Integrating artificial intelligence, operations research, and big data analytics for decision and risk analysis

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In the information age, the volume of data is incredibly large and is continually growing at a high rate, offering unprecedented opportunities for intelligent decision making and risk analysis. However, this overflow of information can also introduce noise, accuracy threats and performance obstacles. Wrong decisions due to the inefficiency of existing techniques can cause significant costs affecting the community. Moreover, “5Vs” characteristics of big data, namely Volume, Variety, Velocity, Variability and Veracity, require new algorithms to enable enhanced decision making and knowledge discovery in context where, for example, real-time massive data need to be processed. The objective of this proposed research is to integrate artificial intelligence, operations research, and big data analytics to develop high performance decision support and risk analysis algorithms. Firstly, it aims at addressing the key issue in big data analytics to find easy-to-calculate, robust and efficient analytical methods. I propose a framework based on partial maximum correlation information (PMCI) for decision and risk analysis in big data. Secondly, the proposed research will creatively integrate DEA and classifiers to predict the relative efficiency level of DMUs. Classifier constructs the mapping relationship between the sample features and the pre-defined class labels after training with samples. The trained classifier can predict the class labels for the new samples. Thirdly, the AI approaches will be combined with big data analytics for information fusion in order to explore business insights and economics of services. Finally, parallel computing is an efficient method to deal with big data. The proposed research will implement different parallel computing frameworks to advance AI algorithms for decision making using big data. Overall, the proposed research will provide an innovative and unique approach to address key issues to apply artificial intelligence, operations research, and big data analytics for decision and risk analysis. It will open up new possibilities for decision and risk analysis in the big data era. It is also the target of this research to parallel the proposed algorithms using Graphics Processing Unit (GPU), Hadoop and Apache Spark to accelerate their execution.