Journal of Korean Society for Quality Management (품질경영학회지)

Korean Society for Quality Management (한국품질경영학회)
권호 표지
DOI QR코드

DOI QR Code

Intelligent Data Governance for the Federated Integration of Air Quality Databases in the Railway Industry

철도 산업의 공기 질 데이터베이스 연합형 통합을 위한 지능형 데이터 거버넌스

  • 김민정 (한국철도기술연구원) ;
  • 원종운 (한국철도기술연구원) ;
  • 박상찬 (한국뉴욕주립대학교, 기술경영학과) ;
  • 박가영 (한국철도기술연구원)
  • Received : 2022.11.22
  • Accepted : 2022.12.02
  • Published : 2022.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Purpose: In this paper, we will discuss 1) prioritizing databases to be integrated; 2) which data elements should be emphasized in federated database integration; and 3) the degree of efficiency in the integration. This paper aims to lay the groundwork for building data governance by presenting guidelines for database integration using metrics to identify and evaluate the capabilities of the UK's air quality databases. Methods: This paper intends to perform relative efficiency analysis using Data Envelope Analysis among the multi-criteria decision-making methods. In federated database integration, it is important to identify databases with high integration efficiency when prioritizing databases to be integrated. Results: The outcome of this paper aims not to present performance indicators for the implementation and evaluation of data governance, but rather to discuss what criteria should be used when performing 'federated integration'. Using Data Envelope Analysis in the process of implementing intelligent data governance, authors will establish and present practical strategies to discover databases with high integration efficiency. Conclusion: Through this study, it was possible to establish internal guidelines from an integrated point of view of data governance. The flexiblity of the federated database integration under the practice of the data governance, makes it possible to integrate databases quickly, easily, and effectively. By utilizing the guidelines presented in this study, authors anticipate that the process of integrating multiple databases, including the air quality databases, will evolve into the intelligent data governance based on the federated database integration when establishing the data governance practice in the railway industry.

Keywords

Acknowledgement

본 연구는 한국철도기술연구원 주요사업 "지능형 철도·교통 기술개발을 위한 인공지능 지원 플랫폼 개발"의 지원을 받아 수행된 연구입니다.

References

  1. Allen, M, and Cervo, D. 2015. Strategy, Scope, and Approach In Allen, M., and Cervo (Eds.), Multi-Domain Master Data Management: Advanced MDM and Data Governance in Practice, (pg.244). Morgan Kaufmann. https://doi.org/10.1016/B978-0-12-800835-5.00001-4.
  2. Bryce, CL., Engberg, JB., and Wholey, DR. 2000. Comparing the agreement among alternative models in evaluating HMO efficiency. Health Serv Res. 35(2):509-28. PMID: 10857474; PMCID: PMC1089131.
  3. Charnes, A., Cooper, W., Lewin, A. Y., and Seiford, L. M., 1997. Data envelopment analysis theory, methodology and applications. Journal of the Operational Research society 48(3):332-333. https://doi.org/10.1038/sj/jors/2600342
  4. Charnes, A., Cooper,. WW, and Rhodes, E. 1987, Measuring the efficiency of decision-making units. European Journal of Operational Research 2:429-44. https://doi.org/10.1016/0377-2217(78)90138-8
  5. Cheon, BJ., and Kim, HW. 2016. An Exploratory Study on the Sharing and Application of Public Open Big Data Informatization Policy 24(3):27-41.
  6. Cho, JH. and Pyun, JB 2022. A Study on the Implementation Plan for Public Service Quality Management Applying the ISO 18091 Framework. J Korean Soc Qual Manag 50(1): 1-19.DOI: https://doi.org/10.7469/JKSQM.2022.50.1.1
  7. Cho, YM., Park, KS., Park, DS., Koo, HY., Bin, HK., and Kim, HM. 2010. Strategies for Improvement of Air Quality in Subway Stations, The Korean Society for Railway 2010 Spring Conferences Proceedings pp. 2117-2121.
  8. Choi, B. and Yoon, JJ 2018. A Study on Policies to Revitalize the Public Big Data in Seoul. The Seoul Institute Policy Report. The Seoul Institute. p.147.
  9. Choi, YH., Kim, TJ., Soh, SH., and Jeong, SH. 2015. A Study on the Improvement Plan for Data Management System. Ministry of the Interior and Safety (MOIS) Research Report.
  10. Cooper, S. A., Smiley, E., Finlayson, J., Jackson, A., Allan, L., Williamson, A., & Morrison, J. 2007. The prevalence, incidence, and factors predictive of mental ill-health in adults with profound intellectual disabilities. Journal of Applied Research in Intellectual Disabilities 20(6):493-501. https://doi.org/10.1111/j.1468-3148.2007.00401.x
  11. Informatica, Best Practices for Intelligent Data Governance. White Papers. Retreived from https://www.informatica.com/kr/lp/best-practices-azure-data-governance_4237.html.
  12. ITF, 2021, Reporting Mobility Data: Good Governance Principles and Practices, International Transport Forum Policy Papers, No. 101, OECD Publishing, Paris
  13. Joen, S., Lim, H., Park, S., & Jung, H. 2022. Indoor Air Data Meter and Monitoring System. Journal of the Korea Institute of Information and Communication Engineering 26(1):140-145.
  14. Jomthanachai, S., Wong, W.-P., & Lim, C.-P. 2021. A Coherent Data Envelopment Analysis to Evaluate the Efficiency of Sustainable Supply Chains. IEEE Trans. Eng. Manag. 1-18.
  15. Jomthanachai, S., Wong, WP., Soh, KL, and Lim, CH. 2021. A global trade supply chain vulnerability in COVID-19 pandemic: An assessment metric of risk and resilience-based efficiency of CoDEA method. Research in Transportation Economics 93(4):101166. https://doi.org/10.1016/j.retrec.2021.101166.
  16. Kans, M. and Ingwald, A. 2021, Service-based business models in the Swedish railway industry, Journal of Quality in Maintenance Engineering, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JQME-06-2021-0051.
  17. Kans, M., Galar, D., and Thaduri, A. 2016, Maintenance 4.0 in railway transportation industry, Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015), Springer. pp.317-331.
  18. Kim, JH., Jeong, SY., Lee, SB., Choi, JA. 2020. Agenda and Key Issues in Data Governance. Korea Institued of Public Administration. Sep 2020 Issue No.5.
  19. Kim, MJ., and Sagong, HS. 2015. Era of Data Technology: Geospatial Information Establishment and Management Plan. KRIHS Policy Brief. 530:1-6.
  20. Ko, DW., Park, HU, and Park, JW. 2011. An Analysis of the efficiency of public sport facilities in local governments. Korean Society of Sport Policy 9(3):1-11.
  21. Lewis, H.F., and Sexton, T.R. 2004. Data Envelopment Analysis with Reverse Inputs and Outputs. Journal of Productivity Analysis 21:113-132. https://doi.org/10.1023/B:PROD.0000016868.69586.b4.
  22. Matthew J.P et al. 2021. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews, BMJ 2021; 372 doi: ttps://doi.org/10.1136/bmj.n71.
  23. Misiunas, N., Oztekin, A., Chen, Y., & Chandra, K. 2016. DEANN: A healthcare analytic methodology of data envelopment analysis and artificial neural networks for the prediction of organ recipient functional status. Omega 58:46-54. https://doi.org/10.1016/j.omega.2015.03.010
  24. Monteith, A, Cronk, O., Yardley, R., Willis, P., and Xiao, X. 2010. Air Quality Data Management and Integration System. AEA Group.
  25. NOCoE Data Governance for TSMO 2021, Virtual Peer Exchange Proceeding Report, National Operations Center of Excellence (NOCoE).
  26. Nyhan, R. C., and Martin, L. L. 1999. Assessing the Performance of Municipal Police Services Using Data Envelopment Analysis: An Exploratory Study. State and Local Government Review 31(1):18-30. https://doi.org/10.1177/0160323X9903100102.
  27. Park, MS., Bae KM., and Kim, YS. 2021. How to Apply the New Quality Dimensions to the New Business in the Digital Transformation Era?J Korean Soc Qual Manag. 49(4):609-622. https://doi.org/10.7469/JKSQM.2021.49.4.609
  28. Pieriegud, J. 2018. Digital Transformation of Railways. Siemens Sp.Z 0.0., Poland.
  29. Rotter, M., Hoffmann, E., Pechan, A. and Stecker, R. 2016. Competing priorities: how actors and institutions influence adaptation of the German railway system, Climatic Change 137(3):609-623. https://doi.org/10.1007/s10584-016-1702-5
  30. Ryu, SM 2018. A study on the comparative analysis of air quality in tunnels according to road bed of underground section in subway tunnel. [Master's thesis, University of Seoul]
  31. Slusarczyk, B. 2018. Industry 4.0: Are we ready? Polish Journal of Management Studies 17(1). DOI: 10.17512/pjms.2018.17.1.19.
  32. Soh, HJ., Byun JH., and Kim, DH. 2021. Quality 4.0: Concept, Elements, Level Evaluation and Deployment Direction. J Korean Soc Qual Manag 2021; 49(4):447-466. DOI: https://doi.org/10.7469/JKSQM.2021.49.4.447.
  33. Soh, JS., and Yoo, SY. 2008a. Measurement and Analysis of Indoor Thermal Environment in Passenger Car. International Journal of Railway 11(2):120-125.
  34. Soh, JS., and Yoo, SY. 2008b. A prediction of CO2 Concentration and Measurement of Indoor Air Quality in the EMU. International Journal of Railway 11(4):378-383.
  35. Transportation Research Board of the National Academies. 2015. Improving Safety Programs Through Data Governance and Data Business Planning, Transportation Research Circular, Number E-C196.