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Intelligent data analytics is here to change engineering management

Jonathan Jingsheng SHI, Saixing ZENG, Xiaohua MENG

《工程管理前沿（英文）》 2017年第4卷第1期页码 41-48 doi: 10.15302/J-FEM-2017003

摘要： A great deal of scientific research in the world aims at discovering the facts about the world so that we understand it better and find solutions to problems. Data enabling technology plays an important role in modern scientific discovery and technologic advancement. The importance of good information was long recognized by prominent leaders such as Sun Tzu and Napoleon. Factual data enables managers to measure, to understand their businesses, and to directly translate that knowledge into improved decision making and performance. This position paper argues that data analytics is ready to change engineering management in the following areas: 1) by making relevant historical data available to the manager at the time when it’s needed; 2) by filtering out actionable intelligence from the ocean of data; and 3) by integrating useful data from multiple sources to support quantitative decision-making. Considering the unique need for engineering management, the paper proposes researchable topics in the two broad areas of data acquisition and data analytics. The purpose of the paper is to provoke discussion from peers and to encourage research activity.

关键词： engineering management project management big data data analytics planning execution

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智能过程制造中的数据解析与机器学习——大数据时代的最新进展与展望 Perspective

尚超、 Fengqi You

《工程（英文）》 2019年第5卷第6期页码 1010-1016 doi: 10.1016/j.eng.2019.01.019

摘要：

安全、高效、可持续的运行是工业生产过程控制的主要目标。然而，目前的技术严重依赖人为干预，因此在实际应用中体现出明显的局限性。蓬勃发展的大数据时代对流程工业产生了巨大的影响，为实现智能制造提供了前所未有的机遇。这种新的生产方式不仅要求机器能够帮助人类减轻繁重的体力劳动，还要能有效地承担智力劳动，甚至能够实现自主创新。为了实现这一目标，数据分析与机器学习扮演着不可或缺的角色。在本文中，我们回顾了数据分析和机器学习在工业生产过程监控、控制和优化方面的最新进展，着重分析机器学习模型的可解释性和功能性。通过分析实际需求与研究现状之间的差距，为未来的研究方向给出了建议。

关键词：大数据机器学习智能制造过程系统工程

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Data analytics and optimization for smart industry

Lixin TANG, Ying MENG

《工程管理前沿（英文）》 2021年第8卷第2期页码 157-171 doi: 10.1007/s42524-020-0126-0

摘要： Industrial intelligence is a core technology in the upgrading of the production processes and management modes of traditional industries. Motivated by the major development strategies and needs of industrial intellectualization in China, this study presents an innovative fusion structure that encompasses the theoretical foundation and technological innovation of data analytics and optimization, as well as their application to smart industrial engineering. First, this study describes a general methodology for the fusion of data analytics and optimization. Then, it identifies some data analytics and system optimization technologies to handle key issues in smart manufacturing. Finally, it provides a four-level framework for smart industry based on the theoretical and technological research on the fusion of data analytics and optimization. The framework uses data analytics to perceive and analyze industrial production and logistics processes. It also demonstrates the intelligent capability of planning, scheduling, operation optimization, and optimal control. Data analytics and system optimization technologies are employed in the four-level framework to overcome some critical issues commonly faced by manufacturing, resources and materials, energy, and logistics systems, such as high energy consumption, high costs, low energy efficiency, low resource utilization, and serious environmental pollution. The fusion of data analytics and optimization allows enterprises to enhance the prediction and control of unknown areas and discover hidden knowledge to improve decision-making efficiency. Therefore, industrial intelligence has great importance in China’s industrial upgrading and transformation into a true industrial power.

关键词： data analytics system optimization smart industry

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工业互联网平台：发展趋势与挑战

王晨,宋亮,李少昆

《中国工程科学》 2018年第20卷第2期页码 15-19 doi: 10.15302/J-SSCAE-2018.02.003

摘要：

随着制造业和新一代互联网、信息化技术的融合，工业互联网高速发展。无论是国际制造业的领先企业，还是我国的制造业国家战略都明确了工业互联网平台研发的重要性。本文对工业互联网平台的发展趋势进行了阐释，并对平台在用户生态、开发者生态和数据生态构建中的挑战展开了分析，并有针对性地探讨了工业互联网平台在工业大数据系统与工业数据建模和分析方面所遇到的技术挑战。

关键词：工业互联网平台工业大数据数据分析

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Special issue: Decision, risk analytics and data intelligence

Xiaozhe ZHAO, Desheng WU

《工程管理前沿（英文）》 2020年第7卷第2期页码 169-171 doi: 10.1007/s42524-020-0114-4

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CORRECTION to: Special issue: Decision, risk analytics and data intelligence

Xiaozhe ZHAO, Desheng Wu

《工程管理前沿（英文）》页码 697-697 doi: 10.1007/s42524-020-0139-8

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Special issue: Innovative applications of big data and artificial intelligence

《工程管理前沿（英文）》 2022年第9卷第4期页码 517-519 doi: 10.1007/s42524-022-0234-0

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A review of systematic evaluation and improvement in the big data environment

Feng YANG, Manman WANG

《工程管理前沿（英文）》 2020年第7卷第1期页码 27-46 doi: 10.1007/s42524-020-0092-6

摘要： The era of big data brings unprecedented opportunities and challenges to management research. As one of the important functions of management decision-making, evaluation has been given more functions and application space. Exploring the applicable evaluation methods in the big data environment has become an important subject of research. The purpose of this paper is to provide an overview and discussion of systematic evaluation and improvement in the big data environment. We first review the evaluation methods based on the main analytic techniques of big data such as data mining, statistical methods, optimization and simulation, and deep learning. Focused on the characteristics of big data (association feature, data loss, data noise, and visualization), the relevant evaluation methods are given. Furthermore, we explore the systematic improvement studies and application fields. Finally, we analyze the new application areas of evaluation methods and give the future directions of evaluation method research in a big data environment from six aspects. We hope our research could provide meaningful insights for subsequent research.

关键词： big data evaluation methods systematic improvement big data analytic techniques data mining

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Challenges to Engineering Management in the Big Data Era

Yong Shi

《工程管理前沿（英文）》 2015年第2卷第3期页码 293-303 doi: 10.15302/J-FEM-2015042

摘要： This paper presents a review of the challenges to engineering management in the Big Data Era as well as the Big Data applications. First, it outlines the definitions of big data, data science and intelligent knowledge and the history of big data. Second, the paper reviews the academic activities about big data in China. Then, it elaborates a number of challenging big data problems, including transforming semi-structured and non-structured data into “structured format” and explores the relationship of data heterogeneity, knowledge heterogeneity and decision heterogeneity. Furthermore, the paper reports various real-life applications of big data, such as financial and petroleum engineering and internet business.

关键词： big data data science intelligent knowledge engineering management real-life applications

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Clinical research of traditional Chinese medicine in big data era

null

《医学前沿（英文）》 2014年第8卷第3期页码 321-327 doi: 10.1007/s11684-014-0370-y

摘要：

With the advent of big data era, our thinking, technology and methodology are being transformed. Data-intensive scientific discovery based on big data, named “The Fourth Paradigm,” has become a new paradigm of scientific research. Along with the development and application of the Internet information technology in the field of healthcare, individual health records, clinical data of diagnosis and treatment, and genomic data have been accumulated dramatically, which generates big data in medical field for clinical research and assessment. With the support of big data, the defects and weakness may be overcome in the methodology of the conventional clinical evaluation based on sampling. Our research target shifts from the “causality inference” to “correlativity analysis.” This not only facilitates the evaluation of individualized treatment, disease prediction, prevention and prognosis, but also is suitable for the practice of preventive healthcare and symptom pattern differentiation for treatment in terms of traditional Chinese medicine (TCM), and for the post-marketing evaluation of Chinese patent medicines. To conduct clinical studies involved in big data in TCM domain, top level design is needed and should be performed orderly. The fundamental construction and innovation studies should be strengthened in the sections of data platform creation, data analysis technology and big-data professionals fostering and training.

关键词： big data traditional Chinese medicine clinical evaluation evidence based medicine

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Appreciating the role of big data in the modernization of environmental governance

《工程管理前沿（英文）》 2022年第9卷第1期页码 163-169 doi: 10.1007/s42524-021-0185-x

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Scientific computation of big data in real-world clinical research

null

《医学前沿（英文）》 2014年第8卷第3期页码 310-315 doi: 10.1007/s11684-014-0358-7

摘要：

The advent of the big data era creates both opportunities and challenges for traditional Chinese medicine (TCM). This study describes the origin, concept, connotation, and value of studies regarding the scientific computation of TCM. It also discusses the integration of science, technology, and medicine under the guidance of the paradigm of real-world, clinical scientific research. TCM clinical diagnosis, treatment, and knowledge were traditionally limited to literature and sensation levels; however, primary methods are used to convert them into statistics, such as the methods of feature subset optimizing, multi-label learning, and complex networks based on complexity, intelligence, data, and computing sciences. Furthermore, these methods are applied in the modeling and analysis of the various complex relationships in individualized clinical diagnosis and treatment, as well as in decision-making related to such diagnosis and treatment. Thus, these methods strongly support the real-world clinical research paradigm of TCM.

关键词： big data real world clinical research Chinese medicine medical computing

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APFD：面向移动轨迹大数据的出租车路径推荐方法 Research Article

张文勇1,夏大文1,常国艳5,胡杨2,霍雨佳1,冯夫健1,李艳涛3,李华青4

《信息与电子工程前沿（英文）》 2022年第23卷第10期页码 1494-1510 doi: 10.1631/FITEE.2100530

摘要：

随着数据驱动智能交通系统的迅猛发展，高效的出租车路径推荐方法成为智慧城市的研究热点。基于移动轨迹大数据，提出一种基于人工势场（APF）和Dijkstra方法的出租车路径推荐方法。为提高路径推荐效率，提出一种区域提取方法，该方法通过原点和终点坐标搜索包含最优路径的区域。基于APF方法，提出一种有效的冗余节点去除方法。最后，通过Dijkstra方法推荐最优路径。将APFD方法应用于仿真地图和北京四环的实际路网。在地图上随机选取20对起点和终点坐标，采用APFD方法、蚁群（AC）算法、贪婪算法（A*）、APF、迅速探索随机树（RRT）、非支配排序遗传算法-II（NSGA-II）、粒子群算法（PSO）和Dijkstra算法进行最短路径推荐。在最短路径规划方面，与AC、A*、APF、RRT、NSGA-II和PSO相比，APFD的路径规划能力分别提高了1.45%–39.56%、4.64%–54.75%、8.59%–37.25%、5.06%–45.34%、0.94%–20.40%和2.43%–38.31%。与Dijkstra算法相比，APFD的执行效率提高了1.03–27.75倍。此外，在北京四环实际路网中，APFD推荐最短路径的能力优于AC、A*、APF、RRT、NSGA-II和PSO，且APFD的执行效率高于Dijkstra方法。

关键词：大数据分析；区域提取；人工势场；Dijkstra；路线推荐；出租车GPS轨迹

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A study on specialist or special disease clinics based on big data

null

《医学前沿（英文）》 2014年第8卷第3期页码 376-381 doi: 10.1007/s11684-014-0356-9

摘要：

Correlation analysis and processing of massive medical information can be implemented through big data technology to find the relevance of different factors in the life cycle of a disease and to provide the basis for scientific research and clinical practice. This paper explores the concept of constructing a big medical data platform and introduces the clinical model construction. Medical data can be collected and consolidated by distributed computing technology. Through analysis technology, such as artificial neural network and grey model, a medical model can be built. Big data analysis, such as Hadoop, can be used to construct early prediction and intervention models as well as clinical decision-making model for specialist and special disease clinics. It establishes a new model for common clinical research for specialist and special disease clinics.

关键词： big data correlation analysis medical information integration data analysis clinical model

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Study on Big Data-based Behavior Modification in Metro Construction

Lie-yun Ding,Sheng-yu Guo

《工程管理前沿（英文）》 2015年第2卷第2期页码 131-136 doi: 10.15302/J-FEM-2015037

摘要： With the rapid development of metro construction in China, construction accidents frequently happen, which are significantly attributable to workers’ unsafe behavior. Behavior-based safety (BBS) is an effective method to modify workers’ unsafe behavior. This paper introduces the study on big data-based metro construction behavior modification, aiming to solve the problem of current research without consideration of workers’ personal characters. First, the behavior modification pushing mechanism based on content-based personalized recommendation is studied. Secondly, the development of behavior modification system of metro construction (BMSMC) is introduced. Thirdly, BMSMC practical applications using the unsafe behavior rate, as a measuring indicator is implemented. Observations at one metro construction site in Wuhan indicate that the unsafe behavior rate of modified scaffolders at this work place decreased by 69.3%. At the same time, as of unmodified scaffolders at another work place for comparison, the unsafe behavior rate decreased by 56.9%, which validates the effectiveness of this system.

关键词： big data unsafe behavior behavior modification behavior-based safety (BBS) unsafe behavior rate