The Journal of Bigdata (한국빅데이터학회지)

Korea Bigdata Society (한국빅데이터학회)
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Partition-based Big Data Analysis and Visualization Algorithm

빅데이터 분석을 위한 파티션 기반 시각화 알고리즘

  • 홍준기 (배재대학교 컴퓨터공학과)
  • Received : 2020.08.05
  • Accepted : 2020.08.25
  • Published : 2020.08.30
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Abstract

Today, research is actively being conducted to derive meaningful results from big data. In this paper, we propose a partition-based big data analysis algorithm that can analyze the correlation between variables by setting the data areas of big data as partitions and calculating the representative values of each partition. In this paper, the analyzed visualization results are compared according to the partition size of a proposed partition-based big data analysis (PBDA) algorithm that can control the size of the partition. In order to verify the proposed PBDA algorithm, the big data of 'A' is analyzed, and meaningful results are obtained through the analysis of changes in sales volume of products according to changes in temperature and sales price.

오늘날 빅데이터로부터 유의미한 결과를 도출하는 연구가 활발히 진행되고 있다. 본 논문에선 빅데이터의 데이터의 영역들을 파티션(partition)으로 설정하고 각 파티션들의 대표 값을 계산하여 변수들 사이의 상관관계를 분석 할 수 있는 파티션 기반 빅데이터 분석 알고리즘을 제안한다. 본 논문에선 파티션의 크기조절이 가능한 파티션 기반 빅데이터 분석 알고리즘의 파티션 크기 변화에 따른 시각화 결과를 비교분석하였다. 제안한 파티션 기반 빅데이터 분석 알고리즘을 검증하기 위해 의류 회사 'A'의 빅데이터를 분석하여 온도와 판매 가격 변화에 따른 상품의 판매량 변화를 분석하고 시각화하여 유의미한 결과를 얻을 수 있었다.

Keywords

References

  1. M, Vojnovic, F. Xu, J. Zhou, "Sampling based range partition methods for big data analytics." Microsoft Res., Microsoft Corp., Redmond, WA, USA, Tech. Rep. MSR-TR-2012-18
  2. L. Wu, R. J. Barker, M. A. Kim, A. K. A. Ross, "Navigating big data with high-throughput, energyefficient data partitioning." ACM SIGARCH Comput. Arch. News, Vol. 41, No. 3, 249-260, 2013. https://doi.org/10.1145/2508148.2485944
  3. S. Jain, J. Bruniaux, X. Zeng, and P. Bruniaux, "Big Data in Fashion Industry," IOP Conference Series: Materials Science and Engineering, Vol. 254, No. 15, 2017.
  4. D. Oivind and T. Stenheim, "Big Data Viewed Through the Lens of Management Fashion Theory," Cogent Business & Management, Vol l, No. 3, 2016.
  5. New York Times, The Age of Big Data(2012). https://www.nytimes.com/2012/02/12/sunday-review/big-datas-impact-in-the-world.html
  6. T. Choi, "Incorporating Social Media Observations and Bounded Rationality into Fashion Quick Response Supply Chains in the Big Data Era," Transportation Research Part E: Logistics and Transportation Review, Vol. 114, pp. 386-397, 2018. https://doi.org/10.1016/j.tre.2016.11.006
  7. S. Thomassey, "Sales Forecasts in Clothing Industry: The Key Success Factor of the Supply Chain Management," International Journal of Production Economics, Vol. 128, No. 2, pp. 470-483, 2010. https://doi.org/10.1016/j.ijpe.2010.07.018
  8. W. K. Wong and Z. X. Guo, "A Hybrid Intelligent Model for Medium-term Sales Forecasting in Fashion Retail Supply Chains using Extreme Learning Machine and Harmony Search Algorithm," International Journal of Production Economics, Vol. 128, No. 2, pp. 614-624, 2010. https://doi.org/10.1016/j.ijpe.2010.07.008
  9. S. Ren, T. Choi, and N. Liu, "Fashion Sales Forecasting with a Panel Data-based Particle-filter Model," IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 45, No. 3, pp. 411-421, 2015. https://doi.org/10.1109/TSMC.2014.2342194
  10. K. Au, T. Choi, and Y. Yu, "Fashion Retail Forecasting by Evolutionary Neural Networks," International Journal of Production Economics, Vol. 114, No. 2, pp. 615-630, 2018. https://doi.org/10.1016/j.ijpe.2007.06.013
  11. Y. Ni and F. Fan, "A Two-stage Dynamic Sales Forecasting Model for the Fashion Retail," Expert Systems with Applications, Vol. 38, No. 3, pp. 1529-1536, 2011. https://doi.org/10.1016/j.eswa.2010.07.065
  12. N. Liu, S. Ren, T.-M. Choi, C.-L. Hui, and S.-F. Ng, "Sales Forecasting for Fashion Retailing Service Industry: A Review," Mathematical Problems in Engineering, Vol. 2013, pp. 1-9, 2013.