A Scalable Approach for Fraud Detection in Online E-Commerce Transactions with Big Data Analytics

With the rapid development of mobile Internet and finance technology, online e-commerce transactions have been increasing and expanding very fast, which globally brings a lot of convenience and availability to our life, but meanwhile, chances of committing frauds also come in all shapes and sizes. Moreover, fraud detection in online e-commerce transactions is not totally the same to that in the existing areas due to the massive amounts of data generated in e-commerce, which makes the fraudulent transactions more covertly scattered with genuine transactions than before. In this article, a novel scalable and comprehensive approach for fraud detection in online e-commerce transactions is proposed with majorly four logical modules, which uses big data analytics and machine learning algorithms to parallelize the processing of the data from a Chinese e-commerce company. Groups of experimental results show that the approach is more accurate and efficient to detect frauds in online e-commerce transactions and scalable for big data processing to obtain real-time property.     

A hierarchical model for characterising spatial wafer variations

Silicon wafers are commonly used materials in the semiconductor manufacturing industry. Their geometric quality directly affects the production cost and yield. Therefore, improvement in the quality of wafers is critical for meeting the current competitive market needs. Conventional summary metrics such as total thickness variation, bow and warp can neither fully reflect the local variability within each wafer nor provide useful insight for root cause diagnosis and quality improvement. The advancement of sensing technology enables two-dimensional (2D) data mapping to characterise the geometric shapes of wafers, which provides more information than summary metrics. The objective of this research is to develop a statistical model to characterise the thickness variation of wafers based on 2D data maps. Specifically, the thickness variation of wafers is decomposed into macro-scale and micro-scale variations, which are modelled as a cubic curve and a first-order intrinsic Gaussian Markov random field, respectively. The models can successfully capture both the macro-scale mean trend and the micro-scale local variation, with important engineering implications for process monitoring, fault diagnosis and run-to-run control. A practical case study from a wafer manufacturing process is performed to show the effectiveness of the proposed methodology.     

基于大数据的产品色彩设计

目的 采用大数据技术,研究大数据时代下产品色彩的设计方法,探究大数据与产品色彩设计结合的价值,为设计提供新的思路和方向,从而创新性地提升产品色彩设计的效率。方法 从大数据的角度出发,通过数据采集技术从互联网上得到大量的原始数据,批量地对数据进行降噪处理,得到有效数据,在将有效数据可视化显示后,深入地对图表数据进行分析,从中挖掘出产品色彩设计的相关规律,从而形成一套行之有效的产品色彩设计方法,并结合大数据技术的应用和发展趋势,探讨两者结合的重要意义。结论 大数据在数据挖掘和趋势预测上具有极大的优势,将其与产品色彩设计相结合,为设计师提供一种在产品色彩设计中解决问题的新思路,可以优化产品色彩的设计流程,协助设计师更好、更快地设计出满足目标消费群体需求的产品。     

Process monitoring in integrated circuit fabrication using both yield and spatial statistics

This paper presents the charting of spatial statistics in addition to yield information from the integrated circuits fabrication processes to detect systematic patterns. Early detection of process anomalies is critical for the manufacturing of integrated circuits because of its long cycle time. Charting spatial statistics provides opportunities to detect special causes that go undetected using only yield statistics. Examples from IC manufacturing processes are used to demonstrate this method.     

大数据：开启面向未来的标准化事业

在当今的信息时代,云技术、物联网和基于二者的大数据技术正推动经济社会发生着变革。未来经济在互联网等技术的作用下变得越来越个性化,对大数据技术的应用将有利于标准化事业对经济社会发展做出更大贡献,标准化的服务内容由经济主体自由选择,标准化机构和研究人员更多地关注经济主体的个性化培养,标准化机构由被动服务逐渐转变为主动服务。在逐步到来的大数据时代,网络标准化服务与实体标准化服务将逐渐分离,更多的交往互动、个性化服务和灵活的服务方式将使标准化事业获得新的生机。     

大数据与智能化环境下的可持续设计趋势研究

目的科技对设计的影响由来已久,尤其是大数据、智能化的运用使新技术、新工艺、新材料不断涌现,赋予了设计领域全新的面貌。可持续设计旨在维持社会经济、生态环境、传统文化等方面的持续性满足,大数据与智能化如何引导可持续设计是需要深入思考的课题。研究大数据与智能化背景下,作为系统的可持续设计框架。方法分析大数据和智能化的相关性和本质,基于此阐述大数据和智能化在设计媒材、设计方法和设计应用方面所体现的特征,进一步提出可持续设计在环境、文化、经济、社会方面的发展趋势。结论大数据与智能化作为新时期重要的设计资源和设计工具,将对可持续设计研究提供新的研究思路,并引导可持续设计的新模式,是未来可持续设计的大势所趋。     

Development and Application of Big Data Platform for Garlic Industry Chain

In order to effectively solve the problems which affect the stable and healthy development of garlic industry, such as the uncertainty of the planting scale and production data, the influence factors of price fluctuation is difficult to be accurately analyzed, the difficult to predict the trend of price change, the uncertainty of the market concentration, and the difficulty of the short-term price prediction etc. the big data platform of the garlic industry chain has been developed. Combined with a variety of data acquisition technology, the information collection of influencing factors for garlic industry chain is realized. Based on the construction of the big data technology platform, the real-time synchronous acquisition, efficient storage and analysis of the planting, market, storage, processing, export and logistics information in five provinces and seven counties are realized. The application of the big data platform for garlic industry chain has realized the accurate acquisition of garlic planting area, the price and trend of market circulation and the information of export information, analyzed the fluctuation regulation of garlic price, and also realized the short-term precision prediction of garlic price.     

火电厂智能化远程管理云平台系统设计

为优化火力发电的生产,解决传统服务器在存储和挖掘大数据能力上的不足,该文采用云存储和云计算技术,将爆发式增长的火电机组生产数据存储在云端,通过对云端数据库的访问,搭建在线的火电厂远程管理平台,对生产数据进行远程监督和规范化存储。智能化云平台系统通过果蝇算法优化的广义回归神经网络(FOA-GRNN),设计一种锅炉热效率实时软测量模型。通过实验验证,云平台相比于传统服务器,在保证预测精度的前提下,有着更加高效的数据处理能力。     

A hybrid approach for identification of root causes and reliability improvement of a die bonding process—a case study

This paper presents an industrial case study on reliability improvement of the die bonding machine in the semiconductor industry. A hybrid approach combining dynamic analysis, process decomposition, and a structured fault tree was used to analyze the die bonding process. Firstly, the process was analyzed technically and decomposed into several stages according to different motions. Then, the die movement and force balance at each stage were analyzed according to physical laws, to identify the root causes of die rotation. A structured fault tree was then constructed to trace all possible causes and effects. A qualitative approach was used to identify critical events (root causes) for further analysis. Experiments were conducted to modify the bonding process to reduce the effects of the critical events. Finally, further process modification was proposed for simplification of the fault tree. This case study combined the knowledge in control and reliability engineering and presented a hybrid approach, which is very useful for practising engineers.     

Modeling and analysis of soft-test/repair for CCD-based digital X-ray systems

Modern X-ray imaging systems evolve toward digitization for reduced cost, faster time-to-diagnosis, and improved diagnostic confidence. For the digital X-ray systems, charge coupled device (CCD) technology is commonly used to detect and digitize optical X-ray image. This paper presents a novel soft-test/repair approach to overcome the defective pixel problem in CCD-based digital X-ray systems through theoretical modeling and analysis of the test/repair process. There are two possible solutions to cope with the defective pixel problem in CCDs: one is the hard-repair approach and another is the proposed soft-test/repair approach. Hard-repair approach employs a high-yield, expensive reparable CCD to minimize the impact of hard defects on the CCD, which occur in the form of noise propagated through A/D converter to the frame memory. Therefore, less work is needed to filter and correct the image at the end-user level while it maybe exceedingly expensive to practice. On the other hand, the proposed soft-test/repair approach is to detect and tolerate defective pixels at the digitized image level; thereby, it is inexpensive to practice and on-line repair can be done for noninterrupted service. It tests the images to detect the detective pixels and filter noise at the frame memory level and caches them in a flash memory in the controller for future repair. The controller cache keeps accumulating all the noise coordinates and preprocesses the incoming image data from the A/D converter by repairing them. The proposed soft-test/repair approach is particularly devised to facilitate hardware level implementation ultimately for real-time telediagnosis. Parametric simulation results demonstrate the speed and virtual yield enhancement by using the proposed approach; thereby highly reliable, yet inexpensive, soft-test/repair of CCD-based digital X-ray systems can be ultimately realized.     

大数据标准化现状与发展思路研究:产业发展视角

随着信息技术和经济社会的融合发展,数据已成为国家基础性战略资源,大数据对推动创新创业、转型升级,提升国家治理能力的作用日益显著.本文结合大数据研究现状,分析了国内外各标准组织开展的大数据标准化工作的情况和进展,在对比分析基础上剖析了我国大数据标准化研究工作所存在的主要问题.结合上述问题从大数据产业发展的角度,提出了未来我国大数据标准化发展的思路和建议.     

Modelling and analysis of big data platform group adoption behaviour based on social network analysis

Due to the importance of big data technology in decision-making, production and service provision, enterprises have adopted various big data technologies and platforms to improve their operational efficiency. However, the number of enterprises that have adopted big data is not promising. The purpose of this study is to explore the current status of big data adoption by Chinese enterprises and to reveal the possible factors that hinder big data adoption from the group behaviour network perspective. Based on a real case survey of 54 big data platforms (BDPs), four types of networks—i.e., the enterprise-platform network, enterprise network, platform network and industry similarity and difference (ISD) network—are constructed and analysed on the basis of social network analysis (SNA). This study finds that among Chinese enterprises, the level and scope of big data adoption are generally low and are imbalanced among industries; the cognitive level and adoption behaviour of enterprises on BDPs are inconsistent, the compatibility of BDPs is different, and the density and distance-based cohesion of networks are weak; although the current big data adoption behaviours of Chinese enterprises have formed some structural features, core-periphery structures and maximal complete cliques are found, and the current network structure has little impact on individual enterprises and platforms; enterprises in the same industry prefer to adopt the same kind of big data technology or platform. Based on these findings, several strategies and suggestions to improve big data adoption are provided.     

新型汽车车箱涂装线的工艺设计及应用

详细介绍了新型汽车车箱涂装线的工艺设计、设备选型及生产应用.新型涂装线在设计上考虑了大车型、高产量、多品种的通性及人们对涂装产品越来越高的质量要求,运用了先进的涂装技术,尤其在涂装设备及材料的选型方面,更是体现了高自动化、易管理维护的现代化生产特点,从而在生产应用中实现了高质量、低能耗、低污染的运行模式,展现了新型汽车车箱涂装线的独特风格.     

A Distributed Approach of Big Data Mining for Financial Fraud Detection in a Supply Chain

Supply Chain Finance (SCF) is important for improving the effectiveness of supply chain capital operations and reducing the overall management cost of a supply chain. In recent years, with the deep integration of supply chain and Internet, Big Data, Artificial Intelligence, Internet of Things, Blockchain, etc., the efficiency of supply chain financial services can be greatly promoted through building more customized risk pricing models and conducting more rigorous investment decision-making processes. However, with the rapid development of new technologies, the SCF data has been massively increased and new financial fraud behaviors or patterns are becoming more covertly scattered among normal ones. The lack of enough capability to handle the big data volumes and mitigate the financial frauds may lead to huge losses in supply chains. In this article, a distributed approach of big data mining is proposed for financial fraud detection in a supply chain, which implements the distributed deep learning model of Convolutional Neural Network (CNN) on big data infrastructure of Apache Spark and Hadoop to speed up the processing of the large dataset in parallel and reduce the processing time significantly. By training and testing on the continually updated SCF dataset, the approach can intelligently and automatically classify the massive data samples and discover the fraudulent financing behaviors, so as to enhance the financial fraud detection with high precision and recall rates, and reduce the losses of frauds in a supply chain.     

军用电子装备全生命周期大数据标准体系研究

本文分析了军用电子装备全生命周期大数据的内涵特征,构建了军用电子装备大数据技术框架,在此基础上提出了军用电子装备大数据标准体系,阐述了该标准体系中各层级标准范围和涉及的主要技术内容。本文研究提出的标准体系,将为军工电子行业研制大数据标准打下基础。     

Design of a low-cost sensor system for the determination of thenumber of somatic cells in milk using bioluminescence analysis

Mastitis, inflammation of the mammary glands of cows, causes big financial losses in milk production each year. As these losses have been calculated to reach $50 million US in several countries, a good screening method for the rapid detection of mastitis helps minimize this problem. The determination of the somatic cell content of milk is a valuable means of detecting mastitis. Conventional methods used to estimate it include electronic cell counting and microscopy techniques. The presence of several chemical compounds in milk has also been suggested as providing the basis for new screening methods to detect mastitis. One of the most promising of these methods is that described in this paper: the determination of the number of somatic cells in milk using bioluminescence analysis     

试论结构工程中的大数据:范式、技术与实例分析

源自信息科学的大数据思想是当前科学和技术领域的热点话题,大数据技术的推广和应用已经迅速上升到了国家战略的高度,给包括结构工程在内的各个学科带来了新的历史性发展机遇。该文尝试结合大数据概念及其技术特征,着重从研究范式转变的角度,探讨大数据思维和大数据技术应用于结构工程领域所带来的冲击、机遇和挑战,在厘清一些模糊认识的同时,结合第三代结构设计理论的发展讨论了大数据技术可能的应用场景,并以建筑物活荷载研究为例进行了分析。     

Deep Learning Based Optimal Multimodal Fusion Framework for Intrusion Detection Systems for Healthcare Data

Data fusion is a multidisciplinary research area that involves different domains. It is used to attain minimum detection error probability and maximum reliability with the help of data retrieved from multiple healthcare sources. The generation of huge quantity of data from medical devices resulted in the formation of big data during which data fusion techniques become essential. Securing medical data is a crucial issue of exponentially-pacing computing world and can be achieved by Intrusion Detection Systems (IDS). In this regard, since singular-modality is not adequate to attain high detection rate, there is a need exists to merge diverse techniques using decision-based multimodal fusion process. In this view, this research article presents a new multimodal fusion-based IDS to secure the healthcare data using Spark. The proposed model involves decision-based fusion model which has different processes such as initialization, pre-processing, Feature Selection (FS) and multimodal classification for effective detection of intrusions. In FS process, a chaotic Butterfly Optimization (BO) algorithm called CBOA is introduced. Though the classic BO algorithm offers effective exploration, it fails in achieving faster convergence. In order to overcome this, i.e., to improve the convergence rate, this research work modifies the required parameters of BO algorithm using chaos theory. Finally, to detect intrusions, multimodal classifier is applied by incorporating three Deep Learning (DL)-based classification models. Besides, the concepts like Hadoop MapReduce and Spark were also utilized in this study to achieve faster computation of big data in parallel computation platform. To validate the outcome of the presented model, a series of experimentations was performed using the benchmark NSLKDDCup99 Dataset repository. The proposed model demonstrated its effective results on the applied dataset by offering the maximum accuracy of 99.21%, precision of 98.93% and detection rate of 99.59%. The results assured the betterment of the proposed model.     

基于数据挖掘的制造过程合格率管理体系研究

在半导体自动化制造过程中产生大量的数据,为应用数据挖掘技术进行有效的过程监控提供了条件。提出在运用数据挖掘技术中聚类分析方法的基础上,对不合格批产品的加工路径进行分析,从而准确地将控制对象定位于某些问题设备,并结合统计过程控制的手段加强监控。     

SMK-means: An Improved Mini Batch <i>K</i>-means Algorithm Based on Mapreduce with Big Data

In recent years, the rapid development of big data technology has also been favored by more and more scholars. Massive data storage and calculation problems have also been solved. At the same time, outlier detection problems in mass data have also come along with it. Therefore, more research work has been devoted to the problem of outlier detection in big data. However, the existing available methods have high computation time, the improved algorithm of outlier detection is presented, which has higher performance to detect outlier. In this paper, an improved algorithm is proposed. The SMK-means is a fusion algorithm which is achieved by Mini Batch K-means based on simulated annealing algorithm for anomalous detection of massive household electricity data, which can give the number of clusters and reduce the number of iterations and improve the accuracy of clustering. In this paper, several experiments are performed to compare and analyze multiple performances of the algorithm. Through analysis, we know that the proposed algorithm is superior to the existing algorithms