Abstract
With the progress of digital and information technology, the rise and rapid development of big data technology has drawn great attention from all quarters. However, there is a general lack of overall planning in the field of catering O2O. Combined with the development and application of catering O2O, this paper analyzes and studies the different levels of the design of the catering O2O cloud platform system. A decision support system for dietary recommendation based on Chinese traditional Chinese medicine theory is described in this research. The theory and method of diet decision support system are analyzed in order to provide a reference for the new method of catering O2O modeling.
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19 December 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s11277-022-10143-z
References
Baoxiang, X., & Yunzhong, Z. (2010). Research on the development of information system modeling theory. Journal of Intelligence, 29(5), 70–74.
McAfee, A., Brynjolfsson, E., Davenport, T. H., et al. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 61–67.
Barton, D., & Court, D. (2012). Spotlight on big data-making advanced analytics work for you. Harvard Business Review, 90, 79–83.
Narayanan, M., & Cherukuri, A. K. (2016). A study and analysis of recommendation systems for location-based social network (LBSN) with big data. Iimb Management Review, 28(1), 25–30.
Gil, D., & Song, I. Y. (2016). Modeling and management of big data. Amsterdam: Elsevier Science Publishers B. V.
Douglas, C. C. (2014). An open framework for dynamic big-data-driven application systems (DBDDAS) development ☆. Procedia Computer Science, 29, 1246–1255.
LaValle, S., Lesser, E., Shockley, R., et al. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21–32.
Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2014). Trends in big data analytics. Journal of Parallel & Distributed Computing, 74(7), 2561–2573.
Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246.
Wang, Y., Kung, L. A., & Byrd, T. A. (2016). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3–13.
Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems, 2(1), 3.
Yang, Q., Wu, G., & Wang, L. (2017). Big data: A new perspective of the engineering project management driven by data. Xitong Gongcheng Lilun Yu Shijian/System Engineering Theory & Practice, 37(3), 710–719.
Zhao, R., Liu, Y., Zhang, N., & Huang, T. (2016). An optimization model for green supply chain management by using a big data analytic approach. Journal of Cleaner Production, 140(2), 1085–1097.
Khalili, A., & Sami, A. (2015). Sysdetect: A systematic approach to critical state determination for industrial intrusion detection systems using apriori algorithm. Journal of Process Control, 32(11), 154–160.
Riggins, F. J., & Wamba, S. F. (2015). Research directions on the adoption, usage, and impact of the internet of things through the use of big data analytics. In IEEE Hawaii international conference on system sciences (pp. 1531–1540).
Poleto, T., Carvalho, V. D. H. D., & Costa, A. P. C. S. (2015). The Roles of Big Data in the Decision-Support Process: An Empirical Investigation. In International conference on decision support system technology (Vol. 216, pp. 10–21). Berlin: Springer.
Caballeroruiz, E., Garcíasáez, G., Rigla, M., Villaplana, M., Pons, B., & Hernando, M. E. (2017). A web-based clinical decision support system for gestational diabetes: Automatic diet prescription and detection of insulin needs. International Journal of Medical Informatics, 102, 35–49.
Malmir, B., Amini, M., & Chang, S. I. (2017). A medical decision support system for disease diagnosis under uncertainty. Expert Systems with Applications, 88, 95–108.
Zhuang, Z. Y., Wilkin, C. L., & Ceglowski, A. (2013). A framework for an intelligent decision support system: A case in pathology test ordering. Decision Support Systems, 55(2), 476–487.
Rustempasic, I., & Can, M. (2013). Diagnosis of parkinson’s disease using fuzzy c-means clustering and pattern recognition. Southeast Europe Journal of Soft Computing, 2(1), 42–49.
Babiceanu, R. F., & Seker, R. (2016). Big data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook. Computers in Industry, 81(C), 128–137.
Lee, J., Ardakani, H. D., Yang, S., & Bagheri, B. (2015). Industrial big data analytics and cyber-physical systems for future maintenance & service innovation ☆. Procedia CIRP, 38, 3–7.
Cevher, V., Becker, S., & Schmidt, M. (2014). Convex optimization for big data: Scalable, randomized, and parallel algorithms for big data analytics. IEEE Signal Processing Magazine, 31(5), 32–43.
Chan, S. H., Song, Q., Sarker, S., & Plumlee, R. D. (2017). Decision support system (DSS) use and decision performance: DSS motivation and its antecedents. Information & Management, 54, 934.
Kumar, S. J., & Madheswaran, M. (2012). An improved medical decision support system to identify the diabetic retinopathy using fundus images. Journal of Medical Systems, 36(6), 3573–3581.
Zhou, X., Chen, S., Liu, B., Zhang, R., Wang, Y., Li, P., et al. (2010). Development of traditional chinese medicine clinical data warehouse for medical knowledge discovery and decision support. Artificial Intelligence in Medicine, 48(2–3), 139–152.
Angel, G. C., Giner, A. H., Linamara, B., & Alejandro, R. G. (2013). Methods and models for diagnosis and prognosis in medical systems. Computational & Mathematical Methods in Medicine, 2013(3), 184257.
Xu, F., Zhang, Y., Cui, W., Yi, T., Tang, Z., & Dong, J. (2017). The association between metabolic syndrome and body constitution in traditional chinese medicine. European Journal of Integrative Medicine, 14, 32–36.
Yu, T., Li, J., Yu, Q., Ye, T., Shun, X., Xu, L., et al. (2017). Knowledge graph for tcm health preservation: Design, construction, and applications. Artificial Intelligence in Medicine, 77, 48–52.
Kolodner, J. L. (1992). An introduction to case-based reasoning. Artificial Intelligence Review, 6(1), 3–34.
Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. Ai Communications, 7(1), 39–59.
Zhao, Y., Zhang, M., Guo, X., Zhou, Z., & Zhang, J. (2017). Research on matching method for case retrieval process in CBR based on FCM ☆. Procedia Engineering, 174, 267–274.
Acknowledgements
The authors acknowledge the National Natural Science Foundation of China (Grant: 71473087), the SRP project of South China University of Technology (Grant: 2017B16020).
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Tang, D., Zhu, W. & Kuvshinov, A. RETRACTED ARTICLE: A Big Data-Driven Approach to Catering O2O Modeling. Wireless Pers Commun 103, 1089–1099 (2018). https://doi.org/10.1007/s11277-018-5503-1
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DOI: https://doi.org/10.1007/s11277-018-5503-1