Chapter 39
Metasynthesis-Based Intelligent Big Data Processing Paradigm Ziqiang Zeng, Xinxin Xu and Jonathan Shi
Abstract This research aims to establish a methodology of metasynthesis-based intelligent big data processing paradigm which works based on the mechanisms of metasynthesis-architecture, metasynthesis-technology, and the principle of metasynthesis-intelligence. The proposed method will achieve the ability of intelligent data acquisition, data identification, data structure design, data analysis, and make decisions automatically and intelligently based on integrated big data processing technologies, decision making modeling methods, and intelligent algorithms. The human involvement, societal characteristics, dynamic characteristics, and uncertainty are also considered in this study. The current research status is analyzed including the big data architectures, big data processing systems, big data management, big data application. In order to deal with the complexity of big data system and establish an intelligent big data processing problem-solving methodology, an idea of “3M” structure of metasynthesis is proposed. The methodology framework is built based on the academic thoughts of metasynthesis. The application prospects for this methodology is discussed for future research. Keywords Meatasynthesis-based intelligent · Big data M-architecture · M-technology · M-intelligence
· Processing paradigm ·
Z. Zeng · X. Xu (B) Uncertainty Decision-Making Laboratory, Sichuan University, Chengdu 610065, People’s Republic of China e-mail:
[email protected] Z. Zeng Smart Transportation Applications and Research Laboratory, Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA X. Xu School of Tourism and Economic Management, Chengdu University, Chengdu 610106, People’s Republic of China J. Shi Department of Construction Management, Louisiana State University, Baton Rouge, LA 70803, USA © Springer Science+Business Media Singapore 2017 J. Xu et al. (eds.), Proceedings of the Tenth International Conference on Management Science and Engineering Management, Advances in Intelligent Systems and Computing 502, DOI 10.1007/978-981-10-1837-4_39
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