Algorithms and Tools for Big Data Analysis and Automated Real Time Optimal or Near Optimal Decision Making for Industrial Systems

用于工业系统大数据分析和自动实时最佳或接近最佳决策的算法和工具

• 批准号: RGPIN-2017-05785
• 负责人: Yacout, Soumaya
• 金额: $ 2.04万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=669108
• 关键词: Algorithms; Tools; Big; Data; Analysis

Data science and data engineering have arguably become some of the most important research fields in this century. These fields are based on fundamental branches of science and engineering, namely, information technology, sensor technology, statistics, operations research, optimization, artificial intelligence, data mining and machine learning.***Along with human centric applications, some of these techniques are now being recommended by researchers in machine centric applications in which data is manufactured by machines and decisions are also made by machines based on ‘Machine to Machine (M2M)' learning.***Presently, an important research question is how to exploit the available Big Data sets since, by definition, they consist of large volumes of data, acquired at high velocity, and in a variety of forms. Traditional data-processing and analysis techniques become inadequate.***The objective of this proposal is to develop algorithms and tools that are designed specifically to analyze and to extract knowledge from Big Data that are obtained from industrial systems. The extracted knowledge should lead to an understanding of how various components of a complex system influence each other and interact with their environment, and how an accurate prediction of the degradation can be obtained in a parallel computing framework.***The proposed methodology is based on an approach called Logical Analysis of Data (LAD), which is a data mining, machine learning approach that is based on Boolean logical reasoning. It extracts knowledge in the form of patterns that distinguish and characterize sets of data, and that identify some phenomena of interest. Different LAD' s algorithms that are used to extract patterns in supervised and unsupervised learning will be considered in parallel computing frameworks; namely, enumeration techniques, mixed integer linear programming, and metaheuristics algorithms, mainly genetic algorithms, and ant colonies. The two parallel frameworks that will be used are Hadoop MapReduce and Spark; both are available in an open source environment, thus they are available to the public.***We intend to present to the scientific community scaled up algorithms in an open source environment. As such, every interested individual can use them, improve upon them and add to them. The impact of this research is the possibility of learning, finding, understanding physical complex phenomena that are not fully understood yet, and the exploitation of this knowledge in decision making. Depending on the specific applications in which these algorithms will be used, this knowledge can lead to an increase in safety and security, energy savings, protection of the environment, and increased efficiency in consuming natural resources. It will also lead to intelligent systems that can make the right decision at the right moment. Eventually, this will lead to self-sustaining and sustainable systems.*****

数据科学和数据工程可以说是科学的分支机构。决策也由基于“机器到机器（M2M）”的机器做出。形式。复杂的系统相互互动并与设想相互作用，以及如何在平行计算框架中预测准确的方法。逻辑推理的低音和识别一些现象的逻辑推理。将使用Hadoop MapReduce，并且在开源环境中都可以使用。他们。正确的时刻。