Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
권호 표지
DOI QR코드

DOI QR Code

Effective visualization methods for a manufacturing big data system

제조 빅데이터 시스템을 위한 효과적인 시각화 기법

  • Yoo, Kwan-Hee (Department of Computer Science, Chungbuk National University)
  • 류관희 (충북대학교 소프트웨어학과)
  • Received : 2017.11.03
  • Accepted : 2017.11.15
  • Published : 2017.11.30
⟨ Previous Next ⟩

Abstract

Manufacturing big data systems have supported decision making that can improve preemptive manufacturing activities through collection, storage, management, and predictive analysis of related 4M data in pre-manufacturing processes. Effective visualization of data is crucial for efficient management and operation of data in these systems. This paper presents visualization techniques that can be used to effectively show data collection, analysis, and prediction results in the manufacturing big data systems. Through the visualization technique presented in this paper, we have confirmed that it was not only easy to identify the problems that occurred at the manufacturing site, but also it was very useful to reply to these problems.

제조 빅데이터 시스템은 제조 전 공정에서 관련된 4M 데이터의 수집, 저장, 관리, 예측적 분석을 통해 선제적 제조 활동 개선이 가능한 의사결정을 지원하고 있다. 이러한 시스템에서 데이터의 효율적인 관리와 운영을 위해 데이터를 효과적으로 시각화하는 것이 무엇보다도 중요하다. 본 논문에서는 제조 빅데이터 시스템에서 데이터 수집, 분석 및 예측 결과를 효과적으로 보여 주기 위해 사용가능한 시각화 기법을 제시한다. 본 논문에서 제시된 시각화 기법을 통해 제조 현장에서 발생하는 문제를 보다 손쉽게 파악할 수 있었을 뿐만 아니라 이들 문제를 효과적으로 대응할 수 있어 매우 유용하게 사용될 수 있음을 확인하였다.

Keywords

Acknowledgement

Supported by : 정보통신기술진흥센터

References

  1. Bao, F. and Chen, J. (2014). Visual framework for big data in d3.js. IEEE Workshop on Electronics, Computer and Applications, 44-50.
  2. Chankhihort, D., Choi, S. S., Lee, G. J., Im, B. M., Nasridinov, A. Kwon, S. O., Lee, S. H., Kang, J. T. and Yoo, K. H. (2016). Integrative manufacturing data visualization using calendar view map. Eighth International Conference on Ubiquitous and Future Networks, 114-116.
  3. Chankhihort, D., Choi, S. S., Lee, G. J., Im, B. M., Nasridinov, A. Kwon, S.O., Lee, S. H., Kang, J. T. and Yoo, K. H. (2016). Integrative visualization of resources abnormal analysis results. The 3rd international conference on big data applications and services (Bigdas-L), 3, 196-200.
  4. Chankhihort, D., Choi, S. S., Lee, G. J., Im, B. M., Nasridinov, A. Kwon, S. O., Lee, S. H., Kang, J. T. and Yoo, K. H. (2017). A visualization scheme with a calendar heat map for abnormal pattern analysis in the manufacturing process. International Journal of Contents, 13, 21-28.
  5. Chi, S. Y. and Yoo, K.-H. (2017). Development of predictive manufacturing system using data analysis of 4M data in small and medium enterprises, ETRI and Chungbuk National University, Korea.
  6. Choi, S., Battulga, L., Nasridinov, A. and Yoo, K. H. (2017). A decision tree approach for identifying defective products in the manufacturing process. Journal of Korea Contents, 13, 57-65.
  7. Choi, S., Jung, K. and Noh, S. D. (2015), Virutal reality applications in manufacturing industries: past research, present findings, and future directions. Concurrent Engineering Research and Applications, 23, 40-63. https://doi.org/10.1177/1063293X14568814
  8. D3js: Data-driven documents (2017), Mike bostock, https://d3js.org/
  9. Harrington, P. (2011), Machine learning in action, Amazon, USA.
  10. Jung, B. H. and Lim, D. H. (2017). Comparison analysis of big data integration models. Journal of the Korean Data & Information Science, 28, 755-768.
  11. Lee, J., Bagheri, B. and Kao, H. A. (2015). A cyber-physical systems architecture for Industry 4.0?based manufacturing systems. Manufacturing Letter, 3, 18-23. https://doi.org/10.1016/j.mfglet.2014.12.001
  12. Lee, J., Lapira, E., Bagheri, B. and Kao, H. (2013). Recent advances and trends in predictive manufacturing system in a big data environment. Manufacturing Letter, 1, 38-41. https://doi.org/10.1016/j.mfglet.2013.09.005
  13. Maaten, L. and Hinton, G. (2008). Visualizing data using t-SNE3. Journal of Machine Learning Research, 9, 2579-2605.
  14. OCED (2017). Better life index, http://www.oecdbetterlifeindex.org/
  15. Shin, J. E., Jung, B. H. and Lim, D. H. (2015). Big data distributed processing system using RHadoop. Journal of the Korean Data & Information Science, 26, 1155-1166. https://doi.org/10.7465/jkdi.2015.26.5.1155
  16. Shin, J. E., Oh, Y. S. and Lim, D. H. (2016). RHadoop platform for K-Means clustering of big data. Journal of the Korean Data & Information Science, 27, 609-619. https://doi.org/10.7465/jkdi.2016.27.3.609
  17. The R project for statistical computing (2017). The R foundation, https://www.r-project.org/
  18. W3C SVG WG (2017). Scalable vector graphics, http://www.w3.org/Graphics/SVG/
  19. Wells, L. J., Megahed, F. M., Camelio, J. A. and Woodall, W. H. (2012). A framework for variation visualization and understanding in complex manufacturing systems. Journal of Intelligent Manufacturing, 23, 2025-2036. https://doi.org/10.1007/s10845-011-0529-1
  20. Weka 3 (2017). Data mining software in java, https://www.cs.waikato.ac.nz/ml/weka/, University of Waikato.
  21. Wind map (2017). http://hint.fm/wind/
  22. Yeon, H., Pi, M., Seo, S., Ha, S., Oh, B. and Jang, Y. (2017). Visual analytics approach for performance improvement of predicting youth physical growth model. Journal of the Korea Computer Graphics Society, 23, 21-30. https://doi.org/10.15701/kcgs.2017.23.4.21
  23. Zhong, Y. and Shirinzadeh, B. (2008). Virtual factory for manufacturing process visualization. Complexity International, 12, 1-22.

Cited by

  1. 사례 연구를 통한 스마트 시티 플랫폼의 서비스를 위한 참조 모델 vol.19, pp.8, 2017, https://doi.org/10.14400/jdc.2021.19.8.241