Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Offline-to-Online Service and Big Data Analysis for End-to-end Freight Management System

  • Selvaraj, Suganya (Dept. of Financial Information Security, Kookmin University) ;
  • Kim, Hanjun (Dept. of Financial Information Security, Kookmin University) ;
  • Choi, Eunmi (Dept. of Financial Information Security, Kookmin University)
  • Received : 2018.10.02
  • Accepted : 2019.11.25
  • Published : 2020.04.30
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Abstract

Freight management systems require a new business model for rapid decision making to improve their business processes by dynamically analyzing the previous experience data. Moreover, the amount of data generated by daily business activities to be analyzed for making better decisions is enormous. Online-to-offline or offline-to-online (O2O) is an electronic commerce (e-commerce) model used to combine the online and physical services. Data analysis is usually performed offline. In the present paper, to extend its benefits to online and to efficiently apply the big data analysis to the freight management system, we suggested a system architecture based on O2O services. We analyzed and extracted the useful knowledge from the real-time freight data for the period 2014-2017 aiming at further business development. The proposed system was deemed useful for truck management companies as it allowed dynamically obtaining the big data analysis results based on O2O services, which were used to optimize logistic freight, improve customer services, predict customer expectation, reduce costs and overhead by improving profit margins, and perform load balancing.

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References

  1. D. Laney, "3D data management: controlling data volume, velocity and variety," 2001; https://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf.
  2. Crunchbase, "Huochebang: an on-demand platform," 2008; https://www.crunchbase.com/organization/huochebang.
  3. M. Jeske, M. Gruner, and F. Weiss, "Big data in logistics: a DHL perspective on how to move beyond the hype," 2013; https://www.dhl.com/content/dam/downloads/g0/about_us/innovation/CSI_Studie_BIG_DATA.pdf.
  4. Uber Freight, https://www.uber.com/us/en/freight/.
  5. R. Ihaka and R. Gentleman, "R: a language for data analysis and graphics," Journal of Computational and Graphical Statistics, vol. 5, no. 3, pp. 299-314, 1996. https://doi.org/10.2307/1390807
  6. Apache Hadoop, http://hadoop.apache.org/.
  7. Revolution Analytics, "Rhadoop and MapR," 2014; https://mapr.com/resources/rhadoop-and-mapr/assets/rhadoop_and_mapr.pdf.
  8. MakeMeAnalyst, "List of Big Companies using R," 2017; http://makemeanalyst.com/companies-using-r/.
  9. A. V. Rotte, G. Patwari, and S. Hiremath, "Big data analytics made easy with Rhadoop," International Journal of Research in Engineering and Technology, vol. 4, no. 5, pp. 9-15, 2015.
  10. W. Cho, Y. Lim, H. Lee, M. K. Varma, M. Lee, and E. Choi, "Big data analysis with interactive visualization using R packages," in Proceedings of the 2014 International Conference on Big Data Science and Computing, Beijing, China, 2014, pp. 1-6.
  11. W. Cho and E. Choi, "DTG big data analysis for fuel consumption estimation," Journal of Information Processing Systems, vol. 13, no. 2, pp. 285-304, 2017. https://doi.org/10.3745/JIPS.04.0031
  12. A. I. Kabir, R. Karim, S. Newaz, and M. I. Hossain, "The power of social media analytics: text analytics based on sentiment analysis and word clouds on R," Informatica Economica, vol. 22, no. 1, pp. 25-38, 2018. https://doi.org/10.12948/issn14531305/22.1.2018.03
  13. J. Singh, G. Singh, and R. Singh, "Optimization of sentiment analysis using machine learning classifiers," Human-centric Computing and information Sciences, vol. 7, article no. 32, 2017.
  14. Y. Wang, K. Kim, B. Lee, and H. Y. Youn, "Word clustering based on POS feature for efficient twitter sentiment analysis," Human-centric Computing and Information Sciences, vol. 8, article no. 17, 2018.
  15. B. Xiao, Z. Wang, Q. Liu, and X. Liu, "SMK-means: an improved mini batch k-means algorithm based on MapReduce with big data," Computers, Materials & Continua, vol. 56, no. 3, pp. 365-379, 2018.
  16. W. Cho and E. Choi, "Big data pre-processing methods with vehicle driving data using MapReduce techniques," The Journal of Supercomputing, vol. 73, no. 7, pp. 3179-3195, 2017. https://doi.org/10.1007/s11227-017-2014-x
  17. W. Cho and E. Choi, "A basis of spatial big data analysis with map-matching system," Cluster Computing, vol. 20, no. 3, pp. 2177-2192, 2017. https://doi.org/10.1007/s10586-017-1014-1
  18. J. J. Kim, "Hadoop based wavelet histogram for big data in cloud," Journal of Information Processing Systems, vol. 13, no. 4, pp. 668-676, 2017. https://doi.org/10.3745/JIPS.04.0036
  19. B. Gesing, S. Peterson, and D. Michelsen, "Artificial intelligence in logistics," 2018; https://www.ibm.com/downloads/cas/XOQW7Q0D.
  20. RStudio, https://www.rstudio.com/products/rstudio.
  21. R packages CRAN, https://cran.r-project.org/.
  22. S. Garcia, S. Ramirez-Gallego, J. Luengo, J. M. Benitez, and F. Herrera, "Big data preprocessing: methods and prospects," Big Data Analytics, vol. 1, article no. 9, 2016.
  23. Kakao Developers, https://developers.kakao.com.
  24. Y. M. Kwon, J. Park, H. Lee, and M. Kim, "Beacon-based O2O marketing for financial institutions," The International Journal of Industrial Distribution & Business, vol. 5, no. 4, pp. 23-29, 2014. https://doi.org/10.13106/ijidb.2014.vol5.no4.23.
  25. J. Zhang, J. "Customer' loyalty forming mechanism of O2O e-commerce," International Journal of Business and Social Science, vol. 5, no. 5, pp. 164-169, 2014.
  26. X. Hu, P. K. Chen, and Z. Lou, "Building a forecasting model of customer demand for O2O dual-channel," in Proceedings of 2017 International Conference on Applied System Innovation (ICASI), Sapporo, Japan, 2017, pp. 721-723.
  27. J. Bort, "Facebook just did a really smart thing by acquiring Glancee," 2012; https://www.businessinsider.com/facebook-just-did-a-really-smart-thing-by-acquiring-glancee-2012-5.
  28. Facebook, "Place tips: a valuable page tool for local businesses," 2015; https://www.facebook.com/business/news/place-tips-for-businesses.
  29. NTT Docomo Shoppulatto, 2013; https://projekter.aau.dk/projekter/files/77307375/Shopping_Assitant_ICTE.pdf.
  30. S. Moore, "Gartner identifies top 10 data and analytics technology trends for 2019," 2019; https://www.gartner.com/en/newsroom/press-releases/2019-02-18-gartner-identifies-top-10-data-and-analytics-technolo.
  31. Gartner Glossary: real-time analytics, https://www.gartner.com/en/information-technology/glossary/realtime-analytics.