. Lee, G. Wu, and H. K. Jung, "Deep learning Document Analysis System Based on Keyworkd Frequency and Section Centrality Analysis," Journal of Information and Communication Convergence Engineering, vol. 19, no. 1, pp. 48-53, Mar. 2021. https://doi.org/10.6109/JICCE.2021.19.1.48
  8. J. W. Lee, H. J. Yang, and J. G. Kim, "Developing Scenario for Implementation of Counseling Chatbot and Verifying Usefulness," Journal of the Korea Contents Association, vol. 19, no. 4, pp. 12-29, Apr. 2019. https://doi.org/10.5392/JKCA.2019.19.04.012