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

集成人工智能、运筹学和大数据分析进行决策和风险分析

• 批准号: RGPIN-2018-05988
• 负责人: Yang, Zijiang
• 金额: $ 1.89万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=713984
• 关键词: Integrating; artificial; intelligence; operations; research

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.

在信息时代，数据量非常大，并且持续高速增长，为智能决策和风险分析提供了前所未有的机会。然而，这种信息溢出也会带来噪音、准确性威胁和性能障碍。由于现有技术的低效率而导致的错误决策可能导致影响社区的重大成本。此外，大数据的“5v”特征，即体积（Volume）、种类（Variety）、速度（Velocity）、可变性（Variability）和准确性（Veracity），需要新的算法来增强决策和知识发现能力，例如需要处理实时海量数据。