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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

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《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

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《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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Urban spatial cluster structure in metro travel networks: An explorative study of Wuhan using big andopen data

《工程管理前沿（英文）》 2024年第11卷第2期页码 231-246 doi: 10.1007/s42524-024-0296-2

摘要： Rail transit plays a crucial role in improving urban sustainability and livability. In many Chinese cities, the planning of rail transit routes and stations is focused on facilitating new developments rather than revitalizing existing built-up areas. This approach reflects the local governments’ expectations of substantial growth to reshape the urban structure. However, existing research on transit-oriented development (TOD) rarely explores the spatial interactions between individual transit stations and investigates how they can be integrated to achieve synergistic effects and balanced development. This study proposes that rail transit systems impact urban structure through two “forces”: the provision of additional and reliable carrying capacity and the reduction of travel time between locations. Metro passenger flow is used as a proxy for these forces, and community detection techniques are employed to identify the actual and optimal spatial clusters in Wuhan, China. The results reveal that the planned sub-centers align reasonably well with the optimal spatial clusters in terms of spatial configuration. However, the actual spatial clusters tend to have longer internal travel times compared to the optimal clusters. Further exploration suggests the need for equalizing land use density within planned spatial clusters served by the metro system. Additionally, promoting concentrated, differentiated, and mixed functional arrangements in metro station areas with low passenger flows within the planned clusters could be beneficial. This paper presents a new framework for investigating urban spatial clusters influenced by a metro system.

关键词： urban spatial clusters metro travel flows land use metro smartcard data Wuhan

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

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《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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Slope stability analysis based on big data and convolutional neural network

Yangpan FU; Mansheng LIN; You ZHANG; Gongfa CHEN; Yongjian LIU

《结构与土木工程前沿（英文）》 2022年第16卷第7期页码 882-895 doi: 10.1007/s11709-022-0859-4

摘要： The Limit Equilibrium Method (LEM) is commonly used in traditional slope stability analyses, but it is time-consuming and complicated. Due to its complexity and nonlinearity involved in the evaluation process, it cannot provide a quick stability estimation when facing a large number of slopes. In this case, the convolutional neural network (CNN) provides a better alternative. A CNN model can process data quickly and complete a large amount of data analysis in a specific situation, while it needs a large number of training samples. It is difficult to get enough slope data samples in practical engineering. This study proposes a slope database generation method based on the LEM. Samples were amplified from 40 typical slopes, and a sample database consisting of 20000 slope samples was established. The sample database for slopes covered a wide range of slope geometries and soil layers’ physical and mechanical properties. The CNN trained with this sample database was then applied to the stability prediction of 15 real slopes to test the accuracy of the CNN model. The results show that the slope stability prediction method based on the CNN does not need complex calculation but only needs to provide the slope coordinate information and physical and mechanical parameters of the soil layers, and it can quickly obtain the safety factor and stability state of the slopes. Moreover, the prediction accuracy of the CNN trained by the sample database for slope stability analysis reaches more than 99%, and the comparisons with the BP neural network show that the CNN has significant superiority in slope stability evaluation. Therefore, the CNN can predict the safety factor of real slopes. In particular, the combination of typical actual slopes and generated slope data provides enough training and testing samples for the CNN, which improves the prediction speed and practicability of the CNN-based evaluation method in engineering practice.

关键词： slope stability limit equilibrium method convolutional neural network database for slopes big data

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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

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Utilizing big data to build personalized technology and system of diagnosis and treatment in traditional

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《医学前沿（英文）》 2014年第8卷第3期页码 272-278 doi: 10.1007/s11684-014-0364-9

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Standard Framework Construction of Technology and Equipment for Big Data in Crop Phenomics

Weiliang Wen,Shenghao Gu,Ying Zhang,Wanneng Yang,Xinyu Guo,

《工程（英文）》 doi: 10.1016/j.eng.2024.06.001

摘要： Crop phenomics has rapidly progressed in recent years due to the growing need for crop functional genomics, digital breeding, and smart cultivation. Despite this advancement, the lack of standards for the creation and usage of crop phenomics technology and equipment has become a bottleneck, limiting the industry’s high-quality development. This paper begins with an overview of the crop phenotyping industry and presents an industrial mapping of technology and equipment for big data in crop phenomics. It analyzes the necessity and current state of constructing a standard framework for crop phenotyping. Furthermore, this paper proposes the intended organizational structure and goals of the standard framework. It details the essentials of the standard framework in the research and development of hardware and equipment, data acquisition, and the storage and management of crop phenotyping data. Finally, it discusses promoting the construction and evaluation of the standard framework, aiming to provide ideas for developing a high-quality standard framework for crop phenotyping.

关键词： Crop phenomics Big data Phenotyping technology and equipment Standard framework Industrial mapping

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Big data and machine learning: A roadmap towards smart plants

《工程管理前沿（英文）》 2022年第9卷第4期页码 623-639 doi: 10.1007/s42524-022-0218-0

摘要： Industry 4.0 aims to transform chemical and biochemical processes into intelligent systems via the integration of digital components with the actual physical units involved. This process can be thought of as addition of a central nervous system with a sensing and control monitoring of components and regulating the performance of the individual physical assets (processes, units, etc.) involved. Established technologies central to the digital integrating components are smart sensing, mobile communication, Internet of Things, modelling and simulation, advanced data processing, storage and analysis, advanced process control, artificial intelligence and machine learning, cloud computing, and virtual and augmented reality. An essential element to this transformation is the exploitation of large amounts of historical process data and large volumes of data generated in real-time by smart sensors widely used in industry. Exploitation of the information contained in these data requires the use of advanced machine learning and artificial intelligence technologies integrated with more traditional modelling techniques. The purpose of this paper is twofold: a) to present the state-of-the-art of the aforementioned technologies, and b) to present a strategic plan for their integration toward the goal of an autonomous smart plant capable of self-adaption and self-regulation for short- and long-term production management.

关键词： big data machine learning artificial intelligence smart sensor cyber–physical system Industry 4.0 intelligent system digitalization

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Big Data to support sustainable urban energy planning: The EvoEnergy project

Moulay Larbi CHALAL, Benachir MEDJDOUB, Nacer BEZAI, Raid SHRAHILY

《工程管理前沿（英文）》 2020年第7卷第2期页码 287-300 doi: 10.1007/s42524-019-0081-9

摘要： Energy sustainability is a complex problem that needs to be tackled holistically by equally addressing other aspects such as socio-economic to meet the strict CO emission targets. This paper builds upon our previous work on the effect of household transition on residential energy consumption where we developed a 3D urban energy prediction system (EvoEnergy) using the old UK panel data survey, namely, the British household panel data survey (BHPS). In particular, the aim of the present study is to examine the validity and reliability of EvoEnergy under the new UK household longitudinal study (UKHLS) launched in 2009. To achieve this aim, the household transition and energy prediction modules of EvoEnergy have been tested under both data sets using various statistical techniques such as Chow test. The analysis of the results advised that EvoEnergy remains a reliable prediction system and had a good prediction accuracy (MAPE  5%) when compared to actual energy performance certificate data. From this premise, we recommend researchers, who are working on data-driven energy consumption forecasting, to consider merging the BHPS and UKHLS data sets. This will, in turn, enable them to capture the bigger picture of different energy phenomena such as fuel poverty; consequently, anticipate problems with policy prior to their occurrence. Finally, the paper concludes by discussing two scenarios of EvoEnergy development in relation to energy policy and decision-making.

关键词： urban energy planning sustainable planning Big Data household transition energy prediction

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IN2CLOUD: A novel concept for collaborative management of big railway data

Jing LIN, Uday KUMAR

《工程管理前沿（英文）》 2017年第4卷第4期页码 428-436 doi: 10.15302/J-FEM-2017048

摘要： In the EU Horizon 2020 Shift2Rail Multi-Annual Action Plan, the challenge of railway maintenance is generating knowledge from data and/or information. Therefore, we promote a novel concept called “IN2CLOUD,” which comprises three sub-concepts, to address this challenge: 1) A hybrid cloud, 2) an intelligent cloud with hybrid cloud learning, and 3) collaborative management using asset-related data acquired from the intelligent hybrid cloud. The concept is developed under the assumption that organizations want/need to learn from each other (including domain knowledge and experience) but do not want to share their raw data or information. IN2CLOUD will help the movement of railway industry systems from “local” to “global” optimization in a collaborative way. The development of cutting-edge intelligent hybrid cloud-based solutions, including information technology (IT) solutions and related methodologies, will enhance business security, economic sustainability, and decision support in the field of intelligent asset management of railway assets.

关键词： railway intelligent asset management collaborative learning big data hybrid cloud Bayesian