Research on the construction of a visualization platform for customer demand analysis based on big data technology

[1] Choi, S. W., Lee, E. B., & Kim, J. H. (2021). The engineering machine-learning automation platform (emap): a big-data-driven ai tool for contractors’ sustainable management solutions for plant projects. Sustainability, 13. Search in Google Scholar

[2] Khujamatov, H., Ahmad, K., Usmanova, N., Khoshimov, J., Alduailij, M., & Alduailij, M. (2022). Fog computing capabilities for big data provisioning: visualization scenario. Sustainability, 14. Search in Google Scholar

[3] Halwani, M. A., Amirkiaee, S. Y., Evangelopoulos, N., & Prybutok, V. (2021). Job qualifications study for data science and big data professions. Information Technology & People, ahead-of-print(ahead-of-print). Search in Google Scholar

[4] Ni, J. (2021). Predictive analysis of user behavior of e-commerce platform based on machine learning image algorithm in internet of things environment. Journal of Intelligent and Fuzzy Systems(2), 1-8. Search in Google Scholar

[5] Almalki, A. (2022). Postoccupancy evaluation for student housing with a focus on maintenance planning, design process, and client requirement. Journal of Performance of Constructed Facilities(1), 36. Search in Google Scholar

[6] Jalali, H., Broeke, M. V. D., & Nieuwenhuyse, I. V. (2022). Platform and product design for markets with quality and feature sensitive customers. International Journal of Production Economics, 244, 108354-. Search in Google Scholar

[7] Yang, H., & Peng, J. (2021). Coordinating a fresh-product supply chain with demand information updating: hema fresh o2o platform. RAIRO - Operations Research, 55(1), 285-318. Search in Google Scholar

[8] Liu, Z. R., Yao, B. N., & Zhang, R. F. (2022). Spamd studio: an integrated platform for atomistic modeling, simulation, analysis, and visualization. Computational Materials Science(210-), 210. Search in Google Scholar

[9] Baima, G., Santoro, G., Pellicelli, A. C., & Mitrga, M. (2022). Testing the antecedents of customer knowledge sharing on social media: a quantitative analysis on italian consumers. International marketing review. Search in Google Scholar

[10] Gina María Pipoli Azambuja, Pea, G. A. R., & Vargas, E. G. T. (2023). The impact of customer engagement on customer happiness. Journal of Consumer Behaviour, 22(4), 1025-1041. Search in Google Scholar

[11] Lee, L., & Madera, J. M. (2021). A within-level analysis of the effect of customer-focused perspective-taking on deep acting and customer helping behaviors: the mediating roles of negative affect and empathy. International Journal of Hospitality Management, 95(2), 102907. Search in Google Scholar

[12] A. Gabaldón, A. García-Garre, M.C. Ruiz-Abellón, A. Guillamón, & Fernandez-Jimenez, L. A. (2021). Improvement of customer baselines for the evaluation of demand response through the use of physically-based load models. Utilities Policy, 70(1), 101213. Search in Google Scholar

[13] Kostromitina, M., Keller, D., Cavusoglu, M., & Beloin, K. (2021). “his lack of a mask ruined everything.” restaurant customer satisfaction during the covid-19 outbreak: an analysis of yelp review texts and star-ratings. International journal of hospitality management, 98, 103048. Search in Google Scholar

[14] Cappers, P. A., & Todd-Blick, A. (2021). Heterogeneity in own-price residential customer demand elasticities for electricity under time-of-use rates: evidence from a randomized-control trial in the united states. Utilities Policy, 73. Search in Google Scholar

[15] Chang, T. H., Hsu, K. Y., Fu, H. P., Teng, Y. H., & Li, Y. J. (2022). Integrating fse and ahp to identify valuable customer needs by service quality analysis. Sustainability, 14. Search in Google Scholar

[16] Ping, Z., & Liu, Y. (2022). Classification and visual design analysis of network expression based on big data multimodal intelligence technology. Discrete Dynamics in Nature and Society, 2022. Search in Google Scholar

[17] Wang, T., & Zhou, M. (2021). Integrating rough set theory with customer satisfaction to construct a novel approach for mining product design rules. Journal of Intelligent and Fuzzy Systems(5), 1-25. Search in Google Scholar

[18] Nagrath, P., Nguyen, T. N., Aggarwal, S., & Hemanth, D. J. (2021). A comprehensive e-commerce customer behavior analysis using convolutional methods. Computers & Electrical Engineering, 96, 107541-. Search in Google Scholar

[19] Breckle, T., Manns, M., & Kiefer, J. (2021). Assembly system design using interval-based customer demand. Journal of Manufacturing Systems, 60(2), 239-251. Search in Google Scholar

[20] Zhang, J., Xie, W., & Sarin, S. C. (2021). Multiproduct newsvendor problem with customer-driven demand substitution: a stochastic integer program perspective. INFORMS journal on computing(3), 33. Search in Google Scholar

[21] Liu, F., Cheng, G., & Pan, B. (2022). Smart management system in electric market considering customer demand response. Computers and Electrical Engineering(99-), 99. Search in Google Scholar

[22] Kumar, R. S., Raghav, L. P., Raju, D. K., & Singh, A. R. (2 21). Customer‐oriented energy demand management of grid connected microgrids. International Journal of Energy Research, 45(13), 18695-18712. Search in Google Scholar

[23] Liu, T., Fu, L., Yin, C., Wu, M., Chen, L., & Niu, N. (2022). Design of smartphone platform by ratiometric fluorescent for visual detection of silver ions. Microchemical Journal: Devoted to the Application of Microtechniques in all Branches of Science(174-), 174. Search in Google Scholar

[24] Amend, C., Revellio, F., Tenner, I., & Schaltegger, S. (2022). The potential of modular product design on repair behavior and user experience - evidence from the smartphone industry. Journal of cleaner production. Search in Google Scholar

[25] Yeo, M. L., Rolland, E., Ulmer, J. R., & Patterson, R. A. (2022). How customer demand reactions impact technology innovation and security. ACM transactions on management information systems. Search in Google Scholar

[26] Kück, Mirko, & Freitag, M. (2021). Forecasting of customer demands for production planning by local k-nearest neighbor models. International Journal of Production Economics, 231. Search in Google Scholar

[27] ermáková Anna, Michael, R., & Roman, B. (2022). Possibilities of graphical interpretation of decomposition quality in fuzzy cluster analysis. Search in Google Scholar

[28] Du, K., & Wei, Z. (2022). Research on the optimization of a drilling rock breaking method based on fuzzy cluster analysis. FDMP: Fluid Dynamics & Materials Processing(3), 18. Search in Google Scholar

[29] Wang, Z., Ge, W., Qiu, L., Zhang, S., Zhou, J., & Hu, K., et al. (2023). Customized product configuration rule intelligent extraction and dynamic updating method based on the least recently used dynamic decision tree. Journal of mechanical design. Search in Google Scholar

[30] Shafiee, S., Wautelet, Y., Friis, S. C., Lis, L., & Hvam, L. (2021). Evaluating the benefits of a computer-aided software engineering tool to develop and document product configuration systems. Computers in Industry, 128, 103432. Search in Google Scholar